Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

PubMed

Liu, Mengjin; Bienfait, Bruno; Sacher, Oliver; Gasteiger, Johann; Siezen, Roland J; Nauta, Arjen; Geurts, Jan M W

2014-01-01

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the "missing links" between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

PubMed Central

Koumakis, Lefteris; Kartsaki, Evgenia; Chatzimina, Maria; Zervakis, Michalis; Vassou, Despoina; Marias, Kostas; Moustakis, Vassilis; Potamias, George

2016-01-01

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes. PMID:27832067

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.

PubMed

Koumakis, Lefteris; Kanterakis, Alexandros; Kartsaki, Evgenia; Chatzimina, Maria; Zervakis, Michalis; Tsiknakis, Manolis; Vassou, Despoina; Kafetzopoulos, Dimitris; Marias, Kostas; Moustakis, Vassilis; Potamias, George

2016-11-01

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Systems Biology and Birth Defects Prevention: Blockade of the Glucocorticoid Receptor Prevents Arsenic-Induced Birth Defects

PubMed Central

Ahir, Bhavesh K.; Sanders, Alison P.; Rager, Julia E.

2013-01-01

Background: The biological mechanisms by which environmental metals are associated with birth defects are largely unknown. Systems biology–based approaches may help to identify key pathways that mediate metal-induced birth defects as well as potential targets for prevention. Objectives: First, we applied a novel computational approach to identify a prioritized biological pathway that associates metals with birth defects. Second, in a laboratory setting, we sought to determine whether inhibition of the identified pathway prevents developmental defects. Methods: Seven environmental metals were selected for inclusion in the computational analysis: arsenic, cadmium, chromium, lead, mercury, nickel, and selenium. We used an in silico strategy to predict genes and pathways associated with both metal exposure and developmental defects. The most significant pathway was identified and tested using an in ovo whole chick embryo culture assay. We further evaluated the role of the pathway as a mediator of metal-induced toxicity using the in vitro midbrain micromass culture assay. Results: The glucocorticoid receptor pathway was computationally predicted to be a key mediator of multiple metal-induced birth defects. In the chick embryo model, structural malformations induced by inorganic arsenic (iAs) were prevented when signaling of the glucocorticoid receptor pathway was inhibited. Further, glucocorticoid receptor inhibition demonstrated partial to complete protection from both iAs- and cadmium-induced neurodevelopmental toxicity in vitro. Conclusions: Our findings highlight a novel approach to computationally identify a targeted biological pathway for examining birth defects prevention. PMID:23458687

Radiogenomics: a systems biology approach to understanding genetic risk factors for radiotherapy toxicity ?

PubMed Central

Herskind, Carsten; Talbot, Christopher J.; Kerns, Sarah L.; Veldwijk, Marlon R.; Rosenstein, Barry S.; West, Catharine M. L.

2016-01-01

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review ‘omics’ approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different ‘omics’ approaches may be more efficient in identifying critical pathways than pathway analysis based on single ‘omics’ data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterized by different mechanisms. Thus ‘omics’ and functional approaches may synergize if they are integrated into radiogenomics ‘systems biology’ to facilitate the goal of individualised radiotherapy. PMID:26944314

Concordance of Changes in Metabolic Pathways Based on Plasma Metabolomics and Skeletal Muscle Transcriptomics in Type 1 Diabetes

PubMed Central

Dutta, Tumpa; Chai, High Seng; Ward, Lawrence E.; Ghosh, Aditya; Persson, Xuan-Mai T.; Ford, G. Charles; Kudva, Yogish C.; Sun, Zhifu; Asmann, Yan W.; Kocher, Jean-Pierre A.; Nair, K. Sreekumaran

2012-01-01

Insulin regulates many cellular processes, but the full impact of insulin deficiency on cellular functions remains to be defined. Applying a mass spectrometry–based nontargeted metabolomics approach, we report here alterations of 330 plasma metabolites representing 33 metabolic pathways during an 8-h insulin deprivation in type 1 diabetic individuals. These pathways included those known to be affected by insulin such as glucose, amino acid and lipid metabolism, Krebs cycle, and immune responses and those hitherto unknown to be altered including prostaglandin, arachidonic acid, leukotrienes, neurotransmitters, nucleotides, and anti-inflammatory responses. A significant concordance of metabolome and skeletal muscle transcriptome–based pathways supports an assumption that plasma metabolites are chemical fingerprints of cellular events. Although insulin treatment normalized plasma glucose and many other metabolites, there were 71 metabolites and 24 pathways that differed between nondiabetes and insulin-treated type 1 diabetes. Confirmation of many known pathways altered by insulin using a single blood test offers confidence in the current approach. Future research needs to be focused on newly discovered pathways affected by insulin deficiency and systemic insulin treatment to determine whether they contribute to the high morbidity and mortality in T1D despite insulin treatment. PMID:22415876

Systems Pharmacology Dissection of the Anti-Inflammatory Mechanism for the Medicinal Herb Folium Eriobotryae

PubMed Central

Zhang, Jingxiao; Li, Yan; Chen, Su-Shing; Zhang, Lilei; Wang, Jinghui; Yang, Yinfeng; Zhang, Shuwei; Pan, Yanqiu; Wang, Yonghua; Yang, Ling

2015-01-01

Inflammation is a hallmark of many diseases like diabetes, cancers, atherosclerosis and arthritis. Thus, lots of concerns have been raised toward developing novel anti-inflammatory agents. Many alternative herbal medicines possess excellent anti-inflammatory properties, yet their precise mechanisms of action are yet to be elucidated. Here, a novel systems pharmacology approach based on a large number of chemical, biological and pharmacological data was developed and exemplified by a probe herb Folium Eriobotryae, a widely used clinical anti-inflammatory botanic drug. The results show that 11 ingredients of this herb with favorable pharmacokinetic properties are predicted as active compounds for anti-inflammatory treatment. In addition, via systematic network analyses, their targets are identified to be 43 inflammation-associated proteins including especially COX2, ALOX5, PPARG, TNF and RELA that are mainly involved in the mitogen-activated protein kinase (MAPK) signaling pathway, the rheumatoid arthritis pathway and NF-κB signaling pathway. All these demonstrate that the integrated systems pharmacology method provides not only an effective tool to illustrate the anti-inflammatory mechanisms of herbs, but also a new systems-based approach for drug discovery from, but not limited to, herbs, especially when combined with further experimental validations. PMID:25636035

Impact of Care Pathway-Based Approach on Outcomes in a Specialist Intellectual Disability Inpatient Unit

ERIC Educational Resources Information Center

Devapriam, John; Alexander, Regi; Gumber, Rohit; Pither, Judith; Gangadharan, Satheesh

2014-01-01

Specialist intellectual disability inpatient units have come under increased scrutiny, leading to questions about the quality of service provision in this sector. A care pathway-based approach was implemented in such a unit and its impact on outcome variables was measured. The care pathway-based approach resulted in the turnover of more patients,…

Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las.

PubMed

Aghdasi, Nava; Whipple, Mark; Humphreys, Ian M; Moe, Kris S; Hannaford, Blake; Bly, Randall A

2018-06-01

Successful multidisciplinary treatment of skull base pathology requires precise preoperative planning. Current surgical approach (pathway) selection for these complex procedures depends on an individual surgeon's experiences and background training. Because of anatomical variation in both normal tissue and pathology (eg, tumor), a successful surgical pathway used on one patient is not necessarily the best approach on another patient. The question is how to define and obtain optimized patient-specific surgical approach pathways? In this article, we demonstrate that the surgeon's knowledge and decision making in preoperative planning can be modeled by a multiobjective cost function in a retrospective analysis of actual complex skull base cases. Two different approaches- weighted-sum approach and Pareto optimality-were used with a defined cost function to derive optimized surgical pathways based on preoperative computed tomography (CT) scans and manually designated pathology. With the first method, surgeon's preferences were input as a set of weights for each objective before the search. In the second approach, the surgeon's preferences were used to select a surgical pathway from the computed Pareto optimal set. Using preoperative CT and magnetic resonance imaging, the patient-specific surgical pathways derived by these methods were similar (85% agreement) to the actual approaches performed on patients. In one case where the actual surgical approach was different, revision surgery was required and was performed utilizing the computationally derived approach pathway.

Strategies for engineering plant natural products: the iridoid-derived monoterpene indole alkaloids of Catharanthus roseus.

PubMed

O'Connor, Sarah E

2012-01-01

The manipulation of pathways to make unnatural variants of natural compounds, a process often termed combinatorial biosynthesis, has been robustly successful in prokaryotic systems. The development of approaches to generate new-to-nature compounds from plant-based pathways is, in comparison, much less advanced. Success will depend on the specific chemistry of the pathway, as well as on the suitability of the plant system for transformation and genetic manipulation. As plant pathways are elucidated, and can be heterologously expressed in hosts that are more amenable to genetic manipulation, biosynthetic production of new-to-nature compounds from plant pathways will become more widespread. In this chapter, some of the key strategies that have been developed for metabolic engineering of plant pathways, namely directed biosynthesis, mutasynthesis, and pathway incorporation of engineered enzymes are highlighted. The iridoid-derived monoterpene indole alkaloids from C. roseus, which are the focus of this chapter, provide an excellent system for developing these strategies. Copyright © 2012 Elsevier Inc. All rights reserved.

A statistical framework for biomedical literature mining.

PubMed

Chung, Dongjun; Lawson, Andrew; Zheng, W Jim

2017-09-30

In systems biology, it is of great interest to identify new genes that were not previously reported to be associated with biological pathways related to various functions and diseases. Identification of these new pathway-modulating genes does not only promote understanding of pathway regulation mechanisms but also allow identification of novel targets for therapeutics. Recently, biomedical literature has been considered as a valuable resource to investigate pathway-modulating genes. While the majority of currently available approaches are based on the co-occurrence of genes within an abstract, it has been reported that these approaches show only sub-optimal performances because 70% of abstracts contain information only for a single gene. To overcome such limitation, we propose a novel statistical framework based on the concept of ontology fingerprint that uses gene ontology to extract information from large biomedical literature data. The proposed framework simultaneously identifies pathway-modulating genes and facilitates interpreting functions of these new genes. We also propose a computationally efficient posterior inference procedure based on Metropolis-Hastings within Gibbs sampler for parameter updates and the poor man's reversible jump Markov chain Monte Carlo approach for model selection. We evaluate the proposed statistical framework with simulation studies, experimental validation, and an application to studies of pathway-modulating genes in yeast. The R implementation of the proposed model is currently available at https://dongjunchung.github.io/bayesGO/. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Simulating Chemical Kinetics Without Differential Equations: A Quantitative Theory Based on Chemical Pathways.

PubMed

Bai, Shirong; Skodje, Rex T

2017-08-17

A new approach is presented for simulating the time-evolution of chemically reactive systems. This method provides an alternative to conventional modeling of mass-action kinetics that involves solving differential equations for the species concentrations. The method presented here avoids the need to solve the rate equations by switching to a representation based on chemical pathways. In the Sum Over Histories Representation (or SOHR) method, any time-dependent kinetic observable, such as concentration, is written as a linear combination of probabilities for chemical pathways leading to a desired outcome. In this work, an iterative method is introduced that allows the time-dependent pathway probabilities to be generated from a knowledge of the elementary rate coefficients, thus avoiding the pitfalls involved in solving the differential equations of kinetics. The method is successfully applied to the model Lotka-Volterra system and to a realistic H 2 combustion model.

Systems Proteomics for Translational Network Medicine

PubMed Central

Arrell, D. Kent; Terzic, Andre

2012-01-01

Universal principles underlying network science, and their ever-increasing applications in biomedicine, underscore the unprecedented capacity of systems biology based strategies to synthesize and resolve massive high throughput generated datasets. Enabling previously unattainable comprehension of biological complexity, systems approaches have accelerated progress in elucidating disease prediction, progression, and outcome. Applied to the spectrum of states spanning health and disease, network proteomics establishes a collation, integration, and prioritization algorithm to guide mapping and decoding of proteome landscapes from large-scale raw data. Providing unparalleled deconvolution of protein lists into global interactomes, integrative systems proteomics enables objective, multi-modal interpretation at molecular, pathway, and network scales, merging individual molecular components, their plurality of interactions, and functional contributions for systems comprehension. As such, network systems approaches are increasingly exploited for objective interpretation of cardiovascular proteomics studies. Here, we highlight network systems proteomic analysis pipelines for integration and biological interpretation through protein cartography, ontological categorization, pathway and functional enrichment and complex network analysis. PMID:22896016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ji, Yanzhou; Heo, Tae Wook; Zhang, Fan

Here we present our theoretical assessment of the kinetic pathways during phase transformations of multi-component Ti alloys. Employing the graphical thermodynamic approach and an integrated free energy function based on the realistic thermodynamic database and assuming that a displacive structural transformation occurs much faster than long-range diffusional processes, we analyze the phase stabilities of Ti-6Al -4V (Ti-6wt.%Al -4wt.%V). Our systematic analyses predict a variety of possible kinetic pathways for β to (α + β) transformations leading to different types of microstructures under various heat treatment conditions. In addition, the possibility of unconventional kinetic pathways is discussed. Lastly, we also brieflymore » discuss the application of our approach to general multicomponent/multiphase alloy systems.« less

Understanding and Measuring LGBTQ Pathways to Health: A Scoping Review of Strengths-Based Health Promotion Approaches in LGBTQ Health Research.

PubMed

Gahagan, Jacqueline; Colpitts, Emily

2017-01-01

Health research traditionally has focused on the health risks and deficits of lesbian, gay, bisexual, transgender, and queer (LGBTQ) populations, obscuring the determinants that can promote health across the life course. Recognizing, appropriately measuring, and rendering visible these determinants of health is paramount to informing appropriate and engaging health policies, services, and systems for LGBTQ populations. The overarching purpose of this article is to provide an overview of the findings of a scoping review aimed at exploring strengths-based health promotion approaches to understanding and measuring LGBTQ health. Specifically, this scoping review examined peer-reviewed, published academic literature to determine (a) existing methodological frameworks for studying LGBTQ health from a strengths-based health promotion approach, and (b) suggestions for future methodological approaches for studying LGBTQ health from a strengths-based health promotion approach. The findings of this scoping review will be used to inform the development of a study aimed at assessing the health of and improving pathways to health services among LGBTQ populations in Nova Scotia, Canada.

Interaction of Herbal Compounds with Biological Targets: A Case Study with Berberine

PubMed Central

Chen, Xiao-Wu; Di, Yuan Ming; Zhang, Jian; Zhou, Zhi-Wei; Li, Chun Guang; Zhou, Shu-Feng

2012-01-01

Berberine is one of the main alkaloids found in the Chinese herb Huang lian (Rhizoma Coptidis), which has been reported to have multiple pharmacological activities. This study aimed to analyze the molecular targets of berberine based on literature data followed by a pathway analysis using the PANTHER program. PANTHER analysis of berberine targets showed that the most classes of molecular functions include receptor binding, kinase activity, protein binding, transcription activity, DNA binding, and kinase regulator activity. Based on the biological process classification of in vitro berberine targets, those targets related to signal transduction, intracellular signalling cascade, cell surface receptor-linked signal transduction, cell motion, cell cycle control, immunity system process, and protein metabolic process are most frequently involved. In addition, berberine was found to interact with a mixture of biological pathways, such as Alzheimer's disease-presenilin and -secretase pathways, angiogenesis, apoptosis signalling pathway, FAS signalling pathway, Hungtington disease, inflammation mediated by chemokine and cytokine signalling pathways, interleukin signalling pathway, and p53 pathways. We also explored the possible mechanism of action for the anti-diabetic effect of berberine. Further studies are warranted to elucidate the mechanisms of action of berberine using systems biology approach. PMID:23213296

Methods and approaches in the topology-based analysis of biological pathways

PubMed Central

Mitrea, Cristina; Taghavi, Zeinab; Bokanizad, Behzad; Hanoudi, Samer; Tagett, Rebecca; Donato, Michele; Voichiţa, Călin; Drăghici, Sorin

2013-01-01

The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as “third generation,” have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity. PMID:24133454

GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes.

PubMed

Arakawa, Kazuharu; Yamada, Yohei; Shinoda, Kosaku; Nakayama, Yoichi; Tomita, Masaru

2006-03-23

Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. We developed the Genome-based Modeling (GEM) System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.

Comparative study on gene set and pathway topology-based enrichment methods.

PubMed

Bayerlová, Michaela; Jung, Klaus; Kramer, Frank; Klemm, Florian; Bleckmann, Annalen; Beißbarth, Tim

2015-10-22

Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis. We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods. In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower. We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both types of methods for enrichment analysis require further improvements in order to deal with the problem of pathway overlaps.

A methodological approach for using high-level Petri Nets to model the immune system response.

PubMed

Pennisi, Marzio; Cavalieri, Salvatore; Motta, Santo; Pappalardo, Francesco

2016-12-22

Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics, and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description; conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of "high level" PN. We propose a novel methodological approach that is based on high level PN, and in particular on Colored Petri Nets (CPN), that can be used to model the immune system response at the cellular scale. To demonstrate the potentiality of the approach we provide a simple model of the humoral immune system response that is able of reproducing some of the most complex well-known features of the adaptive response like memory and specificity features. The methodology we present has advantages of both the two classical approaches based on continuous and discrete models, since it allows to gain good level of granularity in the description of cells behavior without losing the possibility of having a qualitative analysis. Furthermore, the presented methodology based on CPN allows the adoption of the same graphical modeling technique well known to life scientists that use PN for the modeling of signaling pathways. Finally, such an approach may open the floodgates to the realization of multi scale models that integrate both signaling pathways (intra cellular) models and cellular (population) models built upon the same technique and software.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

An overview of bioinformatics methods for modeling biological pathways in yeast

PubMed Central

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao

2016-01-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein–protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae. In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways in S. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. PMID:26476430

PerSubs: A Graph-Based Algorithm for the Identification of Perturbed Subpathways Caused by Complex Diseases.

PubMed

Vrahatis, Aristidis G; Rapti, Angeliki; Sioutas, Spyros; Tsakalidis, Athanasios

2017-01-01

In the era of Systems Biology and growing flow of omics experimental data from high throughput techniques, experimentalists are in need of more precise pathway-based tools to unravel the inherent complexity of diseases and biological processes. Subpathway-based approaches are the emerging generation of pathway-based analysis elucidating the biological mechanisms under the perspective of local topologies onto a complex pathway network. Towards this orientation, we developed PerSub, a graph-based algorithm which detects subpathways perturbed by a complex disease. The perturbations are imprinted through differentially expressed and co-expressed subpathways as recorded by RNA-seq experiments. Our novel algorithm is applied on data obtained from a real experimental study and the identified subpathways provide biological evidence for the brain aging.

Transcriptional pathway and de novo network-based approaches to effects-based monitoring in the Great Lakes

EPA Science Inventory

Transcriptomics provides unique solutions for understanding the impact of complex mixtures and their components on aquatic systems. Here we describe the application of transcriptomics analysis of in situ fathead minnow exposures for assessing biological impacts of wastewater trea...

On determining firing delay time of transitions for Petri net based signaling pathways by introducing stochastic decision rules.

PubMed

Miwa, Yoshimasa; Li, Chen; Ge, Qi-Wei; Matsuno, Hiroshi; Miyano, Satoru

2010-01-01

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

On determining firing delay time of transitions for petri net based signaling pathways by introducing stochastic decision rules.

PubMed

Miwa, Yoshimasa; Li, Chen; Ge, Qi-Wei; Matsuno, Hiroshi; Miyano, Satoru

2011-01-01

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

Theoretical Assessment on the Phase Transformation Kinetic Pathways of Multi-component Ti Alloys: Application to Ti-6Al- 4V

DOE PAGES

Ji, Yanzhou; Heo, Tae Wook; Zhang, Fan; ...

2015-12-21

Here we present our theoretical assessment of the kinetic pathways during phase transformations of multi-component Ti alloys. Employing the graphical thermodynamic approach and an integrated free energy function based on the realistic thermodynamic database and assuming that a displacive structural transformation occurs much faster than long-range diffusional processes, we analyze the phase stabilities of Ti-6Al -4V (Ti-6wt.%Al -4wt.%V). Our systematic analyses predict a variety of possible kinetic pathways for β to (α + β) transformations leading to different types of microstructures under various heat treatment conditions. In addition, the possibility of unconventional kinetic pathways is discussed. Lastly, we also brieflymore » discuss the application of our approach to general multicomponent/multiphase alloy systems.« less

Representative Agricultural Pathways: A Trans-Disciplinary Approach to Agricultural Model Inter-comparison, Improvement, Climate Impact Assessment and Stakeholder Engagement

NASA Astrophysics Data System (ADS)

Antle, J. M.; Valdivia, R. O.; Claessens, L.; Nelson, G. C.; Rosenzweig, C.; Ruane, A. C.; Vervoort, J.

2013-12-01

The global change research community has recognized that new pathway and scenario concepts are needed to implement impact and vulnerability assessment that is logically consistent across local, regional and global scales. For impact and vulnerability assessment, new socio-economic pathway and scenario concepts are being developed. Representative Agricultural Pathways (RAPs) are designed to extend global pathways to provide the detail needed for global and regional assessment of agricultural systems. In addition, research by the Agricultural Model Inter-comparison and Improvement Project (AgMIP) shows that RAPs provide a powerful way to engage stakeholders in climate-related research throughout the research process and in communication of research results. RAPs are based on the integrated assessment framework developed by AgMIP. This framework shows that both bio-physical and socio-economic drivers are essential components of agricultural pathways and logically precede the definition of adaptation and mitigation scenarios that embody associated capabilities and challenges. This approach is based on a trans-disciplinary process for designing pathways and then translating them into parameter sets for bio-physical and economic models that are components of agricultural integrated assessments of climate impact, adaptation and mitigation. RAPs must be designed to be part of a logically consistent set of drivers and outcomes from global to regional and local. Global RAPs are designed to be consistent with higher-level global socio-economic pathways, but add key agricultural drivers such as agricultural growth trends that are not specified in more general pathways, as illustrated in a recent inter-comparison of global agricultural models. To create pathways at regional or local scales, further detail is needed. At this level, teams of scientists and other experts with knowledge of the agricultural systems and regions work together through a step-wise process. Experiences from AgMIP Regional Teams, and from the project on Regional Approaches to Climate Change in the Pacific Northwest, are used to discuss how the RAPs procedures can be further developed and improved, and how RAPs can help engage stakeholders in climate-related research throughout the research process and in communication of research results.

Pivotal role of the muscle-contraction pathway in cryptorchidism and evidence for genomic connections with cardiomyopathy pathways in RASopathies.

PubMed

Cannistraci, Carlo V; Ogorevc, Jernej; Zorc, Minja; Ravasi, Timothy; Dovc, Peter; Kunej, Tanja

2013-02-14

Cryptorchidism is the most frequent congenital disorder in male children; however the genetic causes of cryptorchidism remain poorly investigated. Comparative integratomics combined with systems biology approach was employed to elucidate genetic factors and molecular pathways underlying testis descent. Literature mining was performed to collect genomic loci associated with cryptorchidism in seven mammalian species. Information regarding the collected candidate genes was stored in MySQL relational database. Genomic view of the loci was presented using Flash GViewer web tool (http://gmod.org/wiki/Flashgviewer/). DAVID Bioinformatics Resources 6.7 was used for pathway enrichment analysis. Cytoscape plug-in PiNGO 1.11 was employed for protein-network-based prediction of novel candidate genes. Relevant protein-protein interactions were confirmed and visualized using the STRING database (version 9.0). The developed cryptorchidism gene atlas includes 217 candidate loci (genes, regions involved in chromosomal mutations, and copy number variations) identified at the genomic, transcriptomic, and proteomic level. Human orthologs of the collected candidate loci were presented using a genomic map viewer. The cryptorchidism gene atlas is freely available online: http://www.integratomics-time.com/cryptorchidism/. Pathway analysis suggested the presence of twelve enriched pathways associated with the list of 179 literature-derived candidate genes. Additionally, a list of 43 network-predicted novel candidate genes was significantly associated with four enriched pathways. Joint pathway analysis of the collected and predicted candidate genes revealed the pivotal importance of the muscle-contraction pathway in cryptorchidism and evidence for genomic associations with cardiomyopathy pathways in RASopathies. The developed gene atlas represents an important resource for the scientific community researching genetics of cryptorchidism. The collected data will further facilitate development of novel genetic markers and could be of interest for functional studies in animals and human. The proposed network-based systems biology approach elucidates molecular mechanisms underlying co-presence of cryptorchidism and cardiomyopathy in RASopathies. Such approach could also aid in molecular explanation of co-presence of diverse and apparently unrelated clinical manifestations in other syndromes.

A conceptual framework to support exposure science research ...

EPA Pesticide Factsheets

While knowledge of exposure is fundamental to assessing and mitigating risks, exposure information has been costly and difficult to generate. Driven by major scientific advances in analytical methods, biomonitoring, computational tools, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition that allows it to be more agile, predictive, and data- and knowledge-driven. A necessary element of this evolved paradigm is an organizational and predictive framework for exposure science that furthers the application of systems-based approaches. To enable such systems-based approaches, we proposed the Aggregate Exposure Pathway (AEP) concept to organize data and information emerging from an invigorated and expanding field of exposure science. The AEP framework is a layered structure that describes the elements of an exposure pathway, as well as the relationship between those elements. The basic building blocks of an AEP adopt the naming conventions used for Adverse Outcome Pathways (AOPs): Key Events (KEs) to describe the measurable, obligate steps through the AEP; and Key Event Relationships (KERs) describe the linkages between KEs. Importantly, the AEP offers an intuitive approach to organize exposure information from sources to internal site of action, setting the stage for predicting stressor concentrations at an internal target site. These predicted concentrations can help inform the r

Non-invasive, transdermal, path-selective and specific glucose monitoring via a graphene-based platform

NASA Astrophysics Data System (ADS)

Lipani, Luca; Dupont, Bertrand G. R.; Doungmene, Floriant; Marken, Frank; Tyrrell, Rex M.; Guy, Richard H.; Ilie, Adelina

2018-06-01

Currently, there is no available needle-free approach for diabetics to monitor glucose levels in the interstitial fluid. Here, we report a path-selective, non-invasive, transdermal glucose monitoring system based on a miniaturized pixel array platform (realized either by graphene-based thin-film technology, or screen-printing). The system samples glucose from the interstitial fluid via electroosmotic extraction through individual, privileged, follicular pathways in the skin, accessible via the pixels of the array. A proof of principle using mammalian skin ex vivo is demonstrated for specific and `quantized' glucose extraction/detection via follicular pathways, and across the hypo- to hyper-glycaemic range in humans. Furthermore, the quantification of follicular and non-follicular glucose extraction fluxes is clearly shown. In vivo continuous monitoring of interstitial fluid-borne glucose with the pixel array was able to track blood sugar in healthy human subjects. This approach paves the way to clinically relevant glucose detection in diabetics without the need for invasive, finger-stick blood sampling.

Finding off-targets, biological pathways, and target diseases for chymase inhibitors via structure-based systems biology approach.

PubMed

Arooj, Mahreen; Sakkiah, Sugunadevi; Cao, Guang Ping; Kim, Songmi; Arulalapperumal, Venkatesh; Lee, Keun Woo

2015-07-01

Off-target binding connotes the binding of a small molecule of therapeutic significance to a protein target in addition to the primary target for which it was proposed. Progressively such off-targeting is emerging to be regular practice to reveal side effects. Chymase is an enzyme of hydrolase class that catalyzes hydrolysis of peptide bonds. A link between heart failure and chymase is ascribed, and a chymase inhibitor is in clinical phase II for treatment of heart failure. However, the underlying mechanisms of the off-target effects of human chymase inhibitors are still unclear. Here, we develop a robust computational strategy that is applicable to any enzyme system and that allows the prediction of drug effects on biological processes. Putative off-targets for chymase inhibitors were identified through various structural and functional similarity analyses along with molecular docking studies. Finally, literature survey was performed to incorporate these off-targets into biological pathways and to establish links between pathways and particular adverse effects. Off-targets of chymase inhibitors are linked to various biological pathways such as classical and lectin pathways of complement system, intrinsic and extrinsic pathways of coagulation cascade, and fibrinolytic system. Tissue kallikreins, granzyme M, neutrophil elastase, and mesotrypsin are also identified as off-targets. These off-targets and their associated pathways are elucidated for the effects of inflammation, cancer, hemorrhage, thrombosis, and central nervous system diseases (Alzheimer's disease). Prospectively, our approach is helpful not only to better understand the mechanisms of chymase inhibitors but also for drug repurposing exercises to find novel uses for these inhibitors. © 2014 Wiley Periodicals, Inc.

Evolution of branched regulatory genetic pathways: directional selection on pleiotropic loci accelerates developmental system drift.

PubMed

Johnson, Norman A; Porter, Adam H

2007-01-01

Developmental systems are regulated by a web of interacting loci. One common and useful approach in studying the evolution of development is to focus on classes of interacting elements within these systems. Here, we use individual-based simulations to study the evolution of traits controlled by branched developmental pathways involving three loci, where one locus regulates two different traits. We examined the system under a variety of selective regimes. In the case where one branch was under stabilizing selection and the other under directional selection, we observed "developmental system drift": the trait under stabilizing selection showed little phenotypic change even though the loci underlying that trait showed considerable evolutionary divergence. This occurs because the pleiotropic locus responds to directional selection and compensatory mutants are then favored in the pathway under stabilizing selection. Though developmental system drift may be caused by other mechanisms, it seems likely that it is accelerated by the same underlying genetic mechanism as that producing the Dobzhansky-Muller incompatibilities that lead to speciation in both linear and branched pathways. We also discuss predictions of our model for developmental system drift and how different selective regimes affect probabilities of speciation in the branched pathway system.

An overview of bioinformatics methods for modeling biological pathways in yeast.

PubMed

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao; Cheng, Jianlin

2016-03-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Proof of Concept: A review on how network and systems biology approaches aid in the discovery of potent anticancer drug combinations

PubMed Central

Azmi, Asfar S.; Wang, Zhiwei; Philip, Philip A.; Mohammad, Ramzi M.; Sarkar, Fazlul H.

2010-01-01

Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignances. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. This requires innovative approaches such as understanding the global physiological environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multifaceted diseases where efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology has emerged as a powerful tool benefiting our understanding of drug mechanism of action in real time. This mini-review highlights the significance of the network and systems biology-based strategy and presents a “proof-of-concept” recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma. PMID:21041384

Developing students’ ideas about lens imaging: teaching experiments with an image-based approach

NASA Astrophysics Data System (ADS)

Grusche, Sascha

2017-07-01

Lens imaging is a classic topic in physics education. To guide students from their holistic viewpoint to the scientists’ analytic viewpoint, an image-based approach to lens imaging has recently been proposed. To study the effect of the image-based approach on undergraduate students’ ideas, teaching experiments are performed and evaluated using qualitative content analysis. Some of the students’ ideas have not been reported before, namely those related to blurry lens images, and those developed by the proposed teaching approach. To describe learning pathways systematically, a conception-versus-time coordinate system is introduced, specifying how teaching actions help students advance toward a scientific understanding.

Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: Linking systems biology with vaccine development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Leslie G.; Khare, Sangeeta; Lawhon, Sara D.

The aim of research on infectious diseases is their prevention, and brucellosis and salmonellosis as such are classic examples of worldwide zoonoses for application of a systems biology approach for enhanced rational vaccine development. When used optimally, vaccines prevent disease manifestations, reduce transmission of disease, decrease the need for pharmaceutical intervention, and improve the health and welfare of animals, as well as indirectly protecting against zoonotic diseases of people. Advances in the last decade or so using comprehensive systems biology approaches linking genomics, proteomics, bioinformatics, and biotechnology with immunology, pathogenesis and vaccine formulation and delivery are expected to enable enhancedmore » approaches to vaccine development. The goal of this paper is to evaluate the role of computational systems biology analysis of host:pathogen interactions (the interactome) as a tool for enhanced rational design of vaccines. Systems biology is bringing a new, more robust approach to veterinary vaccine design based upon a deeper understanding of the host pathogen interactions and its impact on the host's molecular network of the immune system. A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Mycobacterium avium paratuberculosis (MAP), Salmonella enterica Typhimurium (STM), and a Salmonella mutant (isogenic *sipA, sopABDE2) and linked to the basis for rational development of vaccines for brucellosis and salmonellosis as reviewed by Adams et al. and Ficht et al. [1,2]. A bovine ligated ileal loop biological model was established to capture the host gene expression response at multiple time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a comparative pathogenicity analysis of 219 signaling and metabolic pathways and 1620 gene ontology (GO) categories that defined the host's biosignatures to each infectious condition. Through this DBN computational approach, the method identified significantly perturbed pathways and GO category groups of genes that define the pathogenicity signatures of the infectious agent. Our preliminary results provide deeper understanding of the overall complexity of host innate immune response as well as the identification of host gene perturbations that defines a unique host temporal biosignature response to each pathogen. The application of advanced computational methods for developing interactome models based on DBNs has proven to be instrumental in elucidating novel host responses and improved functional biological insight into the host defensive mechanisms. Evaluating the unique differences in pathway and GO perturbations across pathogen conditions allowed the identification of plausible host pathogen interaction mechanisms. Accordingly, a systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host pathogen interactome. Thus, we propose that such an approach has immediate application to the rational design of brucellosis and salmonellosis vaccines.« less

Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: linking systems biology with vaccine development.

PubMed

Adams, L Garry; Khare, Sangeeta; Lawhon, Sara D; Rossetti, Carlos A; Lewin, Harris A; Lipton, Mary S; Turse, Joshua E; Wylie, Dennis C; Bai, Yu; Drake, Kenneth L

2011-09-22

The aim of research on infectious diseases is their prevention, and brucellosis and salmonellosis as such are classic examples of worldwide zoonoses for application of a systems biology approach for enhanced rational vaccine development. When used optimally, vaccines prevent disease manifestations, reduce transmission of disease, decrease the need for pharmaceutical intervention, and improve the health and welfare of animals, as well as indirectly protecting against zoonotic diseases of people. Advances in the last decade or so using comprehensive systems biology approaches linking genomics, proteomics, bioinformatics, and biotechnology with immunology, pathogenesis and vaccine formulation and delivery are expected to enable enhanced approaches to vaccine development. The goal of this paper is to evaluate the role of computational systems biology analysis of host:pathogen interactions (the interactome) as a tool for enhanced rational design of vaccines. Systems biology is bringing a new, more robust approach to veterinary vaccine design based upon a deeper understanding of the host-pathogen interactions and its impact on the host's molecular network of the immune system. A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Mycobacterium avium paratuberculosis (MAP), Salmonella enterica Typhimurium (STM), and a Salmonella mutant (isogenic ΔsipA, sopABDE2) and linked to the basis for rational development of vaccines for brucellosis and salmonellosis as reviewed by Adams et al. and Ficht et al. [1,2]. A bovine ligated ileal loop biological model was established to capture the host gene expression response at multiple time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a comparative pathogenicity analysis of 219 signaling and metabolic pathways and 1620 gene ontology (GO) categories that defined the host's biosignatures to each infectious condition. Through this DBN computational approach, the method identified significantly perturbed pathways and GO category groups of genes that define the pathogenicity signatures of the infectious agent. Our preliminary results provide deeper understanding of the overall complexity of host innate immune response as well as the identification of host gene perturbations that defines a unique host temporal biosignature response to each pathogen. The application of advanced computational methods for developing interactome models based on DBNs has proven to be instrumental in elucidating novel host responses and improved functional biological insight into the host defensive mechanisms. Evaluating the unique differences in pathway and GO perturbations across pathogen conditions allowed the identification of plausible host-pathogen interaction mechanisms. Accordingly, a systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host-pathogen interactome. Thus, we propose that such an approach has immediate application to the rational design of brucellosis and salmonellosis vaccines. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Research Roadmap for Computation-Based Human Reliability Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boring, Ronald; Mandelli, Diego; Joe, Jeffrey

2015-08-01

The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and improve the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermalhydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is oftenmore » secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computation-based approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodels for Nuclear Technology to Enhance Reliability (HUNTER) and incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to model human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.« less

An integrated pathway system modeling of Saccharomyces cerevisiae HOG pathway: a Petri net based approach.

PubMed

Tomar, Namrata; Choudhury, Olivia; Chakrabarty, Ankush; De, Rajat K

2013-02-01

Biochemical networks comprise many diverse components and interactions between them. It has intracellular signaling, metabolic and gene regulatory pathways which are highly integrated and whose responses are elicited by extracellular actions. Previous modeling techniques mostly consider each pathway independently without focusing on the interrelation of these which actually functions as a single system. In this paper, we propose an approach of modeling an integrated pathway using an event-driven modeling tool, i.e., Petri nets (PNs). PNs have the ability to simulate the dynamics of the system with high levels of accuracy. The integrated set of signaling, regulatory and metabolic reactions involved in Saccharomyces cerevisiae's HOG pathway has been collected from the literature. The kinetic parameter values have been used for transition firings. The dynamics of the system has been simulated and the concentrations of major biological species over time have been observed. The phenotypic characteristics of the integrated system have been investigated under two conditions, viz., under the absence and presence of osmotic pressure. The results have been validated favorably with the existing experimental results. We have also compared our study with the study of idFBA (Lee et al., PLoS Comput Biol 4:e1000-e1086, 2008) and pointed out the differences between both studies. We have simulated and monitored concentrations of multiple biological entities over time and also incorporated feedback inhibition by Ptp2 which has not been included in the idFBA study. We have concluded that our study is the first to the best of our knowledge to model signaling, metabolic and regulatory events in an integrated form through PN model framework. This study is useful in computational simulation of system dynamics for integrated pathways as there are growing evidences that the malfunctioning of the interplay among these pathways is associated with disease.

Assigning Quantitative Function to Post-Translational Modifications Reveals Multiple Sites of Phosphorylation That Tune Yeast Pheromone Signaling Output

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pincus, David; Ryan, Christopher J.; Smith, Richard D.

2013-03-12

Cell signaling systems transmit information by post-­translationally modifying signaling proteins, often via phosphorylation. While thousands of sites of phosphorylation have been identified in proteomic studies, the vast majority of sites have no known function. Assigning functional roles to the catalog of uncharacterized phosphorylation sites is a key research challenge. Here we present a general approach to address this challenge and apply it to a prototypical signaling pathway, the pheromone response pathway in Saccharomyces cerevisiae. The pheromone pathway includes a mitogen activated protein kinase (MAPK) cascade activated by a G-­protein coupled receptor (GPCR). We used mass spectrometry-based proteomics to identify sitesmore » whose phosphorylation changed when the system was active, and evolutionary conservation to assign priority to a list of candidate MAPK regulatory sites. We made targeted alterations in those sites, and measured the effects of the mutations on pheromone pathway output in single cells. Our work identified six new sites that quantitatively tuned system output. We developed simple computational models to find system architectures that recapitulated the quantitative phenotypes of the mutants. Our results identify a number of regulated phosphorylation events that contribute to adjust the input-­output relationship of this model eukaryotic signaling system. We believe this combined approach constitutes a general means not only to reveal modification sites required to turn a pathway on and off, but also those required for more subtle quantitative effects that tune pathway output. Our results further suggest that relatively small quantitative influences from individual regulatory phosphorylation events endow signaling systems with plasticity that evolution may exploit to quantitatively tailor signaling outcomes.« less

System-based strategies for p53 recovery.

PubMed

Azam, Muhammad Rizwan; Fazal, Sahar; Ullah, Mukhtar; Bhatti, Aamer I

2018-06-01

The authors have proposed a systems theory-based novel drug design approach for the p53 pathway. The pathway is taken as a dynamic system represented by ordinary differential equations-based mathematical model. Using control engineering practices, the system analysis and subsequent controller design is performed for the re-activation of wild-type p53. p53 revival is discussed for both modes of operation, i.e. the sustained and oscillatory. To define the problem in control system paradigm, modification in the existing mathematical model is performed to incorporate the effect of Nutlin. Attractor point analysis is carried out to select the suitable domain of attraction. A two-loop negative feedback control strategy is devised to drag the system trajectories to the attractor point and to regulate cellular concentration of Nutlin, respectively. An integrated framework is constituted to incorporate the pharmacokinetic effects of Nutlin in the cancerous cells. Bifurcation analysis is also performed on the p53 model to see the conditions for p53 oscillation.

Using the zebrafish to understand tendon development and repair

PubMed Central

Chen, Jessica W.; Galloway, Jenna L.

2017-01-01

Tendons are important components of our musculoskeletal system. Injuries to these tissues are very common, resulting from occupational-related injuries, sports-related trauma, and age-related degeneration. Unfortunately, there are few treatment options, and current therapies rarely restore injured tendons to their original function. An improved understanding of the pathways regulating their development and repair would have significant impact in stimulating the formulation of regenerative-based approaches for tendon injury. The zebrafish provides an ideal system in which to perform genetic and chemical screens to identify new pathways involved in tendon biology. Until recently, there had been few descriptions of tendons and ligaments in the zebrafish and their similarity to mammalian tendon tissues. In this chapter, we describe the development of the zebrafish tendon and ligament tissues in the context of their gene expression, structure, and interactions with neighboring musculoskeletal tissues. We highlight the similarities with tendon development in higher vertebrates, showing that the craniofacial tendons and ligaments in zebrafish morphologically, molecularly, and structurally resemble mammalian tendons and ligaments from embryonic to adult stages. We detail methods for fluorescent in situ hybridization and immunohistochemistry as an assay to examine morphological changes in the zebrafish musculoskeleton. Staining assays such as these could provide the foundation for screen-based approaches to identify new regulators of tendon development, morphogenesis, and repair. These discoveries would provide new targets and pathways to study in the context of regenerative medicine-based approaches to improve tendon healing. PMID:28129848

Pathways--A Case of Large-Scale Implementation of Evidence-Based Practice in Scientific Inquiry-Based Science Education

ERIC Educational Resources Information Center

Sotiriou, Sofoklis; Bybee, Rodger W.; Bogner, Franz X.

2017-01-01

The fundamental pioneering ideas about student-centered, inquiry-based learning initiatives are differing in Europe and the US. The latter had initiated various top-down schemes that have led to well-defined standards, while in Europe, with its some 50 independent educational systems, a wide variety of approaches has been evolved. In this present…

Integration of bio-inspired, control-based visual and olfactory data for the detection of an elusive target

NASA Astrophysics Data System (ADS)

Duong, Tuan A.; Duong, Nghi; Le, Duong

2017-01-01

In this paper, we present an integration technique using a bio-inspired, control-based visual and olfactory receptor system to search for elusive targets in practical environments where the targets cannot be seen obviously by either sensory data. Bio-inspired Visual System is based on a modeling of extended visual pathway which consists of saccadic eye movements and visual pathway (vertebrate retina, lateral geniculate nucleus and visual cortex) to enable powerful target detections of noisy, partial, incomplete visual data. Olfactory receptor algorithm, namely spatial invariant independent component analysis, that was developed based on data of old factory receptor-electronic nose (enose) of Caltech, is adopted to enable the odorant target detection in an unknown environment. The integration of two systems is a vital approach and sets up a cornerstone for effective and low-cost of miniaturized UAVs or fly robots for future DOD and NASA missions, as well as for security systems in Internet of Things environments.

Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cannon, William; Zucker, Jeremy; Baxter, Douglas

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ODE-based optimization approach based on Marcelin’s 1910 mass action equation is used to obtain the maximum entropy distribution, (2) the predicted metabolite concentrations are compared to those generally expected from experiment using a loss function from which post-translational regulation of enzymes is inferred, (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrationsmore » and reaction fluxes, and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1, 6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.« less

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework.

PubMed

Teeguarden, Justin G; Tan, Yu-Mei; Edwards, Stephen W; Leonard, Jeremy A; Anderson, Kim A; Corley, Richard A; Kile, Molly L; Simonich, Staci M; Stone, David; Tanguay, Robert L; Waters, Katrina M; Harper, Stacey L; Williams, David E

2016-05-03

Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the "systems approaches" used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.

A Brain Centred View of Psychiatric Comorbidity in Tinnitus: From Otology to Hodology

PubMed Central

Minichino, Amedeo; Panico, Roberta; Testugini, Valeria; Altissimi, Giancarlo; Cianfrone, Giancarlo

2014-01-01

Introduction. Comorbid psychiatric disorders are frequent among patients affected by tinnitus. There are mutual clinical influences between tinnitus and psychiatric disorders, as well as neurobiological relations based on partially overlapping hodological and neuroplastic phenomena. The aim of the present paper is to review the evidence of alterations in brain networks underlying tinnitus physiopathology and to discuss them in light of the current knowledge of the neurobiology of psychiatric disorders. Methods. Relevant literature was identified through a search on Medline and PubMed; search terms included tinnitus, brain, plasticity, cortex, network, and pathways. Results. Tinnitus phenomenon results from systemic-neurootological triggers followed by neuronal remapping within several auditory and nonauditory pathways. Plastic reorganization and white matter alterations within limbic system, arcuate fasciculus, insula, salience network, dorsolateral prefrontal cortex, auditory pathways, ffrontocortical, and thalamocortical networks are discussed. Discussion. Several overlapping brain network alterations do exist between tinnitus and psychiatric disorders. Tinnitus, initially related to a clinicoanatomical approach based on a cortical localizationism, could be better explained by an holistic or associationist approach considering psychic functions and tinnitus as emergent properties of partially overlapping large-scale neural networks. PMID:25018882

Identification of metabolic pathways using pathfinding approaches: a systematic review.

PubMed

Abd Algfoor, Zeyad; Shahrizal Sunar, Mohd; Abdullah, Afnizanfaizal; Kolivand, Hoshang

2017-03-01

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization

PubMed Central

Hasegawa, Takanori; Yamaguchi, Rui; Nagasaki, Masao; Miyano, Satoru; Imoto, Seiya

2014-01-01

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information. PMID:25162401

Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction.

PubMed

Gawthrop, Peter J

2017-04-01

Engineering systems modeling and analysis based on the bond graph approach has been applied to biomolecular systems. In this context, the notion of a Faraday-equivalent chemical potential is introduced which allows chemical potential to be expressed in an analogous manner to electrical volts thus allowing engineering intuition to be applied to biomolecular systems. Redox reactions, and their representation by half-reactions, are key components of biological systems which involve both electrical and chemical domains. A bond graph interpretation of redox reactions is given which combines bond graphs with the Faraday-equivalent chemical potential. This approach is particularly relevant when the biomolecular system implements chemoelectrical transduction - for example chemiosmosis within the key metabolic pathway of mitochondria: oxidative phosphorylation. An alternative way of implementing computational modularity using bond graphs is introduced and used to give a physically based model of the mitochondrial electron transport chain To illustrate the overall approach, this model is analyzed using the Faraday-equivalent chemical potential approach and engineering intuition is used to guide affinity equalisation: a energy based analysis of the mitochondrial electron transport chain.

Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach

PubMed Central

Sternberg, Michael J.E.; Tamaddoni-Nezhad, Alireza; Lesk, Victor I.; Kay, Emily; Hitchen, Paul G.; Cootes, Adrian; van Alphen, Lieke B.; Lamoureux, Marc P.; Jarrell, Harold C.; Rawlings, Christopher J.; Soo, Evelyn C.; Szymanski, Christine M.; Dell, Anne; Wren, Brendan W.; Muggleton, Stephen H.

2013-01-01

Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning—the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system. PMID:23103756

Gene function hypotheses for the Campylobacter jejuni glycome generated by a logic-based approach.

PubMed

Sternberg, Michael J E; Tamaddoni-Nezhad, Alireza; Lesk, Victor I; Kay, Emily; Hitchen, Paul G; Cootes, Adrian; van Alphen, Lieke B; Lamoureux, Marc P; Jarrell, Harold C; Rawlings, Christopher J; Soo, Evelyn C; Szymanski, Christine M; Dell, Anne; Wren, Brendan W; Muggleton, Stephen H

2013-01-09

Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning-the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system. Copyright © 2012 Elsevier Ltd. All rights reserved.

Genetics pathway-based imaging approaches in Chinese Han population with Alzheimer's disease risk.

PubMed

Bai, Feng; Liao, Wei; Yue, Chunxian; Pu, Mengjia; Shi, Yongmei; Yu, Hui; Yuan, Yonggui; Geng, Leiyu; Zhang, Zhijun

2016-01-01

The tau hypothesis has been raised with regard to the pathophysiology of Alzheimer's disease (AD). Mild cognitive impairment (MCI) is associated with a high risk for developing AD. However, no study has directly examined the brain topological alterations based on combined effects of tau protein pathway genes in MCI population. Forty-three patients with MCI and 30 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) in Chinese Han, and a tau protein pathway-based imaging approaches (7 candidate genes: 17 SNPs) were used to investigate changes in the topological organisation of brain activation associated with MCI. Impaired regional activation is related to tau protein pathway genes (5/7 candidate genes) in patients with MCI and likely in topologically convergent and divergent functional alterations patterns associated with genes, and combined effects of tau protein pathway genes disrupt the topological architecture of cortico-cerebellar loops. The associations between the loops and behaviours further suggest that tau protein pathway genes do play a significant role in non-episodic memory impairment. Tau pathway-based imaging approaches might strengthen the credibility in imaging genetic associations and generate pathway frameworks that might provide powerful new insights into the neural mechanisms that underlie MCI.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

PubMed Central

Benson, Helen E; Sharman, Joanna L; Mpamhanga, Chido P; Parton, Andrew; Southan, Christopher; Harmar, Anthony J; Ghazal, Peter

2017-01-01

Background and Purpose An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. Experimental Approach Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. Key Results We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects. Conclusion and Implications We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses. PMID:28910500

Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts.

PubMed

Mrabet, Yassine; Semmar, Nabil

2010-05-01

Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stiochiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize the metabolic flux distribution through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites in order to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.

Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach.

PubMed

Peng, Huiming; Peng, Tao; Wen, Jianguo; Engler, David A; Matsunami, Risë K; Su, Jing; Zhang, Le; Chang, Chung-Che Jeff; Zhou, Xiaobo

2014-07-01

p38 mitogen-activated protein kinase activation plays an important role in resistance to chemotherapeutic cytotoxic drugs in treating multiple myeloma (MM). However, how the p38 mitogen-activated protein kinase signaling pathway is involved in drug resistance, in particular the roles that the various p38 isoforms play, remains largely unknown. To explore the underlying mechanisms, we developed a novel systems biology approach by integrating liquid chromatography-mass spectrometry and reverse phase protein array data from human MM cell lines with computational pathway models in which the unknown parameters were inferred using a proposed novel algorithm called modularized factor graph. New mechanisms predicted by our models suggest that combined activation of various p38 isoforms may result in drug resistance in MM via regulating the related pathways including extracellular signal-regulated kinase (ERK) pathway and NFкB pathway. ERK pathway regulating cell growth is synergistically regulated by p38δ isoform, whereas nuclear factor kappa B (NFкB) pathway regulating cell apoptosis is synergistically regulated by p38α isoform. This finding that p38δ isoform promotes the phosphorylation of ERK1/2 in MM cells treated with bortezomib was validated by western blotting. Based on the predicted mechanisms, we further screened drug combinations in silico and found that a promising drug combination targeting ERK1/2 and NFκB might reduce the effects of drug resistance in MM cells. This study provides a framework of a systems biology approach to studying drug resistance and drug combination selection. RPPA experimental Data and Matlab source codes of modularized factor graph for parameter estimation are freely available online at http://ctsb.is.wfubmc.edu/publications/modularized-factor-graph.php. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Application of Petri net based analysis techniques to signal transduction pathways.

PubMed

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-11-02

Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules.

Application of Petri net based analysis techniques to signal transduction pathways

PubMed Central

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-01-01

Background Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. Methods We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. Results We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. Conclusion The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules. PMID:17081284

A systems biology-led insight into the role of the proteome in neurodegenerative diseases.

PubMed

Fasano, Mauro; Monti, Chiara; Alberio, Tiziana

2016-09-01

Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases. Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer's disease, Parkinson's disease and other neurodegenerative disorders, as well as of affected human tissues. Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks.

PubMed

Xu, Zixiang; Sun, Jibin; Wu, Qiaqing; Zhu, Dunming

2017-12-11

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

A tiered approach for integrating exposure and dosimetry with ...

EPA Pesticide Factsheets

High-throughput (HT) risk screening approaches apply in vitro dose-response data to estimate potential health risks that arise from exposure to chemicals. However, much uncertainty is inherent in relating bioactivities observed in an in vitro system to the perturbations of biological mechanisms that lead to apical adverse health outcomes in living organisms. The chemical-agnostic Adverse Outcome Pathway (AOP) framework addresses this uncertainty by acting as a scaffold onto which pathway-based data can be arranged to aid in the understanding of in vitro toxicity testing results. In addition, risk estimation also requires reconciling chemical concentrations sufficient to produce bioactivity in vitro with concentrations that trigger a molecular initiating event (MIE) at the relevant biological target in vivo. Such target site exposures (TSEs) can be estimated using computational models to integrate exposure information with a chemical’s absorption, distribution, metabolism, and elimination (ADME) processes. In this presentation, the utility of a tiered approach for integrating exposure, ADME, and hazard into risk-based decision making will be demonstrated using several case studies, along with the investigation of how uncertainties in exposure and ADME might impact risk estimates. These case studies involve 1) identifying and prioritizing chemicals capable of altering biological pathways based on their potential to reach an in vivo target; 2) evaluating the infl

Pathway enrichment analysis approach based on topological structure and updated annotation of pathway.

PubMed

Yang, Qian; Wang, Shuyuan; Dai, Enyu; Zhou, Shunheng; Liu, Dianming; Liu, Haizhou; Meng, Qianqian; Jiang, Bin; Jiang, Wei

2017-08-16

Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Parallel labeling experiments for pathway elucidation and (13)C metabolic flux analysis.

PubMed

Antoniewicz, Maciek R

2015-12-01

Metabolic pathway models provide the foundation for quantitative studies of cellular physiology through the measurement of intracellular metabolic fluxes. For model organisms metabolic models are well established, with many manually curated genome-scale model reconstructions, gene knockout studies and stable-isotope tracing studies. However, for non-model organisms a similar level of knowledge is often lacking. Compartmentation of cellular metabolism in eukaryotic systems also presents significant challenges for quantitative (13)C-metabolic flux analysis ((13)C-MFA). Recently, innovative (13)C-MFA approaches have been developed based on parallel labeling experiments, the use of multiple isotopic tracers and integrated data analysis, that allow more rigorous validation of pathway models and improved quantification of metabolic fluxes. Applications of these approaches open new research directions in metabolic engineering, biotechnology and medicine. Copyright © 2015 Elsevier Ltd. All rights reserved.

The multiscale backbone of the human phenotype network based on biological pathways.

PubMed

Darabos, Christian; White, Marquitta J; Graham, Britney E; Leung, Derek N; Williams, Scott M; Moore, Jason H

2014-01-25

Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases' common biology, and in the elaboration of diagnosis and treatments.

Use of High-Throughput Testing and Approaches for Evaluating Chemical Risk-Relevance to Humans

EPA Science Inventory

ToxCast is profiling the bioactivity of thousands of chemicals based on high-throughput screening (HTS) and computational models that integrate knowledge of biological systems and in vivo toxicities. Many of these assays probe signaling pathways and cellular processes critical to...

Drug target inference through pathway analysis of genomics data

PubMed Central

Ma, Haisu; Zhao, Hongyu

2013-01-01

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Establishment of a luciferase assay-based screening system: Fumitremorgin C selectively inhibits cellular proliferation of immortalized astrocytes expressing an active form of AKT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Lei; Sasai, Ken; Akagi, Tsuyoshi

2008-08-29

The AKT pathway is frequently activated in glioblastoma, and as such, inhibitors of this pathway could prove very useful as anti-glioblastoma therapies. Here we established immortalized astrocytes expressing Renilla luciferase as well as those expressing both an active form of AKT and firefly luciferase. Since both luciferase activities represent the numbers of corresponding cell lines, novel inhibitors of the AKT pathway can be identified by treating co-cultures containing the two types of luciferase-expressing cells with individual compounds. Indeed, such a screening system succeeded in identifying fumitremorgin C as an efficient inhibitor of the AKT pathway, which was further confirmed bymore » the ability of fumitremorgin C to selectively inhibit the growth of immortalized astrocytes expressing an active form of AKT. The present study proposes a broadly applicable approach for identifying therapeutic agents that target the pathways and/or molecules responsible for cancer development.« less

Explaining outcomes in major system change: a qualitative study of implementing centralised acute stroke services in two large metropolitan regions in England.

PubMed

Fulop, Naomi J; Ramsay, Angus I G; Perry, Catherine; Boaden, Ruth J; McKevitt, Christopher; Rudd, Anthony G; Turner, Simon J; Tyrrell, Pippa J; Wolfe, Charles D A; Morris, Stephen

2016-06-03

Implementing major system change in healthcare is not well understood. This gap may be addressed by analysing change in terms of interrelated components identified in the implementation literature, including decision to change, intervention selection, implementation approaches, implementation outcomes, and intervention outcomes. We conducted a qualitative study of two cases of major system change: the centralisation of acute stroke services in Manchester and London, which were associated with significantly different implementation outcomes (fidelity to referral pathway) and intervention outcomes (provision of evidence-based care, patient mortality). We interviewed stakeholders at national, pan-regional, and service-levels (n = 125) and analysed 653 documents. Using a framework developed for this study from the implementation science literature, we examined factors influencing implementation approaches; how these approaches interacted with the models selected to influence implementation outcomes; and their relationship to intervention outcomes. London and Manchester's differing implementation outcomes were influenced by the different service models selected and implementation approaches used. Fidelity to the referral pathway was higher in London, where a 'simpler', more inclusive model was used, implemented with a 'big bang' launch and 'hands-on' facilitation by stroke clinical networks. In contrast, a phased approach of a more complex pathway was used in Manchester, and the network acted more as a platform to share learning. Service development occurred more uniformly in London, where service specifications were linked to financial incentives, and achieving standards was a condition of service launch, in contrast to Manchester. 'Hands-on' network facilitation, in the form of dedicated project management support, contributed to achievement of these standards in London; such facilitation processes were less evident in Manchester. Using acute stroke service centralisation in London and Manchester as an example, interaction between model selected and implementation approaches significantly influenced fidelity to the model. The contrasting implementation outcomes may have affected differences in provision of evidence-based care and patient mortality. The framework used in this analysis may support planning and evaluating major system changes, but would benefit from application in different healthcare contexts.

Application of Adverse Outcome Pathways to U.S. EPA’s Endocrine Disruptor Screening Program

PubMed Central

Noyes, Pamela D.; Casey, Warren M.; Dix, David J.

2017-01-01

Background: The U.S. EPA’s Endocrine Disruptor Screening Program (EDSP) screens and tests environmental chemicals for potential effects in estrogen, androgen, and thyroid hormone pathways, and it is one of the only regulatory programs designed around chemical mode of action. Objectives: This review describes the EDSP’s use of adverse outcome pathway (AOP) and toxicity pathway frameworks to organize and integrate diverse biological data for evaluating the endocrine activity of chemicals. Using these frameworks helps to establish biologically plausible links between endocrine mechanisms and apical responses when those end points are not measured in the same assay. Results: Pathway frameworks can facilitate a weight of evidence determination of a chemical’s potential endocrine activity, identify data gaps, aid study design, direct assay development, and guide testing strategies. Pathway frameworks also can be used to evaluate the performance of computational approaches as alternatives for low-throughput and animal-based assays and predict downstream key events. In cases where computational methods can be validated based on performance, they may be considered as alternatives to specific assays or end points. Conclusions: A variety of biological systems affect apical end points used in regulatory risk assessments, and without mechanistic data, an endocrine mode of action cannot be determined. Because the EDSP was designed to consider mode of action, toxicity pathway and AOP concepts are a natural fit. Pathway frameworks have diverse applications to endocrine screening and testing. An estrogen pathway example is presented, and similar approaches are being used to evaluate alternative methods and develop predictive models for androgen and thyroid pathways. https://doi.org/10.1289/EHP1304 PMID:28934726

Web-based applications for building, managing and analysing kinetic models of biological systems.

PubMed

Lee, Dong-Yup; Saha, Rajib; Yusufi, Faraaz Noor Khan; Park, Wonjun; Karimi, Iftekhar A

2009-01-01

Mathematical modelling and computational analysis play an essential role in improving our capability to elucidate the functions and characteristics of complex biological systems such as metabolic, regulatory and cell signalling pathways. The modelling and concomitant simulation render it possible to predict the cellular behaviour of systems under various genetically and/or environmentally perturbed conditions. This motivates systems biologists/bioengineers/bioinformaticians to develop new tools and applications, allowing non-experts to easily conduct such modelling and analysis. However, among a multitude of systems biology tools developed to date, only a handful of projects have adopted a web-based approach to kinetic modelling. In this report, we evaluate the capabilities and characteristics of current web-based tools in systems biology and identify desirable features, limitations and bottlenecks for further improvements in terms of usability and functionality. A short discussion on software architecture issues involved in web-based applications and the approaches taken by existing tools is included for those interested in developing their own simulation applications.

Computational Systems Biology and Dose Response Modeling Workshop, September 22-26, 2008

EPA Science Inventory

The recently published National Academy of Sciences (NAS) report “Toxicity Testing in the 21st Century” recommends a new approach to toxicity testing, based on evaluating cellular responses in a suite of toxicity pathway assays in human cells or cells lines in vitro. Such a parad...

Systems Biology Approach for Understanding MOA, Dose-Response and Susceptibility to Environmental Chemicals

EPA Science Inventory

There is an increasing need for assays for the rapid and efficient assessment of toxicities of large numbers of environmental chemicals. To meet this need, we have developed a battery of cell-based reporter assays that measure the activation of key cellular stress pathways. These...

A Systems Biology Approach to Iron Metabolism

PubMed Central

Chifman, J.; Laubenbacher, R.; Torti, S.V.

2015-01-01

Iron is critical to the survival of almost all living organisms. However, inappropriately low or high levels of iron are detrimental and contribute to a wide range of diseases. Recent advances in the study of iron metabolism have revealed multiple intricate pathways that are essential to the maintenance of iron homeostasis. Further, iron regulation involves processes at several scales, ranging from the subcellular to the organismal. This complexity makes a systems biology approach crucial, with its enabling technology of computational models based on a mathematical description of regulatory systems. Systems biology may represent a new strategy for understanding imbalances in iron metabolism and their underlying causes. PMID:25480643

Implementing Toxicity Testing in the 21st Century (TT21C): Making safety decisions using toxicity pathways, and progress in a prototype risk assessment.

PubMed

Adeleye, Yeyejide; Andersen, Melvin; Clewell, Rebecca; Davies, Michael; Dent, Matthew; Edwards, Sue; Fowler, Paul; Malcomber, Sophie; Nicol, Beate; Scott, Andrew; Scott, Sharon; Sun, Bin; Westmoreland, Carl; White, Andrew; Zhang, Qiang; Carmichael, Paul L

2015-06-05

Risk assessment methodologies in toxicology have remained largely unchanged for decades. The default approach uses high dose animal studies, together with human exposure estimates, and conservative assessment (uncertainty) factors or linear extrapolations to determine whether a specific chemical exposure is 'safe' or 'unsafe'. Although some incremental changes have appeared over the years, results from all new approaches are still judged against this process of extrapolating high-dose effects in animals to low-dose exposures in humans. The US National Research Council blueprint for change, entitled Toxicity Testing in the 21st Century: A Vision and Strategy called for a transformation of toxicity testing from a system based on high-dose studies in laboratory animals to one founded primarily on in vitro methods that evaluate changes in normal cellular signalling pathways using human-relevant cells or tissues. More recently, this concept of pathways-based approaches to risk assessment has been expanded by the description of 'Adverse Outcome Pathways' (AOPs). The question, however, has been how to translate this AOP/TT21C vision into the practical tools that will be useful to those expected to make safety decisions. We have sought to provide a practical example of how the TT21C vision can be implemented to facilitate a safety assessment for a commercial chemical without the use of animal testing. To this end, the key elements of the TT21C vision have been broken down to a set of actions that can be brought together to achieve such a safety assessment. Such components of a pathways-based risk assessment have been widely discussed, however to-date, no worked examples of the entire risk assessment process exist. In order to begin to test the process, we have taken the approach of examining a prototype toxicity pathway (DNA damage responses mediated by the p53 network) and constructing a strategy for the development of a pathway based risk assessment for a specific chemical in a case study mode. This contribution represents a 'work-in-progress' and is meant to both highlight concepts that are well-developed and identify aspects of the overall process which require additional development. To guide our understanding of what a pathways-based risk assessment could look like in practice, we chose to work on a case study chemical (quercetin) with a defined human exposure and to bring a multidisciplinary team of chemists, biologists, modellers and risk assessors to work together towards a safety assessment. Our goal was to see if the in vitro dose response for quercetin could be sufficiently understood to construct a TT21C risk assessment without recourse to rodent carcinogenicity study data. The data presented include high throughput pathway biomarkers (p-H2AX, p-ATM, p-ATR, p-Chk2, p53, p-p53, MDM2 and Wip1) and markers of cell-cycle, apoptosis and micronuclei formation, plus gene transcription in HT1080 cells. Eighteen point dose response curves were generated using flow cytometry and imaging to determine the concentrations that resulted in significant perturbation. NOELs and BMDs were compared to the output from biokinetic modelling and the potential for in vitro to in vivo extrapolation explored. A first tier risk assessment was performed comparing the total quercetin concentration in the in vitro systems with the predicted total quercetin concentration in plasma and tissues. The shortcomings of this approach and recommendations for improvement are described. This paper therefore describes the current progress in an ongoing research effort aimed at providing a pathways-based, proof-of-concept in vitro-only safety assessment for a consumer use product. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Linear relations in microbial reaction systems: a general overview of their origin, form, and use.

PubMed

Noorman, H J; Heijnen, J J; Ch A M Luyben, K

1991-09-01

In microbial reaction systems, there are a number of linear relations among net conversion rates. These can be very useful in the analysis of experimental data. This article provides a general approach for the formation and application of the linear relations. Two type of system descriptions, one considering the biomass as a black box and the other based on metabolic pathways, are encountered. These are defined in a linear vector and matrix algebra framework. A correct a priori description can be obtained by three useful tests: the independency, consistency, and observability tests. The independency are different. The black box approach provides only conservations relations. They are derived from element, electrical charge, energy, and Gibbs energy balances. The metabolic approach provides, in addition to the conservation relations, metabolic and reaction relations. These result from component, energy, and Gibbs energy balances. Thus it is more attractive to use the metabolic description than the black box approach. A number of different types of linear relations given in the literature are reviewed. They are classified according to the different categories that result from the black box or the metabolic system description. Validation of hypotheses related to metabolic pathways can be supported by experimental validation of the linear metabolic relations. However, definite proof from biochemical evidence remains indispensable.

Network medicine in disease analysis and therapeutics.

PubMed

Chen, B; Butte, A J

2013-12-01

Two parallel trends are occurring in drug discovery. The first is that we are moving away from a symptom-based disease classification system to a system based on molecules and molecular states. The second is that we are shifting from targeting a single molecule toward targeting multiple molecules, pathways, or networks. Network medicine is an approach to understanding disease and discovering therapeutics looking at many molecules and how they interrelate, and it may play a critical role in the adoption of both trends.

Enriched pathways for major depressive disorder identified from a genome-wide association study.

PubMed

Kao, Chung-Feng; Jia, Peilin; Zhao, Zhongming; Kuo, Po-Hsiu

2012-11-01

Major depressive disorder (MDD) has caused a substantial burden of disease worldwide with moderate heritability. Despite efforts through conducting numerous association studies and now, genome-wide association (GWA) studies, the success of identifying susceptibility loci for MDD has been limited, which is partially attributed to the complex nature of depression pathogenesis. A pathway-based analytic strategy to investigate the joint effects of various genes within specific biological pathways has emerged as a powerful tool for complex traits. The present study aimed to identify enriched pathways for depression using a GWA dataset for MDD. For each gene, we estimated its gene-wise p value using combined and minimum p value, separately. Canonical pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta were used. We employed four pathway-based analytic approaches (gene set enrichment analysis, hypergeometric test, sum-square statistic, sum-statistic). We adjusted for multiple testing using Benjamini & Hochberg's method to report significant pathways. We found 17 significantly enriched pathways for depression, which presented low-to-intermediate crosstalk. The top four pathways were long-term depression (p⩽1×10-5), calcium signalling (p⩽6×10-5), arrhythmogenic right ventricular cardiomyopathy (p⩽1.6×10-4) and cell adhesion molecules (p⩽2.2×10-4). In conclusion, our comprehensive pathway analyses identified promising pathways for depression that are related to neurotransmitter and neuronal systems, immune system and inflammatory response, which may be involved in the pathophysiological mechanisms underlying depression. We demonstrated that pathway enrichment analysis is promising to facilitate our understanding of complex traits through a deeper interpretation of GWA data. Application of this comprehensive analytic strategy in upcoming GWA data for depression could validate the findings reported in this study.

A systems-level approach for investigating organophosphorus pesticide toxicity.

PubMed

Zhu, Jingbo; Wang, Jing; Ding, Yan; Liu, Baoyue; Xiao, Wei

2018-03-01

The full understanding of the single and joint toxicity of a variety of organophosphorus (OP) pesticides is still unavailable, because of the extreme complex mechanism of action. This study established a systems-level approach based on systems toxicology to investigate OP pesticide toxicity by incorporating ADME/T properties, protein prediction, and network and pathway analysis. The results showed that most OP pesticides are highly toxic according to the ADME/T parameters, and can interact with significant receptor proteins to cooperatively lead to various diseases by the established OP pesticide -protein and protein-disease networks. Furthermore, the studies that multiple OP pesticides potentially act on the same receptor proteins and/or the functionally diverse proteins explained that multiple OP pesticides could mutually enhance toxicological synergy or additive on a molecular/systematic level. To the end, the integrated pathways revealed the mechanism of toxicity of the interaction of OP pesticides and elucidated the pathogenesis induced by OP pesticides. This study demonstrates a systems-level approach for investigating OP pesticide toxicity that can be further applied to risk assessments of various toxins, which is of significant interest to food security and environmental protection. Copyright © 2017 Elsevier Inc. All rights reserved.

Simulating Metabolism with Statistical Thermodynamics

PubMed Central

Cannon, William R.

2014-01-01

New methods are needed for large scale modeling of metabolism that predict metabolite levels and characterize the thermodynamics of individual reactions and pathways. Current approaches use either kinetic simulations, which are difficult to extend to large networks of reactions because of the need for rate constants, or flux-based methods, which have a large number of feasible solutions because they are unconstrained by the law of mass action. This report presents an alternative modeling approach based on statistical thermodynamics. The principles of this approach are demonstrated using a simple set of coupled reactions, and then the system is characterized with respect to the changes in energy, entropy, free energy, and entropy production. Finally, the physical and biochemical insights that this approach can provide for metabolism are demonstrated by application to the tricarboxylic acid (TCA) cycle of Escherichia coli. The reaction and pathway thermodynamics are evaluated and predictions are made regarding changes in concentration of TCA cycle intermediates due to 10- and 100-fold changes in the ratio of NAD+:NADH concentrations. Finally, the assumptions and caveats regarding the use of statistical thermodynamics to model non-equilibrium reactions are discussed. PMID:25089525

Simulating metabolism with statistical thermodynamics.

PubMed

Cannon, William R

2014-01-01

New methods are needed for large scale modeling of metabolism that predict metabolite levels and characterize the thermodynamics of individual reactions and pathways. Current approaches use either kinetic simulations, which are difficult to extend to large networks of reactions because of the need for rate constants, or flux-based methods, which have a large number of feasible solutions because they are unconstrained by the law of mass action. This report presents an alternative modeling approach based on statistical thermodynamics. The principles of this approach are demonstrated using a simple set of coupled reactions, and then the system is characterized with respect to the changes in energy, entropy, free energy, and entropy production. Finally, the physical and biochemical insights that this approach can provide for metabolism are demonstrated by application to the tricarboxylic acid (TCA) cycle of Escherichia coli. The reaction and pathway thermodynamics are evaluated and predictions are made regarding changes in concentration of TCA cycle intermediates due to 10- and 100-fold changes in the ratio of NAD+:NADH concentrations. Finally, the assumptions and caveats regarding the use of statistical thermodynamics to model non-equilibrium reactions are discussed.

From integrative genomics to systems genetics in the rat to link genotypes to phenotypes

PubMed Central

Moreno-Moral, Aida

2016-01-01

ABSTRACT Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease. PMID:27736746

From integrative genomics to systems genetics in the rat to link genotypes to phenotypes.

PubMed

Moreno-Moral, Aida; Petretto, Enrico

2016-10-01

Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease. © 2016. Published by The Company of Biologists Ltd.

A multiscale computational approach to dissect early events in the Erb family receptor mediated activation, differential signaling, and relevance to oncogenic transformations.

PubMed

Liu, Yingting; Purvis, Jeremy; Shih, Andrew; Weinstein, Joshua; Agrawal, Neeraj; Radhakrishnan, Ravi

2007-06-01

We describe a hierarchical multiscale computational approach based on molecular dynamics simulations, free energy-based molecular docking simulations, deterministic network-based kinetic modeling, and hybrid discrete/continuum stochastic dynamics protocols to study the dimer-mediated receptor activation characteristics of the Erb family receptors, specifically the epidermal growth factor receptor (EGFR). Through these modeling approaches, we are able to extend the prior modeling of EGF-mediated signal transduction by considering specific EGFR tyrosine kinase (EGFRTK) docking interactions mediated by differential binding and phosphorylation of different C-terminal peptide tyrosines on the RTK tail. By modeling signal flows through branching pathways of the EGFRTK resolved on a molecular basis, we are able to transcribe the effects of molecular alterations in the receptor (e.g., mutant forms of the receptor) to differing kinetic behavior and downstream signaling response. Our molecular dynamics simulations show that the drug sensitizing mutation (L834R) of EGFR stabilizes the active conformation to make the system constitutively active. Docking simulations show preferential characteristics (for wildtype vs. mutant receptors) in inhibitor binding as well as preferential enhancement of phosphorylation of particular substrate tyrosines over others. We find that in comparison to the wildtype system, the L834R mutant RTK preferentially binds the inhibitor erlotinib, as well as preferentially phosphorylates the substrate tyrosine Y1068 but not Y1173. We predict that these molecular level changes result in preferential activation of the Akt signaling pathway in comparison to the Erk signaling pathway for cells with normal EGFR expression. For cells with EGFR over expression, the mutant over activates both Erk and Akt pathways, in comparison to wildtype. These results are consistent with qualitative experimental measurements reported in the literature. We discuss these consequences in light of how the network topology and signaling characteristics of altered (mutant) cell lines are shaped differently in relationship to native cell lines.

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS.

PubMed

Durmaz, Arda; Henderson, Tim A D; Brubaker, Douglas; Bebek, Gurkan

2017-01-01

Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies. In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups. These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples. Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.

PERSPECTIVE: Electrical activity enhances neuronal survival and regeneration

NASA Astrophysics Data System (ADS)

Corredor, Raul G.; Goldberg, Jeffrey L.

2009-10-01

The failure of regeneration in the central nervous system (CNS) remains an enormous scientific and clinical challenge. After injury or in degenerative diseases, neurons in the adult mammalian CNS fail to regrow their axons and reconnect with their normal targets, and furthermore the neurons frequently die and are not normally replaced. While significant progress has been made in understanding the molecular basis for this lack of regenerative ability, a second approach has gained momentum: replacing lost neurons or lost connections with artificial electrical circuits that interface with the nervous system. In the visual system, gene therapy-based 'optogenetics' prostheses represent a competing technology. Now, the two approaches are converging, as recent data suggest that electrical activity itself, via the molecular signaling pathways such activity stimulates, is sufficient to induce neuronal survival and regeneration, particularly in retinal ganglion cells. Here, we review these data, discuss the effects of electrical activity on neurons' molecular signaling pathways and propose specific mechanisms by which exogenous electrical activity may be acting to enhance survival and regeneration.

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study.

PubMed

MacLean, Adam L; Harrington, Heather A; Stumpf, Michael P H; Byrne, Helen M

2016-01-01

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.

A Rapid Pathway Toward a Superb Gene Delivery System: Programming Structural and Functional Diversity into a Supramolecular Nanoparticle Library

PubMed Central

Wang, Hao; Liu, Kan; Chen, Kuan-Ju; Lu, Yujie; Wang, Shutao; Lin, Wei-Yu; Guo, Feng; Kamei, Ken-ichiro; Chen, Yi-Chun; Ohashi, Minori; Wang, Mingwei; Garcia, Mitch André; Zhao, Xing-Zhong; Shen, Clifton K.-F.; Tseng, Hsian-Rong

2010-01-01

Nanoparticles are regarded as promising transfection reagents for effective and safe delivery of nucleic acids into specific type of cells or tissues providing an alternative manipulation/therapy strategy to viral gene delivery. However, the current process of searching novel delivery materials is limited due to conventional low-throughput and time-consuming multistep synthetic approaches. Additionally, conventional approaches are frequently accompanied with unpredictability and continual optimization refinements, impeding flexible generation of material diversity creating a major obstacle to achieving high transfection performance. Here we have demonstrated a rapid developmental pathway toward highly efficient gene delivery systems by leveraging the powers of a supramolecular synthetic approach and a custom-designed digital microreactor. Using the digital microreactor, broad structural/functional diversity can be programmed into a library of DNA-encapsulated supramolecular nanoparticles (DNA⊂SNPs) by systematically altering the mixing ratios of molecular building blocks and a DNA plasmid. In vitro transfection studies with DNA⊂SNPs library identified the DNA⊂SNPs with the highest gene transfection efficiency, which can be attributed to cooperative effects of structures and surface chemistry of DNA⊂SNPs. We envision such a rapid developmental pathway can be adopted for generating nanoparticle-based vectors for delivery of a variety of loads. PMID:20925389

Systems Biology for Organotypic Cell Cultures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis J.

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the discussions held.« less

Workshop Report: Systems Biology for Organotypic Cell Cultures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

Workshop Report: Systems Biology for Organotypic Cell Cultures

DOE PAGES

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph; ...

2016-11-14

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

Systems biology for organotypic cell cultures.

PubMed

Grego, Sonia; Dougherty, Edward R; Alexander, Francis J; Auerbach, Scott S; Berridge, Brian R; Bittner, Michael L; Casey, Warren; Cooley, Philip C; Dash, Ajit; Ferguson, Stephen S; Fennell, Timothy R; Hawkins, Brian T; Hickey, Anthony J; Kleensang, Andre; Liebman, Michael N J; Martin, Florian; Maull, Elizabeth A; Paragas, Jason; Qiao, Guilin Gary; Ramaiahgari, Sreenivasa; Sumner, Susan J; Yoon, Miyoung

2017-01-01

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

InFlo: a novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer.

PubMed

Dimitrova, N; Nagaraj, A B; Razi, A; Singh, S; Kamalakaran, S; Banerjee, N; Joseph, P; Mankovich, A; Mittal, P; DiFeo, A; Varadan, V

2017-04-27

Characterizing the complex interplay of cellular processes in cancer would enable the discovery of key mechanisms underlying its development and progression. Published approaches to decipher driver mechanisms do not explicitly model tissue-specific changes in pathway networks and the regulatory disruptions related to genomic aberrations in cancers. We therefore developed InFlo, a novel systems biology approach for characterizing complex biological processes using a unique multidimensional framework integrating transcriptomic, genomic and/or epigenomic profiles for any given cancer sample. We show that InFlo robustly characterizes tissue-specific differences in activities of signalling networks on a genome scale using unique probabilistic models of molecular interactions on a per-sample basis. Using large-scale multi-omics cancer datasets, we show that InFlo exhibits higher sensitivity and specificity in detecting pathway networks associated with specific disease states when compared to published pathway network modelling approaches. Furthermore, InFlo's ability to infer the activity of unmeasured signalling network components was also validated using orthogonal gene expression signatures. We then evaluated multi-omics profiles of primary high-grade serous ovarian cancer tumours (N=357) to delineate mechanisms underlying resistance to frontline platinum-based chemotherapy. InFlo was the only algorithm to identify hyperactivation of the cAMP-CREB1 axis as a key mechanism associated with resistance to platinum-based therapy, a finding that we subsequently experimentally validated. We confirmed that inhibition of CREB1 phosphorylation potently sensitized resistant cells to platinum therapy and was effective in killing ovarian cancer stem cells that contribute to both platinum-resistance and tumour recurrence. Thus, we propose InFlo to be a scalable and widely applicable and robust integrative network modelling framework for the discovery of evidence-based biomarkers and therapeutic targets.

Herb pair Danggui-Honghua: mechanisms underlying blood stasis syndrome by system pharmacology approach

NASA Astrophysics Data System (ADS)

Yue, Shi-Jun; Xin, Lan-Ting; Fan, Ya-Chu; Li, Shu-Jiao; Tang, Yu-Ping; Duan, Jin-Ao; Guan, Hua-Shi; Wang, Chang-Yun

2017-01-01

Herb pair Danggui-Honghua has been frequently used for treatment of blood stasis syndrome (BSS) in China, one of the most common clinical pathological syndromes in traditional Chinese medicine (TCM). However, its therapeutic mechanism has not been clearly elucidated. In the present study, a feasible system pharmacology model based on chemical, pharmacokinetic and pharmacological data was developed via network construction approach to clarify the mechanisms of this herb pair. Thirty-one active ingredients of Danggui-Honghua possessing favorable pharmacokinetic profiles and biological activities were selected, interacting with 42 BSS-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as HMOX1, NOS2, NOS3, HIF1A and PTGS2 were mainly involved in TNF signaling pathway, HIF-1 signaling pathway, estrogen signaling pathway and neurotrophin signaling pathway. The contribution index of every active ingredient also indicated six compounds, including hydroxysafflor yellow A, safflor yellow A, safflor yellow B, Z-ligustilide, ferulic acid, and Z-butylidenephthalide, as the principal components of this herb pair. These results successfully explained the polypharmcological mechanisms underlying the efficiency of Danggui-Honghua for BSS treatment, and also probed into the potential novel therapeutic strategies for BSS in TCM.

Introducing care pathway commissioning to primary dental care: measuring performance.

PubMed

Harris, R; Bridgman, C; Ahmad, M; Bowes, L; Haley, R; Saleem, S; Singh, R; Taylor, S

2011-12-09

Care pathways have been used in a variety of ways: firstly to support quality improvement through standardising clinical processes, but also for secondary purposes, by purchasers of healthcare, to monitor activity and health outcomes and to commission services. This paper focuses on reporting a secondary use of care pathways: to commission and monitor performance of primary dental care services. Findings of a project involving three dental practices implementing a system based on rating patients according to their risk of disease and need for care are outlined. Data from surgery-based clinical databases and interviews from commissioners and providers are reported. The use of both process and outcome key performance indicators in this context is discussed, as well as issues which arise such as attributability of outcome measures and strategic approaches to improving quality of care.

Construction of large signaling pathways using an adaptive perturbation approach with phosphoproteomic data.

PubMed

Melas, Ioannis N; Mitsos, Alexander; Messinis, Dimitris E; Weiss, Thomas S; Rodriguez, Julio-Saez; Alexopoulos, Leonidas G

2012-04-01

Construction of large and cell-specific signaling pathways is essential to understand information processing under normal and pathological conditions. On this front, gene-based approaches offer the advantage of large pathway exploration whereas phosphoproteomic approaches offer a more reliable view of pathway activities but are applicable to small pathway sizes. In this paper, we demonstrate an experimentally adaptive approach to construct large signaling pathways from phosphoproteomic data within a 3-day time frame. Our approach--taking advantage of the fast turnaround time of the xMAP technology--is carried out in four steps: (i) screen optimal pathway inducers, (ii) select the responsive ones, (iii) combine them in a combinatorial fashion to construct a phosphoproteomic dataset, and (iv) optimize a reduced generic pathway via an Integer Linear Programming formulation. As a case study, we uncover novel players and their corresponding pathways in primary human hepatocytes by interrogating the signal transduction downstream of 81 receptors of interest and constructing a detailed model for the responsive part of the network comprising 177 species (of which 14 are measured) and 365 interactions.

Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches

PubMed Central

Bhattacharya, Sudin; Shoda, Lisl K.M.; Zhang, Qiang; Woods, Courtney G.; Howell, Brett A.; Siler, Scott Q.; Woodhead, Jeffrey L.; Yang, Yuching; McMullen, Patrick; Watkins, Paul B.; Andersen, Melvin E.

2012-01-01

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales. PMID:23248599

A systems biology approach for miRNA-mRNA expression patterns analysis in non-small cell lung cancer.

PubMed

Najafi, Ali; Tavallaei, Mahmood; Hosseini, Sayed Mostafa

2016-01-01

Non-small cell lung cancers (NSCLCs) is a prevalent and heterogeneous subtype of lung cancer accounting for 85 percent of patients. MicroRNAs (miRNAs), a class of small endogenous non-coding RNAs, incorporate into regulation of gene expression post-transcriptionally. Therefore, deregulation of miRNAs' expression has provided further layers of complexity to the molecular etiology and pathogenesis of different diseases and malignancies. Although, until now considerable number of studies has been carried out to illuminate this complexity in NSCLC, they have remained less effective in their goal due to lack of a holistic and integrative systems biology approach which considers all natural elaborations of miRNAs' function. It is able to reliably nominate most affected signaling pathways and therapeutic target genes by deregulated miRNAs during a particular pathological condition. Herein, we utilized a holistic systems biology approach, based on appropriate re-analyses of microarray datasets followed by reliable data filtering, to analyze integrative and combinatorial deregulated miRNA-mRNA interaction network in NSCLC, aiming to ascertain miRNA-dysregulated signaling pathway and potential therapeutic miRNAs and mRNAs which represent a lion' share during various aspects of NSCLC's pathogenesis. Our systems biology approach introduced and nominated 1) important deregulated miRNAs in NSCLCs compared with normal tissue 2) significant and confident deregulated mRNAs which were anti-correlatively targeted by deregulated miRNA in NSCLCs and 3) dysregulated signaling pathways in association with deregulated miRNA-mRNAs interactions in NSCLCs. These results introduce possible mechanism of function of deregulated miRNAs and mRNAs in NSCLC that could be used as potential therapeutic targets.

Discovery of Novel Virulence Factors of Biothreat Agents: Validation of the Phosphoproteome-Based Approach

DTIC Science & Technology

2007-06-01

minutes of infection these pathways focus on the production proteins that will regulate pro- inflammatory cytokines, chemotaxis cytokines, apoptosis, and...cytoskeleton rearrangement. The production of these proteins and events will eventually elicit a total innate immune system response. However...decreases the innate immune system response (16). The lack of proper cytokine Figure 7 - 14 - production might be caused by Francisella’s ability to

TNF-related apoptosis-inducing ligand (TRAIL): A new path to anti-cancer therapies

PubMed Central

Holoch, Peter A.; Griffith, Thomas S.

2009-01-01

Since its discovery in 1995, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), a member of the tumor necrosis factor super family, has been under intense focus because of its remarkable ability to induce apoptosis in malignant human cells while leaving normal cells unscathed. Consequently, activation of the apoptotic signaling pathway from the death-inducing TRAIL receptors provides an attractive, biologically-targeted approach to cancer therapy. A great deal of research has focused on deciphering the TRAIL receptor signaling cascade and intracellular regulation of this pathway, as many human tumor cells possess mechanisms of resistance to TRAIL-induced apoptosis. This review focuses on the currently state of knowledge regarding TRAIL signaling and resistance, the preclinical development of therapies targeted at TRAIL receptors and modulators of the pathway, and the results of clinical trials for cancer treatment that have emerged from this base of knowledge. TRAIL-based approaches to cancer therapy vary from systemic administration of recombinant, soluble TRAIL protein with or without the combination of traditional chemotherapy, radiation or novel anticancer agents to agonistic monoclonal antibodies directed against functional TRAIL receptors to TRAIL gene transfer therapy. A better understanding of TRAIL resistance mechanisms may allow for the development of more effective therapies that exploit this cell-mediated pathway to apoptosis. PMID:19836385

Modeling of nitrous oxide production by autotrophic ammonia-oxidizing bacteria with multiple production pathways.

PubMed

Ni, Bing-Jie; Peng, Lai; Law, Yingyu; Guo, Jianhua; Yuan, Zhiguo

2014-04-01

Autotrophic ammonia oxidizing bacteria (AOB) have been recognized as a major contributor to N2O production in wastewater treatment systems. However, so far N2O models have been proposed based on a single N2O production pathway by AOB, and there is still a lack of effective approach for the integration of these models. In this work, an integrated mathematical model that considers multiple production pathways is developed to describe N2O production by AOB. The pathways considered include the nitrifier denitrification pathway (N2O as the final product of AOB denitrification with NO2(-) as the terminal electron acceptor) and the hydroxylamine (NH2OH) pathway (N2O as a byproduct of incomplete oxidation of NH2OH to NO2(-)). In this model, the oxidation and reduction processes are modeled separately, with intracellular electron carriers introduced to link the two types of processes. The model is calibrated and validated using experimental data obtained with two independent nitrifying cultures. The model satisfactorily describes the N2O data from both systems. The model also predicts shifts of the dominating pathway at various dissolved oxygen (DO) and nitrite levels, consistent with previous hypotheses. This unified model is expected to enhance our ability to predict N2O production by AOB in wastewater treatment systems under varying operational conditions.

Associations between Proprioceptive Neural Pathway Structural Connectivity and Balance in People with Multiple Sclerosis

PubMed Central

Fling, Brett W.; Dutta, Geetanjali Gera; Schlueter, Heather; Cameron, Michelle H.; Horak, Fay B.

2014-01-01

Mobility and balance impairments are a hallmark of multiple sclerosis (MS), affecting nearly half of patients at presentation and resulting in decreased activity and participation, falls, injuries, and reduced quality of life. A growing body of work suggests that balance impairments in people with mild MS are primarily the result of deficits in proprioception, the ability to determine body position in space in the absence of vision. A better understanding of the pathophysiology of balance disturbances in MS is needed to develop evidence-based rehabilitation approaches. The purpose of the current study was to (1) map the cortical proprioceptive pathway in vivo using diffusion-weighted imaging and (2) assess associations between proprioceptive pathway white matter microstructural integrity and performance on clinical and behavioral balance tasks. We hypothesized that people with MS (PwMS) would have reduced integrity of cerebral proprioceptive pathways, and that reduced white matter microstructure within these tracts would be strongly related to proprioceptive-based balance deficits. We found poorer balance control on proprioceptive-based tasks and reduced white matter microstructural integrity of the cortical proprioceptive tracts in PwMS compared with age-matched healthy controls (HC). Microstructural integrity of this pathway in the right hemisphere was also strongly associated with proprioceptive-based balance control in PwMS and controls. Conversely, while white matter integrity of the right hemisphere’s proprioceptive pathway was significantly correlated with overall balance performance in HC, there was no such relationship in PwMS. These results augment existing literature suggesting that balance control in PwMS may become more dependent upon (1) cerebellar-regulated proprioceptive control, (2) the vestibular system, and/or (3) the visual system. PMID:25368564

Associations between Proprioceptive Neural Pathway Structural Connectivity and Balance in People with Multiple Sclerosis.

PubMed

Fling, Brett W; Dutta, Geetanjali Gera; Schlueter, Heather; Cameron, Michelle H; Horak, Fay B

2014-01-01

Mobility and balance impairments are a hallmark of multiple sclerosis (MS), affecting nearly half of patients at presentation and resulting in decreased activity and participation, falls, injuries, and reduced quality of life. A growing body of work suggests that balance impairments in people with mild MS are primarily the result of deficits in proprioception, the ability to determine body position in space in the absence of vision. A better understanding of the pathophysiology of balance disturbances in MS is needed to develop evidence-based rehabilitation approaches. The purpose of the current study was to (1) map the cortical proprioceptive pathway in vivo using diffusion-weighted imaging and (2) assess associations between proprioceptive pathway white matter microstructural integrity and performance on clinical and behavioral balance tasks. We hypothesized that people with MS (PwMS) would have reduced integrity of cerebral proprioceptive pathways, and that reduced white matter microstructure within these tracts would be strongly related to proprioceptive-based balance deficits. We found poorer balance control on proprioceptive-based tasks and reduced white matter microstructural integrity of the cortical proprioceptive tracts in PwMS compared with age-matched healthy controls (HC). Microstructural integrity of this pathway in the right hemisphere was also strongly associated with proprioceptive-based balance control in PwMS and controls. Conversely, while white matter integrity of the right hemisphere's proprioceptive pathway was significantly correlated with overall balance performance in HC, there was no such relationship in PwMS. These results augment existing literature suggesting that balance control in PwMS may become more dependent upon (1) cerebellar-regulated proprioceptive control, (2) the vestibular system, and/or (3) the visual system.

Systematic analysis of signaling pathways using an integrative environment.

PubMed

Visvanathan, Mahesh; Breit, Marc; Pfeifer, Bernhard; Baumgartner, Christian; Modre-Osprian, Robert; Tilg, Bernhard

2007-01-01

Understanding the biological processes of signaling pathways as a whole system requires an integrative software environment that has comprehensive capabilities. The environment should include tools for pathway design, visualization, simulation and a knowledge base concerning signaling pathways as one. In this paper we introduce a new integrative environment for the systematic analysis of signaling pathways. This system includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning signaling pathways that provides the basic understanding of the biological system, its structure and functioning. The system is designed with a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. The TNFa-mediated NF-kB signal trans-duction pathway model was designed and tested using our integrative framework. It was also useful to define the structure of the knowledge base. Sensitivity analysis of this specific pathway was performed providing simulation data. Then the model was extended showing promising initial results. The proposed system offers a holistic view of pathways containing biological and modeling data. It will help us to perform biological interpretation of the simulation results and thus contribute to a better understanding of the biological system for drug identification.

Towards a mechanism-based approach to pain management in osteoarthritis

PubMed Central

Malfait, Anne-Marie; Schnitzer, Thomas J.

2014-01-01

Pain is the defining symptom of osteoarthritis (OA), yet available treatment options, of which NSAIDs are the most common, provide inadequate pain relief and are associated with serious health risks when used long term. Chronic pain pathways are subject to complex levels of control and modulation, both in the periphery and in the central nervous system. Ongoing clinical and basic research is uncovering how these pathways operate in OA. Indeed, clinical investigation into the types of pain associated with progressive OA, the presence of central sensitization, the correlation with structural changes in the joint, and the efficacy of novel analgesics affords new insights into the pathophysiology of OA pain. Moreover, studies in disease-specific animal models enable the unravelling of the cellular and molecular pathways involved. We expect that increased understanding of the mechanisms by which chronic OA-associated pain is generated and maintained will offer opportunities for targeting and improving the safety of analgesia. In addition, using clinical and genetic approaches, it might become possible to identify subsets of patients with pain of different pathophysiology, thus enabling a tailored approach to pain management. PMID:24045707

Cross-species extrapolation of toxicity information using the ...

EPA Pesticide Factsheets

In the United States, the Endocrine Disruptor Screening Program (EDSP) was established to identify chemicals that may lead to adverse effects via perturbation of the endocrine system (i.e., estrogen, androgen, and thyroid hormone systems). In the mid-1990s the EDSP adopted a two tiered approach for screening chemicals that applied standardized in vitro and in vivo toxicity tests. The Tier 1 screening assays were designed to identify substances that have the potential of interacting with the endocrine system and Tier 2 testing was developed to identify adverse effects caused by the chemical, with documentation of dose-response relationships. While this tiered approach was effective in identifying possible endocrine disrupting chemicals, the cost and time to screen a single chemical was significant. Therefore, in 2012 the EDSP proposed a transition to make greater use of computational approaches (in silico) and high-throughput screening (HTS; in vitro) assays to more rapidly and cost-efficiently screen chemicals for endocrine activity. This transition from resource intensive, primarily in vivo, screening methods to more pathway-based approaches aligns with the simultaneously occurring transformation in toxicity testing termed “Toxicity Testing in the 21st Century” which shifts the focus to the disturbance of the biological pathway predictive of the observable toxic effects. An example of such screening tools include the US Environmental Protection Agency’s

Turning Biochemistry Inside Out: A New Approach to Teaching Metabolism in the Post-Genomic Era

ERIC Educational Resources Information Center

Gerrard, Juliet A.; Sparrow, Ashley D.

2002-01-01

This article describes a new approach to teaching metabolic pathways, designed to engage students with the material, and its complexities. Based on a novel way of presenting metabolic pathways, in which the focus is placed on proteins rather than metabolites, simple tutorial-based exercises and mini-projects are described, bringing metabolism to…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wellman, Dawn M.; Freshley, Mark D.; Truex, Michael J.

Current requirements for site remediation and closure are standards-based and are often overly conservative, costly, and in some cases, technically impractical to achieve. Use of risk-informed alternate endpoints provide a means to achieve remediation goals that are permitted by regulations and are protective of human health and the environment. Alternate endpoints enable establishing a path for cleanup that may include intermediate remedial milestones and transition points and/or regulatory alternatives to standards-based remediation. A framework is presented that is centered around developing and refining conceptual models in conjunction with assessing risks and potential endpoints as part of a system-based assessment thatmore » integrates site data with scientific understanding of processes that control the distribution and transport of contaminants in the subsurface and pathways to receptors. This system based assessment and subsequent implementation of the remediation strategy with appropriate monitoring are targeted at providing a holistic approach to addressing risks to human health and the environment. This holistic approach also enables effective predictive analysis of contaminant behavior to provide defensible criteria and data for making long-term decisions. Developing and implementing an alternate endpoint-based approach for remediation and waste site closure presents a number of challenges and opportunities. Categories of these challenges include scientific and technical, regulatory, institutional, and budget and resource allocation issues. Opportunities exist for developing and implementing systems-based approaches with respect to supportive characterization, monitoring, predictive modeling, and remediation approaches.« less

Controlling cell-free metabolism through physiochemical perturbations.

PubMed

Karim, Ashty S; Heggestad, Jacob T; Crowe, Samantha A; Jewett, Michael C

2018-01-01

Building biosynthetic pathways and engineering metabolic reactions in cells can be time-consuming due to complexities in cellular metabolism. These complexities often convolute the combinatorial testing of biosynthetic pathway designs needed to define an optimal biosynthetic system. To simplify the optimization of biosynthetic systems, we recently reported a new cell-free framework for pathway construction and testing. In this framework, multiple crude-cell extracts are selectively enriched with individual pathway enzymes, which are then mixed to construct full biosynthetic pathways on the time scale of a day. This rapid approach to building pathways aids in the study of metabolic pathway performance by providing a unique freedom of design to modify and control biological systems for both fundamental and applied biotechnology. The goal of this work was to demonstrate the ability to probe biosynthetic pathway performance in our cell-free framework by perturbing physiochemical conditions, using n-butanol synthesis as a model. We carried out three unique case studies. First, we demonstrated the power of our cell-free approach to maximize biosynthesis yields by mapping physiochemical landscapes using a robotic liquid-handler. This allowed us to determine that NAD and CoA are the most important factors that govern cell-free n-butanol metabolism. Second, we compared metabolic profile differences between two different approaches for building pathways from enriched lysates, heterologous expression and cell-free protein synthesis. We discover that phosphate from PEP utilization, along with other physiochemical reagents, during cell-free protein synthesis-coupled, crude-lysate metabolic system operation inhibits optimal cell-free n-butanol metabolism. Third, we show that non-phosphorylated secondary energy substrates can be used to fuel cell-free protein synthesis and n-butanol biosynthesis. Taken together, our work highlights the ease of using cell-free systems to explore physiochemical perturbations and suggests the need for a more controllable, multi-step, separated cell-free framework for future pathway prototyping and enzyme discovery efforts. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Activation and Resolution of Periodontal Inflammation and Its Systemic Impact

PubMed Central

Hasturk, Hatice; Kantarci, Alpdogan

2015-01-01

Inflammation is a highly organized event impacting upon organs, tissues and biological systems. Periodontal diseases are characterized by dysregulation or dysfunction of resolution pathways of inflammation resulting in a failure of healing and a dominant chronic, progressive, destructive and predominantly unresolved inflammation. The biological consequences of inflammatory processes may be independent of the etiological agents such as trauma, microbial organisms and stress. The impact of the inflammatory pathological process depends upon the affected tissues or organ system. Whilst mediators are similar, there is a tissue specificity for the inflammatory events. It is plausible that inflammatory processes in one organ could directly lead to pathologies in another organ or tissue. Communication between distant parts of the body and their inflammatory status is also mediated by common signaling mechanisms mediated via cells and soluble mediators. This review focuses on periodontal inflammation, its systemic associations and advances in therapeutic approaches based on mediators acting through orchestration of natural pathway to resolution of inflammation. We also discuss a new treatment concept where natural pathways of resolution of periodontal inflammation can be used to limit systemic inflammation and promote healing and regeneration. PMID:26252412

Development of an image-based screening system for inhibitors of the plastidial MEP pathway and of protein geranylgeranylation

PubMed Central

Hartmann, Michael; Gas-Pascual, Elisabet; Hemmerlin, Andrea; Rohmer, Michel; Bach, Thomas J.

2015-01-01

In a preceding study we have recently established an in vivo visualization system for the geranylgeranylation of proteins in a stably transformed tobacco BY-2 cell line, which involves expressing a dexamethasone-inducible GFP fused to the prenylable, carboxy-terminal basic domain of the rice calmodulin CaM61, which naturally bears a CaaL geranylgeranylation motif (GFP-BD-CVIL). By using pathway-specific inhibitors it was there demonstrated that inhibition of the methylerythritol phosphate (MEP) pathway with oxoclomazone and fosmidomycin, as well as inhibition of protein geranylgeranyl transferase type 1 (PGGT-1), shifted the localization of the GFP-BD-CVIL protein from the membrane to the nucleus. In contrast, the inhibition of the mevalonate (MVA) pathway with mevinolin did not affect this localization. Furthermore, in this initial study complementation assays with pathway-specific intermediates confirmed that the precursors for the cytosolic isoprenylation of this fusion protein are predominantly provided by the MEP pathway. In order to optimize this visualization system from a more qualitative assay to a statistically trustable medium or a high-throughput screening system, we established now new conditions that permit culture and analysis in 96-well microtiter plates, followed by fluorescence microscopy. For further refinement, the existing GFP-BD-CVIL cell line was transformed with an estradiol-inducible vector driving the expression of a RFP protein, C-terminally fused to a nuclear localization signal (NLS-RFP). We are thus able to quantify the total number of viable cells versus the number of inhibited cells after various treatments. This approach also includes a semi-automatic counting system, based on the freely available image processing software. As a result, the time of image analysis as well as the risk of user-generated bias is reduced to a minimum. Moreover, there is no cross-induction of gene expression by dexamethasone and estradiol, which is an important prerequisite for this test system. PMID:26309725

Reconstruction of metabolic pathways for the cattle genome

PubMed Central

Seo, Seongwon; Lewin, Harris A

2009-01-01

Background Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement. Results An amalgamated cattle genome database was created using the NCBI and Ensembl cattle genome databases (based on build 3.1) as data sources. PathwayTools was used to create a cattle-specific pathway genome database, which was followed by comprehensive manual curation for the reconstruction of metabolic pathways. The curated database, CattleCyc 1.0, consists of 217 metabolic pathways. A total of 64 mammalian-specific metabolic pathways were modified from the reference pathways in MetaCyc, and two pathways previously identified but missing from MetaCyc were added. Comparative analysis of metabolic pathways revealed the absence of mammalian genes for 22 metabolic enzymes whose activity was reported in the literature. We also identified six human metabolic protein-coding genes for which the cattle ortholog is missing from the sequence assembly. Conclusion CattleCyc is a powerful tool for understanding the biology of ruminants and other cetartiodactyl species. In addition, the approach used to develop CattleCyc provides a framework for the metabolic reconstruction of other newly sequenced mammalian genomes. It is clear that metabolic pathway analysis strongly reflects the quality of the underlying genome annotations. Thus, having well-annotated genomes from many mammalian species hosted in BioCyc will facilitate the comparative analysis of metabolic pathways among different species and a systems approach to comparative physiology. PMID:19284618

Shotgun metabolomic approach based on mass spectrometry for hepatic mitochondria of mice under arsenic exposure.

PubMed

García-Sevillano, M A; García-Barrera, T; Navarro, F; Montero-Lobato, Z; Gómez-Ariza, J L

2015-04-01

Mass spectrometry (MS)-based toxicometabolomics requires analytical approaches for obtaining unbiased metabolic profiles. The present work explores the general application of direct infusion MS using a high mass resolution analyzer (a hybrid systems triple quadrupole-time-of-flight) and a complementary gas chromatography-MS analysis to mitochondria extracts from mouse hepatic cells, emphasizing on mitochondria isolation from hepatic cells with a commercial kit, sample treatment after cell lysis, comprehensive metabolomic analysis and pattern recognition from metabolic profiles. Finally, the metabolomic platform was successfully checked on a case-study based on the exposure experiment of mice Mus musculus to inorganic arsenic during 12 days. Endogenous metabolites alterations were recognized by partial least squares-discriminant analysis. Subsequently, metabolites were identified by combining MS/MS analysis and metabolomics databases. This work reports for the first time the effects of As-exposure on hepatic mitochondria metabolic pathways based on MS, and reveals disturbances in Krebs cycle, β-oxidation pathway, amino acids degradation and perturbations in creatine levels. This non-target analysis provides extensive metabolic information from mitochondrial organelle, which could be applied to toxicology, pharmacology and clinical studies.

Quantifying nitrous oxide production pathways in wastewater treatment systems using isotope technology - A critical review.

PubMed

Duan, Haoran; Ye, Liu; Erler, Dirk; Ni, Bing-Jie; Yuan, Zhiguo

2017-10-01

Nitrous oxide (N 2 O) is an important greenhouse gas and an ozone-depleting substance which can be emitted from wastewater treatment systems (WWTS) causing significant environmental impacts. Understanding the N 2 O production pathways and their contribution to total emissions is the key to effective mitigation. Isotope technology is a promising method that has been applied to WWTS for quantifying the N 2 O production pathways. Within the scope of WWTS, this article reviews the current status of different isotope approaches, including both natural abundance and labelled isotope approaches, to N 2 O production pathways quantification. It identifies the limitations and potential problems with these approaches, as well as improvement opportunities. We conclude that, while the capabilities of isotope technology have been largely recognized, the quantification of N 2 O production pathways with isotope technology in WWTS require further improvement, particularly in relation to its accuracy and reliability. Copyright © 2017 Elsevier Ltd. All rights reserved.

Flooding Capability for River-based Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Curtis L.; Prescott, Steven; Ryan, Emerald

2015-10-01

This report describes the initial investigation into modeling and simulation tools for application of riverine flooding representation as part of the Risk-Informed Safety Margin Characterization (RISMC) Pathway external hazards evaluations. The report provides examples of different flooding conditions and scenarios that could impact river and watershed systems. Both 2D and 3D modeling approaches are described.

Biological profiling of the ToxCast Phase II Chemical Library in Primary Human Cell Co-Culture Systems

EPA Science Inventory

The U.S. EPA’s ToxCast research project was developed to address the need for high-throughput testing of chemicals and a pathway-based approach to hazard screening. Phase I of ToxCast tested over 300 unique compounds (mostly pesticides and antimicrobials). With the addition of Ph...

Adverse Outcome Pathways and Systems Biology as Conceptual Approaches to Support Development of 21st Century Test Methods and Extrapolation Tools

EPA Science Inventory

The proposed paradigm for “Toxicity Testing in the 21st Century” supports the development of mechanistically-based, high-throughput in vitro assays as a potential cost effective and scientifically-sound alternative to some whole animal hazard testing. To accomplish this long-term...

A comprehensive map of the influenza A virus replication cycle

PubMed Central

2013-01-01

Background Influenza is a common infectious disease caused by influenza viruses. Annual epidemics cause severe illnesses, deaths, and economic loss around the world. To better defend against influenza viral infection, it is essential to understand its mechanisms and associated host responses. Many studies have been conducted to elucidate these mechanisms, however, the overall picture remains incompletely understood. A systematic understanding of influenza viral infection in host cells is needed to facilitate the identification of influential host response mechanisms and potential drug targets. Description We constructed a comprehensive map of the influenza A virus (‘IAV’) life cycle (‘FluMap’) by undertaking a literature-based, manual curation approach. Based on information obtained from publicly available pathway databases, updated with literature-based information and input from expert virologists and immunologists, FluMap is currently composed of 960 factors (i.e., proteins, mRNAs etc.) and 456 reactions, and is annotated with ~500 papers and curation comments. In addition to detailing the type of molecular interactions, isolate/strain specific data are also available. The FluMap was built with the pathway editor CellDesigner in standard SBML (Systems Biology Markup Language) format and visualized as an SBGN (Systems Biology Graphical Notation) diagram. It is also available as a web service (online map) based on the iPathways+ system to enable community discussion by influenza researchers. We also demonstrate computational network analyses to identify targets using the FluMap. Conclusion The FluMap is a comprehensive pathway map that can serve as a graphically presented knowledge-base and as a platform to analyze functional interactions between IAV and host factors. Publicly available webtools will allow continuous updating to ensure the most reliable representation of the host-virus interaction network. The FluMap is available at http://www.influenza-x.org/flumap/. PMID:24088197

Cost comparison of orthopaedic fracture pathways using discrete event simulation in a Glasgow hospital

PubMed Central

Jenkins, Paul J; McDonald, David A; Van Der Meer, Robert; Morton, Alec; Nugent, Margaret; Rymaszewski, Lech A

2017-01-01

Objective Healthcare faces the continual challenge of improving outcome while aiming to reduce cost. The aim of this study was to determine the micro cost differences of the Glasgow non-operative trauma virtual pathway in comparison to a traditional pathway. Design Discrete event simulation was used to model and analyse cost and resource utilisation with an activity-based costing approach. Data for a full comparison before the process change was unavailable so we used a modelling approach, comparing a virtual fracture clinic (VFC) with a simulated traditional fracture clinic (TFC). Setting The orthopaedic unit VFC pathway pioneered at Glasgow Royal Infirmary has attracted significant attention and interest and is the focus of this cost study. Outcome measures Our study focused exclusively on patients with non-operative trauma attending emergency department or the minor injuries unit and the subsequent step in the patient pathway. Retrospective studies of patient outcomes as a result of the protocol introductions for specific injuries are presented in association with activity costs from the models. Results Patients are satisfied with the new pathway, the information provided and the outcome of their injuries (Evidence Level IV). There was a 65% reduction in the number of first outpatient face-to-face (f2f) attendances in orthopaedics. In the VFC pathway, the resources required per day were significantly lower for all staff groups (p≤0.001). The overall cost per patient of the VFC pathway was £22.84 (95% CI 21.74 to 23.92) per patient compared with £36.81 (95% CI 35.65 to 37.97) for the TFC pathway. Conclusions Our results give a clearer picture of the cost comparison of the virtual pathway over a wholly traditional f2f clinic system. The use of simulation-based stochastic costings in healthcare economic analysis has been limited to date, but this study provides evidence for adoption of this method as a basis for its application in other healthcare settings. PMID:28882905

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, M; Craft, D

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchicalmore » clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.« less

Integrated omics analysis of specialized metabolism in medicinal plants.

PubMed

Rai, Amit; Saito, Kazuki; Yamazaki, Mami

2017-05-01

Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

A three-tiered approach for linking pharmacokinetic ...

EPA Pesticide Factsheets

The power of the adverse outcome pathway (AOP) framework arises from its utilization of pathway-based data to describe the initial interaction of a chemical with a molecular target (molecular initiating event; (MIE), followed by a progression through a series of key events that lead to an adverse outcome relevant for regulatory purposes. The AOP itself is not chemical specific, thus providing the biological context necessary for interpreting high throughput (HT) toxicity screening results. Application of the AOP framework and HT predictions in ecological and human health risk assessment, however, requires the consideration of chemical-specific properties that influence external exposure doses and target tissue doses. To address this requirement, a three-tiered approach was developed to provide a workflow for connecting biology-based AOPs to biochemical-based pharmacokinetic properties (absorption, distribution, metabolism, excretion; ADME), and then to chemical/human activity-based exposure pathways. This approach included: (1) The power of the adverse outcome pathway (AOP) framework arisesfrom its utilization of pathway-based data to describe the initial interaction of a chemical with a molecular target (molecular initiating event; (MIE), followed by a progression through a series of key events that lead to an adverse outcome relevant for regulatory purposes. The AOP itself is not chemical specific, thus providing the biological context necessary for interpreti

Endocrine disrupting chemicals in fish: developing exposure indicators and predictive models of effects based on mechanism of action.

PubMed

Ankley, Gerald T; Bencic, David C; Breen, Michael S; Collette, Timothy W; Conolly, Rory B; Denslow, Nancy D; Edwards, Stephen W; Ekman, Drew R; Garcia-Reyero, Natalia; Jensen, Kathleen M; Lazorchak, James M; Martinović, Dalma; Miller, David H; Perkins, Edward J; Orlando, Edward F; Villeneuve, Daniel L; Wang, Rong-Lin; Watanabe, Karen H

2009-05-05

Knowledge of possible toxic mechanisms (or modes) of action (MOA) of chemicals can provide valuable insights as to appropriate methods for assessing exposure and effects, thereby reducing uncertainties related to extrapolation across species, endpoints and chemical structure. However, MOA-based testing seldom has been used for assessing the ecological risk of chemicals. This is in part because past regulatory mandates have focused more on adverse effects of chemicals (reductions in survival, growth or reproduction) than the pathways through which these effects are elicited. A recent departure from this involves endocrine-disrupting chemicals (EDCs), where there is a need to understand both MOA and adverse outcomes. To achieve this understanding, advances in predictive approaches are required whereby mechanistic changes caused by chemicals at the molecular level can be translated into apical responses meaningful to ecological risk assessment. In this paper we provide an overview and illustrative results from a large, integrated project that assesses the effects of EDCs on two small fish models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio). For this work a systems-based approach is being used to delineate toxicity pathways for 12 model EDCs with different known or hypothesized toxic MOA. The studies employ a combination of state-of-the-art genomic (transcriptomic, proteomic, metabolomic), bioinformatic and modeling approaches, in conjunction with whole animal testing, to develop response linkages across biological levels of organization. This understanding forms the basis for predictive approaches for species, endpoint and chemical extrapolation. Although our project is focused specifically on EDCs in fish, we believe that the basic conceptual approach has utility for systematically assessing exposure and effects of chemicals with other MOA across a variety of biological systems.

Gene Expression Profiling Confirms the Dosage-Dependent Additive Neuroprotective Effects of Jasminoidin in a Mouse Model of Ischemia-Reperfusion Injury.

PubMed

Li, Haixia; Wang, Jingtao; Wang, Pengqian; Zhang, Yingying; Liu, Jun; Yu, Yanan; Li, Bing; Wang, Zhong

2018-01-01

Recent evidence demonstrates that a double dose of Jasminoidin (2·JA) is more effective than Jasminoidin (JA) in cerebral ischemia therapy, but its dosage-effect mechanisms are unclear. In this study, the software GeneGo MetaCore was used to perform pathway analysis of the differentially expressed genes obtained in microarrays of mice belonging to four groups (Sham, Vehicle, JA, and 2·JA), aiming to elucidate differences in JA and 2·JA's dose-dependent pharmacological mechanism from a system's perspective. The top 10 enriched pathways in the 2·JA condition were mainly involved in neuroprotection (70% of the pathways), apoptosis and survival (40%), and anti-inflammation (20%), while JA induced pathways were mainly involved in apoptosis and survival (60%), anti-inflammation (20%), and lipid metabolism (20%). Regarding shared pathways and processes, 3, 1, and 3 pathways overlapped between the Vehicle and JA, Vehicle and 2·JA, and JA and 2·JA conditions, respectively; for the top ten overlapped processes these numbers were 3, 0, and 4, respectively. The common pathways and processes in the 2·JA condition included differentially expressed genes significantly different from those in JA. Seven representative pathways were only activated by 2·JA, such as Gamma-Secretase regulation of neuronal cell development. Process network comparison indicated that significant nodes, such as alpha-MSH , ACTH , PKR1 , and WNT , were involved in the pharmacological mechanism of 2·JA. Function distribution was different between JA and 2·JA groups, indicating a dosage additive mechanism in cerebral ischemia treatment. Such systemic approach based on whole-genome multiple pathways and networks may provide an effective and alternative approach to identify alterations underlining dosage-dependent therapeutic benefits of pharmacological compounds on complex disease processes.

Molecular profiles to biology and pathways: a systems biology approach.

PubMed

Van Laere, Steven; Dirix, Luc; Vermeulen, Peter

2016-06-16

Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters.

B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis

PubMed Central

Towfic, Fadi; Gupta, Shakti; Honavar, Vasant; Subramaniam, Shankar

2012-01-01

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Collectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG pathways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches (e.g., gene coexpression network alignment algorithms) in elucidating biochemical pathways involved in complex signaling events in cells. PMID:22917187

Deciphering the pharmacological mechanism of the Chinese formula Huanglian-Jie-Du decoction in the treatment of ischemic stroke using a systems biology-based strategy

PubMed Central

Zhang, Yan-qiong; Wang, Song-song; Zhu, Wei-liang; Ma, Yan; Zhang, Fang-bo; Liang, Ri-xin; Xu, Hai-yu; Yang, Hong-jun

2015-01-01

Aim: Huanglian-Jie-Du decoction (HLJDD) is an important multiherb remedy in TCM, which is recently demonstrated to be effective to treat ischemic stroke. Here, we aimed to investigate the pharmacological mechanisms of HLJDD in the treatment of ischemic stroke using systems biology approaches. Methods: Putative targets of HLJDD were predicted using MetaDrug. An interaction network of putative HLJDD targets and known therapeutic targets for the treatment of ischemic stroke was then constructed, and candidate HLJDD targets were identified by calculating topological features, including 'Degree', 'Node-betweenness', 'Closeness', and 'K-coreness'. The binding efficiencies of the candidate HLJDD targets with the corresponding compositive compounds were further validated by a molecular docking simulation. Results: A total of 809 putative targets were obtained for 168 compositive compounds in HLJDD. Additionally, 39 putative targets were common to all four herbs of HLJDD. Next, 49 major nodes were identified as candidate HLJDD targets due to their network topological importance. The enrichment analysis based on the Gene Ontology (GO) annotation system and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway demonstrated that candidate HLJDD targets were more frequently involved in G-protein-coupled receptor signaling pathways, neuroactive ligand-receptor interactions and gap junctions, which all played important roles in the progression of ischemic stroke. Finally, the molecular docking simulation showed that 170 pairs of chemical components and candidate HLJDD targets had strong binding efficiencies. Conclusion: This study has developed for the first time a comprehensive systems approach integrating drug target prediction, network analysis and molecular docking simulation to reveal the relationships between the herbs contained in HLJDD and their putative targets and ischemic stroke-related pathways. PMID:25937634

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

PubMed

Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

2014-01-01

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.

A novel approach to select differential pathways associated with hypertrophic cardiomyopathy based on gene co‑expression analysis.

PubMed

Chen, Xiao-Min; Feng, Ming-Jun; Shen, Cai-Jie; He, Bin; Du, Xian-Feng; Yu, Yi-Bo; Liu, Jing; Chu, Hui-Min

2017-07-01

The present study was designed to develop a novel method for identifying significant pathways associated with human hypertrophic cardiomyopathy (HCM), based on gene co‑expression analysis. The microarray dataset associated with HCM (E‑GEOD‑36961) was obtained from the European Molecular Biology Laboratory‑European Bioinformatics Institute database. Informative pathways were selected based on the Reactome pathway database and screening treatments. An empirical Bayes method was utilized to construct co‑expression networks for informative pathways, and a weight value was assigned to each pathway. Differential pathways were extracted based on weight threshold, which was calculated using a random model. In order to assess whether the co‑expression method was feasible, it was compared with traditional pathway enrichment analysis of differentially expressed genes, which were identified using the significance analysis of microarrays package. A total of 1,074 informative pathways were screened out for subsequent investigations and their weight values were also obtained. According to the threshold of weight value of 0.01057, 447 differential pathways, including folding of actin by chaperonin containing T‑complex protein 1 (CCT)/T‑complex protein 1 ring complex (TRiC), purine ribonucleoside monophosphate biosynthesis and ubiquinol biosynthesis, were obtained. Compared with traditional pathway enrichment analysis, the number of pathways obtained from the co‑expression approach was increased. The results of the present study demonstrated that this method may be useful to predict marker pathways for HCM. The pathways of folding of actin by CCT/TRiC and purine ribonucleoside monophosphate biosynthesis may provide evidence of the underlying molecular mechanisms of HCM, and offer novel therapeutic directions for HCM.

Semiconductor-inspired design principles for superconducting quantum computing.

PubMed

Shim, Yun-Pil; Tahan, Charles

2016-03-17

Superconducting circuits offer tremendous design flexibility in the quantum regime culminating most recently in the demonstration of few qubit systems supposedly approaching the threshold for fault-tolerant quantum information processing. Competition in the solid-state comes from semiconductor qubits, where nature has bestowed some very useful properties which can be utilized for spin qubit-based quantum computing. Here we begin to explore how selective design principles deduced from spin-based systems could be used to advance superconducting qubit science. We take an initial step along this path proposing an encoded qubit approach realizable with state-of-the-art tunable Josephson junction qubits. Our results show that this design philosophy holds promise, enables microwave-free control, and offers a pathway to future qubit designs with new capabilities such as with higher fidelity or, perhaps, operation at higher temperature. The approach is also especially suited to qubits on the basis of variable super-semi junctions.

A Systems Biology Approach Reveals Converging Molecular Mechanisms that Link Different POPs to Common Metabolic Diseases.

PubMed

Ruiz, Patricia; Perlina, Ally; Mumtaz, Moiz; Fowler, Bruce A

2016-07-01

A number of epidemiological studies have identified statistical associations between persistent organic pollutants (POPs) and metabolic diseases, but testable hypotheses regarding underlying molecular mechanisms to explain these linkages have not been published. We assessed the underlying mechanisms of POPs that have been associated with metabolic diseases; three well-known POPs [2,3,7,8-tetrachlorodibenzodioxin (TCDD), 2,2´,4,4´,5,5´-hexachlorobiphenyl (PCB 153), and 4,4´-dichlorodiphenyldichloroethylene (p,p´-DDE)] were studied. We used advanced database search tools to delineate testable hypotheses and to guide laboratory-based research studies into underlying mechanisms by which this POP mixture could produce or exacerbate metabolic diseases. For our searches, we used proprietary systems biology software (MetaCore™/MetaDrug™) to conduct advanced search queries for the underlying interactions database, followed by directional network construction to identify common mechanisms for these POPs within two or fewer interaction steps downstream of their primary targets. These common downstream pathways belong to various cytokine and chemokine families with experimentally well-documented causal associations with type 2 diabetes. Our systems biology approach allowed identification of converging pathways leading to activation of common downstream targets. To our knowledge, this is the first study to propose an integrated global set of step-by-step molecular mechanisms for a combination of three common POPs using a systems biology approach, which may link POP exposure to diseases. Experimental evaluation of the proposed pathways may lead to development of predictive biomarkers of the effects of POPs, which could translate into disease prevention and effective clinical treatment strategies. Ruiz P, Perlina A, Mumtaz M, Fowler BA. 2016. A systems biology approach reveals converging molecular mechanisms that link different POPs to common metabolic diseases. Environ Health Perspect 124:1034-1041; http://dx.doi.org/10.1289/ehp.1510308.

RiboFACSeq: A new method for investigating metabolic and transport pathways in bacterial cells by combining a riboswitch-based sensor, fluorescence-activated cell sorting and next-generation sequencing

PubMed Central

Li, Yingfu

2017-01-01

The elucidation of the cellular processes involved in vitamin and cofactor biosynthesis is a challenging task. The conventional approaches to these investigations rely on the discovery and purification of the products (i.e proteins and metabolites) of a particular transport or biosynthetic pathway, prior to their subsequent analysis. However, the purification of low-abundance proteins or metabolites is a formidable undertaking that presents considerable technical challenges. As a solution, we present an alternative approach to such studies that circumvents the purification step. The proposed approach takes advantage of: (1) the molecular detection capabilities of a riboswitch-based sensor to detect the cellular levels of its cognate molecule, as a means to probe the integrity of the transport and biosynthetic pathways of the target molecule in cells, (2) the high-throughput screening ability of fluorescence-activated cell sorters to isolate cells in which only these specific pathways are disrupted, and (3) the ability of next-generation sequencing to quickly identify the genes of the FACS-sorted populations. This approach was named “RiboFACSeq”. Following their identification by RiboFACSeq, the role of these genes in the presumed pathway needs to be verified through appropriate functional assays. To demonstrate the utility of our approach, an adenosylcobalamin (AdoCbl)-responsive riboswitch-based sensor was used in this study to demonstrate that RiboFACSeq can be used to track and sort cells carrying genetic mutations in known AdoCbl transport and biosynthesis genes with desirable sensitivity and specificity. This method could potentially be used to elucidate any pathway of interest, as long as a suitable riboswitch-based sensor can be created. We believe that RiboFACSeq would be especially useful for the elucidation of biological pathways in which the proteins and/or their metabolites are present at very low physiological concentrations in cells, as is the case with vitamin and cofactor biosynthesis. PMID:29211762

Constructing Adverse Outcome Pathways: a Demonstration of an Ontology-based Semantics Mapping Approach

EPA Science Inventory

Adverse outcome pathway (AOP) provides a conceptual framework to evaluate and integrate chemical toxicity and its effects across the levels of biological organization. As such, it is essential to develop a resource-efficient and effective approach to extend molecular initiating ...

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teeguarden, Justin G.; Tan, Yu-Mei; Edwards, Stephen W.

Driven by major scientific advances in analytical methods, biomonitoring, and computational exposure assessment, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the computationally enabled “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) conceptmore » in the toxicological sciences. The AEP framework offers an intuitive approach to successful organization of exposure science data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathway and adverse outcome pathways, completing the source to outcome continuum and setting the stage for more efficient integration of exposure science and toxicity testing information. Together these frameworks form and inform a decision making framework with the flexibility for risk-based, hazard-based or exposure-based decisions.« less

Beyond Earth's boundaries: Human exploration of the Solar System in the 21st Century

NASA Technical Reports Server (NTRS)

1991-01-01

This is an annual report describing work accomplished in developing the knowledge base that will permit informed recommendations and decisions concerning national space policy and the goal of human expansion into the solar system. The following topics are presented: (1) pathways to human exploration; (2) human exploration case studies; (3) case study results and assessment; (4) exploration program implementation strategy; (5) approach to international cooperation; (6) recommendations; and (7) future horizons.

Protein-protein interaction analysis of Alzheimer`s disease and NAFLD based on systems biology methods unhide common ancestor pathways.

PubMed

Karbalaei, Reza; Allahyari, Marzieh; Rezaei-Tavirani, Mostafa; Asadzadeh-Aghdaei, Hamid; Zali, Mohammad Reza

2018-01-01

Analysis reconstruction networks from two diseases, NAFLD and Alzheimer`s diseases and their relationship based on systems biology methods. NAFLD and Alzheimer`s diseases are two complex diseases, with progressive prevalence and high cost for countries. There are some reports on relation and same spreading pathways of these two diseases. In addition, they have some similar risk factors, exclusively lifestyle such as feeding, exercises and so on. Therefore, systems biology approach can help to discover their relationship. DisGeNET and STRING databases were sources of disease genes and constructing networks. Three plugins of Cytoscape software, including ClusterONE, ClueGO and CluePedia, were used to analyze and cluster networks and enrichment of pathways. An R package used to define best centrality method. Finally, based on degree and Betweenness, hubs and bottleneck nodes were defined. Common genes between NAFLD and Alzheimer`s disease were 190 genes that used construct a network with STRING database. The resulting network contained 182 nodes and 2591 edges and comprises from four clusters. Enrichment of these clusters separately lead to carbohydrate metabolism, long chain fatty acid and regulation of JAK-STAT and IL-17 signaling pathways, respectively. Also seven genes selected as hub-bottleneck include: IL6, AKT1, TP53, TNF, JUN, VEGFA and PPARG. Enrichment of these proteins and their first neighbors in network by OMIM database lead to diabetes and obesity as ancestors of NAFLD and AD. Systems biology methods, specifically PPI networks, can be useful for analyzing complicated related diseases. Finding Hub and bottleneck proteins should be the goal of drug designing and introducing disease markers.

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases

PubMed Central

Arakelyan, Arsen; Nersisyan, Lilit; Petrek, Martin; Löffler-Wirth, Henry; Binder, Hans

2016-01-01

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent. PMID:27200087

Identification of glia phenotype modulators based on select glial function regulatory signaling pathways.

PubMed

Lee, Sun-Hwa; Suk, Kyoungho

2018-04-20

Despite the considerable social and economic burden on the healthcare system worldwide due to neurodegenerative diseases, there are currently few disease-altering treatment options for many of these conditions. Therefore, new approaches for both prevention and intervention for neurodegenerative diseases are urgently required. Microglia-mediated neurotoxicity is one of the pathologic hallmarks common to Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Current therapeutic approaches to target microglia-mediated neurotoxicity are focused on the identification of glia phenotype modulators (GPMs), which can inhibit the 'classical' pro-inflammatory and neurotoxic phenotypes of microglia. Areas covered: This article reviews selected microglial molecular targets and pathways involved in either neurotoxicity or neuroprotection and how their identification. Expert opinion: Microglial activation and their signaling pathways have important implications in the neurotoxicity and brain disorders. Pharmacological modulation of microglial activation may serve as a potential therapeutic approach for targeting microglia-mediated neurotoxicity. However, given that microglia change their activation states depending on the timing, stage, and severity of disease, and even aging, the appropriate window should be considered for this approach to be clinically effective. In the future, the identification of unknown extracellular signals and intracellular molecular switches that control phenotypic shifts may facilitate the development of novel therapeutics targeting microglia-mediated neurotoxicity.

21st Century Sea-Level Rise in Line with the Paris Accord

NASA Astrophysics Data System (ADS)

Jackson, Luke P.; Grinsted, Aslak; Jevrejeva, Svetlana

2018-02-01

As global average sea-level rises in the early part of this century there is great interest in how much global and local sea level will change in the forthcoming decades. The Paris Climate Agreement's proposed temperature thresholds of 1.5°C and 2°C have directed the research community to ask what differences occur in the climate system for these two states. We have developed a novel approach to combine climate model outputs that follow specific temperature pathways to make probabilistic projections of sea-level in a 1.5°C and 2°C world. We find median global sea-level (GSL) projections for 1.5°C and 2°C temperature pathways of 44 and 50 cm, respectively. The 90% uncertainty ranges (5%-95%) are both around 48 cm by 2100. In addition, we take an alternative approach to estimate the contribution from ice sheets by using a semi-empirical GSL model. Here we find median projections of 58 and 68 cm for 1.5°C and 2°C temperature pathways. The 90% uncertainty ranges are 67 and 82 cm respectively. Regional projections show similar patterns for both temperature pathways, though differences vary between the median projections (2-10 cm) and 95th percentile (5-20 cm) for the bulk of oceans using process-based approach and 10-15 cm (median) and 15-25 cm (95th percentile) using the semi-empirical approach.

A Systems Biology-Based Investigation into the Pharmacological Mechanisms of Sheng-ma-bie-jia-tang Acting on Systemic Lupus Erythematosus by Multi-Level Data Integration.

PubMed

Huang, Lin; Lv, Qi; Liu, Fenfen; Shi, Tieliu; Wen, Chengping

2015-11-12

Sheng-ma-bie-jia-tang (SMBJT) is a Traditional Chinese Medicine (TCM) formula that is widely used for the treatment of Systemic Lupus Erythematosus (SLE) in China. However, molecular mechanism behind this formula remains unknown. Here, we systematically analyzed targets of the ingredients in SMBJT to evaluate its potential molecular mechanism. First, we collected 1,267 targets from our previously published database, the Traditional Chinese Medicine Integrated Database (TCMID). Next, we conducted gene ontology and pathway enrichment analyses for these targets and determined that they were enriched in metabolism (amino acids, fatty acids, etc.) and signaling pathways (chemokines, Toll-like receptors, adipocytokines, etc.). 96 targets, which are known SLE disease proteins, were identified as essential targets and the rest 1,171 targets were defined as common targets of this formula. The essential targets directly interacted with SLE disease proteins. Besides, some common targets also had essential connections to both key targets and SLE disease proteins in enriched signaling pathway, e.g. toll-like receptor signaling pathway. We also found distinct function of essential and common targets in immune system processes. This multi-level approach to deciphering the underlying mechanism of SMBJT treatment of SLE details a new perspective that will further our understanding of TCM formulas.

Pathway Distiller - multisource biological pathway consolidation

PubMed Central

2012-01-01

Background One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. Methods After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. Results We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. Conclusions By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments. PMID:23134636

Pathway Distiller - multisource biological pathway consolidation.

PubMed

Doderer, Mark S; Anguiano, Zachry; Suresh, Uthra; Dashnamoorthy, Ravi; Bishop, Alexander J R; Chen, Yidong

2012-01-01

One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments.

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

PubMed Central

Zhang, Shihua; Zhang, Liang; Tai, Yuling; Wang, Xuewen; Ho, Chi-Tang; Wan, Xiaochun

2018-01-01

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant. PMID:29915604

Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms.

PubMed

Bianco, Luca; Riccadonna, Samantha; Lavezzo, Enrico; Falda, Marco; Formentin, Elide; Cavalieri, Duccio; Toppo, Stefano; Fontana, Paolo

2017-02-01

Pathway Inspector is an easy-to-use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology. Pathway Inspector is available at the website http://admiral.fmach.it/PI and has been developed in Python, making use of the Django Web Framework. Contact:paolo.fontana@fmach.it

Electronic Risk Assessment System as an Appropriate Tool for the Prevention of Cancer: a Qualitative Study.

PubMed

Javan Amoli, Amir Hossein; Maserat, Elham; Safdari, Reza; Zali, Mohammad Reza

2015-01-01

Decision making modalities for screening for many cancer conditions and different stages have become increasingly complex. Computer-based risk assessment systems facilitate scheduling and decision making and support the delivery of cancer screening services. The aim of this article was to survey electronic risk assessment system as an appropriate tool for the prevention of cancer. A qualitative design was used involving 21 face-to-face interviews. Interviewing involved asking questions and getting answers from exclusive managers of cancer screening. Of the participants 6 were female and 15 were male, and ages ranged from 32 to 78 years. The study was based on a grounded theory approach and the tool was a semi- structured interview. Researchers studied 5 dimensions, comprising electronic guideline standards of colorectal cancer screening, work flow of clinical and genetic activities, pathways of colorectal cancer screening and functionality of computer based guidelines and barriers. Electronic guideline standards of colorectal cancer screening were described in the s3 categories of content standard, telecommunications and technical standards and nomenclature and classification standards. According to the participations' views, workflow and genetic pathways of colorectal cancer screening were identified. The study demonstrated an effective role of computer-guided consultation for screening management. Electronic based systems facilitate real-time decision making during a clinical interaction. Electronic pathways have been applied for clinical and genetic decision support, workflow management, update recommendation and resource estimates. A suitable technical and clinical infrastructure is an integral part of clinical practice guidline of screening. As a conclusion, it is recommended to consider the necessity of architecture assessment and also integration standards.

Reconstruction and visualization of carbohydrate, N-glycosylation pathways in Pichia pastoris CBS7435 using computational and system biology approaches.

PubMed

Srivastava, Akriti; Somvanshi, Pallavi; Mishra, Bhartendu Nath

2013-06-01

Pichia pastoris is an efficient expression system for production of recombinant proteins. To understand its physiology for building novel applications it is important to understand and reconstruct its metabolic network. The metabolic reconstruction approach connects genotype with phenotype. Here, we have attempted to reconstruct carbohydrate metabolism pathways responsible for high biomass density and N-glycosylation pathways involved in the post translational modification of proteins of P. pastoris CBS7435. Both these metabolic pathways play a crucial role in heterologous protein production. We report novel, missing and unannotated enzymes involved in the target metabolic pathways. A strong possibility of cellulose and xylose metabolic processes in P. pastoris CBS7435 suggests its use in the area of biofuels. The reconstructed metabolic networks can be used for increased yields and improved product quality, for designing appropriate growth medium, for production of recombinant therapeutics and for making biofuels.

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways.

PubMed

Pey, Jon; Valgepea, Kaspar; Rubio, Angel; Beasley, John E; Planes, Francisco J

2013-12-08

The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

Protein–protein interactions and selection: yeast-based approaches that exploit guanine nucleotide-binding protein signaling.

PubMed

Ishii, Jun; Fukuda, Nobuo; Tanaka, Tsutomu; Ogino, Chiaki; Kondo, Akihiko

2010-05-01

For elucidating protein–protein interactions, many methodologies have been developed during the past two decades. For investigation of interactions inside cells under physiological conditions, yeast is an attractive organism with which to quickly screen for hopeful candidates using versatile genetic technologies, and various types of approaches are now available.Among them, a variety of unique systems using the guanine nucleotide-binding protein (G-protein) signaling pathway in yeast have been established to investigate the interactions of proteins for biological study and pharmaceutical research. G-proteins involved in various cellular processes are mainly divided into two groups: small monomeric G-proteins,and heterotrimeric G-proteins. In this minireview, we summarize the basic principles and applications of yeast-based screening systems, using these two types of G-protein, which are typically used for elucidating biological protein interactions but are differentiated from traditional yeast two-hybrid systems.

Bio-Inspired Networking — Self-Organizing Networked Embedded Systems

NASA Astrophysics Data System (ADS)

Dressler, Falko

The turn to nature has brought us many unforeseen great concepts and solutions. This course seems to hold on for many research domains. In this article, we study the applicability of biological mechanisms and techniques in the domain of communications. In particular, we study the behavior and the challenges in networked embedded systems that are meant to self-organize in large groups of nodes. Application examples include wireless sensor networks and sensor/actuator networks. Based on a review of the needs and requirements in such networks, we study selected bio-inspired networking approaches that claim to outperform other methods in specific domains. We study mechanisms in swarm intelligence, the artificial immune system, and approaches based on investigations on the cellular signaling pathways. As a major conclusion, we derive that bio-inspired networking techniques do have advantages compared to engineering methods. Nevertheless, selection and employment must be done carefully to achieve the desired performance gains.

Emerging systems biology approaches in nanotoxicology: Towards a mechanism-based understanding of nanomaterial hazard and risk

DOE Office of Scientific and Technical Information (OSTI.GOV)

Costa, Pedro M.; Fadeel, Bengt, E-mail: Bengt.Fade

Engineered nanomaterials are being developed for a variety of technological applications. However, the increasing use of nanomaterials in society has led to concerns about their potential adverse effects on human health and the environment. During the first decade of nanotoxicological research, the realization has emerged that effective risk assessment of the multitudes of new nanomaterials would benefit from a comprehensive understanding of their toxicological mechanisms, which is difficult to achieve with traditional, low-throughput, single end-point oriented approaches. Therefore, systems biology approaches are being progressively applied within the nano(eco)toxicological sciences. This novel paradigm implies that the study of biological systems shouldmore » be integrative resulting in quantitative and predictive models of nanomaterial behaviour in a biological system. To this end, global ‘omics’ approaches with which to assess changes in genes, proteins, metabolites, etc. are deployed allowing for computational modelling of the biological effects of nanomaterials. Here, we highlight omics and systems biology studies in nanotoxicology, aiming towards the implementation of a systems nanotoxicology and mechanism-based risk assessment of nanomaterials. - Highlights: • Systems nanotoxicology is a multi-disciplinary approach to quantitative modelling. • Transcriptomics, proteomics and metabolomics remain the most common methods. • Global “omics” techniques should be coupled to computational modelling approaches. • The discovery of nano-specific toxicity pathways and biomarkers is a prioritized goal. • Overall, experimental nanosafety research must endeavour reproducibility and relevance.« less

Improving the performance of surgery-based clinical pathways: a simulation-optimization approach.

PubMed

Ozcan, Yasar A; Tànfani, Elena; Testi, Angela

2017-03-01

This paper aims to improve the performance of clinical processes using clinical pathways (CPs). The specific goal of this research is to develop a decision support tool, based on a simulation-optimization approach, which identify the proper adjustment and alignment of resources to achieve better performance for both the patients and the health-care facility. When multiple perspectives are present in a decision problem, critical issues arise and often require the balancing of goals. In our approach, meeting patients' clinical needs in a timely manner, and to avoid worsening of clinical conditions, we assess the level of appropriate resources. The simulation-optimization model seeks and evaluates alternative resource configurations aimed at balancing the two main objectives-meeting patient needs and optimal utilization of beds and operating rooms.Using primary data collected at a Department of Surgery of a public hospital located in Genoa, Italy. The simulation-optimization modelling approach in this study has been applied to evaluate the thyroid surgical treatment together with the other surgery-based CPs. The low rate of bed utilization and the long elective waiting lists of the specialty under study indicates that the wards were oversized while the operating room capacity was the bottleneck of the system. The model enables hospital managers determine which objective has to be given priority, as well as the corresponding opportunity costs.

Systems-level mechanisms of action of Panax ginseng: a network pharmacological approach.

PubMed

Park, Sa-Yoon; Park, Ji-Hun; Kim, Hyo-Su; Lee, Choong-Yeol; Lee, Hae-Jeung; Kang, Ki Sung; Kim, Chang-Eop

2018-01-01

Panax ginseng has been used since ancient times based on the traditional Asian medicine theory and clinical experiences, and currently, is one of the most popular herbs in the world. To date, most of the studies concerning P. ginseng have focused on specific mechanisms of action of individual constituents. However, in spite of many studies on the molecular mechanisms of P. ginseng , it still remains unclear how multiple active ingredients of P. ginseng interact with multiple targets simultaneously, giving the multidimensional effects on various conditions and diseases. In order to decipher the systems-level mechanism of multiple ingredients of P. ginseng , a novel approach is needed beyond conventional reductive analysis. We aim to review the systems-level mechanism of P. ginseng by adopting novel analytical framework-network pharmacology. Here, we constructed a compound-target network of P. ginseng using experimentally validated and machine learning-based prediction results. The targets of the network were analyzed in terms of related biological process, pathways, and diseases. The majority of targets were found to be related with primary metabolic process, signal transduction, nitrogen compound metabolic process, blood circulation, immune system process, cell-cell signaling, biosynthetic process, and neurological system process. In pathway enrichment analysis of targets, mainly the terms related with neural activity showed significant enrichment and formed a cluster. Finally, relative degrees analysis for the target-disease association of P. ginseng revealed several categories of related diseases, including respiratory, psychiatric, and cardiovascular diseases.

Nutritional Approaches for Managing Obesity-Associated Metabolic Diseases

PubMed Central

Botchlett, Rachel; Woo, Shih-Lung; Liu, Mengyang; Pei, Ya; Guo, Xin; Li, Honggui; Wu, Chaodong

2017-01-01

Obesity is an ongoing pandemic and serves as a causal factor of a wide spectrum of metabolic diseases including diabetes, fatty liver disease, and cardiovascular disease. Much evidence has demonstrated that nutrient overload/overnutrition initiates or exacerbates inflammatory responses in tissues/organs involved in the regulation of systemic metabolic homeostasis. This obesity-associated inflammation is usually at a low-grade and viewed as metabolic inflammation. When it exists continuously, inflammation inappropriately alters metabolic pathways and impairs insulin signaling cascades in peripheral tissues/organs such as adipose tissue, the liver and skeletal muscle, resulting in local fat deposition and insulin resistance and systemic metabolic dysregulation. In addition, inflammatory mediators, e.g., proinflammatory cytokines, and excessive nutrients, e.g., glucose and fatty acids, act together to aggravate local insulin resistance and form a vicious cycle to further disturb local metabolic pathways and exacerbate systemic metabolic dysregulation. Owing to the critical role of nutrient metabolism in the control of the initiation and progression of inflammation and insulin resistance, nutritional approaches have been implicated as effective tools for managing obesity and obesity-associated metabolic diseases. Based on the mounting evidence generated from both basic and clinical research, nutritional approaches are commonly used for suppressing inflammation, improving insulin sensitivity, and/or decreasing fat deposition. Consequently, the combined effects are responsible for improvement of systemic insulin sensitivity and metabolic homeostasis. PMID:28400405

Double Dutch: A Tool for Designing Combinatorial Libraries of Biological Systems.

PubMed

Roehner, Nicholas; Young, Eric M; Voigt, Christopher A; Gordon, D Benjamin; Densmore, Douglas

2016-06-17

Recently, semirational approaches that rely on combinatorial assembly of characterized DNA components have been used to engineer biosynthetic pathways. In practice, however, it is not practical to assemble and test millions of pathway variants in order to elucidate how different DNA components affect the behavior of a pathway. To address this challenge, we apply a rigorous mathematical approach known as design of experiments (DOE) that can be used to construct empirical models of system behavior without testing all variants. To support this approach, we have developed a tool named Double Dutch, which uses a formal grammar and heuristic algorithms to automate the process of DOE library design. Compared to designing by hand, Double Dutch enables users to more efficiently and scalably design libraries of pathway variants that can be used in a DOE framework and uniquely provides a means to flexibly balance design considerations of statistical analysis, construction cost, and risk of homologous recombination, thereby demonstrating the utility of automating decision making when faced with complex design trade-offs.

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

PubMed Central

2011-01-01

Background The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data. Results We present and justify a new technique for comparing pairs of networks--in our case these networks are based on the same set of nodes and there are two distinct types of weighted edges. The algorithm is based on the Generalized Singular Value Decomposition (GSVD), which may be regarded as an extension of Principle Components Analysis to the case of two data sets. We show how the GSVD can be interpreted as a technique for reordering the two networks in order to reveal clusters that are exclusive to only one. Here we apply this algorithm to a new set of metabolomic data from the prefrontal cortex (PFC) of a translational model relevant to schizophrenia, rats treated subchronically with the N-methyl-D-Aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). This provides us with a means to quantify which predefined metabolic pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolite pathway database) were altered in the PFC of PCP-treated rats. Several significant changes were discovered, notably: 1) neuroactive ligands active at glutamate and GABA receptors are disrupted in the PFC of PCP-treated animals, 2) glutamate dysfunction in these animals was not limited to compromised glutamatergic neurotransmission but also involves the disruption of metabolic pathways linked to glutamate; and 3) a specific series of purine reactions Xanthine ← Hypoxyanthine ↔ Inosine ← IMP → adenylosuccinate is also disrupted in the PFC of PCP-treated animals. Conclusions Network reordering via the GSVD provides a means to discover statistically validated differences in clustering between a pair of networks. In practice this analytical approach, when applied to metabolomic data, allows us to quantify the alterations in metabolic pathways between two experimental groups. With this new computational technique we identified metabolic pathway alterations that are consistent with known results. Furthermore, we discovered disruption in a novel series of purine reactions that may contribute to the PFC dysfunction and cognitive deficits seen in schizophrenia. PMID:21575198

Meta-learning framework applied in bioinformatics inference system design.

PubMed

Arredondo, Tomás; Ormazábal, Wladimir

2015-01-01

This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

Modular analysis of biological networks.

PubMed

Kaltenbach, Hans-Michael; Stelling, Jörg

2012-01-01

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

Applying the tuple space-based approach to the simulation of the caspases, an essential signalling pathway.

PubMed

Cárdenas-García, Maura; González-Pérez, Pedro Pablo

2013-04-11

Apoptotic cell death plays a crucial role in development and homeostasis. This process is driven by mitochondrial permeabilization and activation of caspases. In this paper we adopt a tuple spaces-based modelling and simulation approach, and show how it can be applied to the simulation of this intracellular signalling pathway. Specifically, we are working to explore and to understand the complex interaction patterns of the caspases apoptotic and the mitochondrial role. As a first approximation, using the tuple spaces-based in silico approach, we model and simulate both the extrinsic and intrinsic apoptotic signalling pathways and the interactions between them. During apoptosis, mitochondrial proteins, released from mitochondria to cytosol are decisively involved in the process. If the decision is to die, from this point there is normally no return, cancer cells offer resistance to the mitochondrial induction.

Xtalk: a path-based approach for identifying crosstalk between signaling pathways

PubMed Central

Tegge, Allison N.; Sharp, Nicholas; Murali, T. M.

2016-01-01

Motivation: Cells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk. Existing computational methods to discover such pathway pairs rely on simple overlap statistics. Results: We present Xtalk, a path-based approach for identifying pairs of pathways that may crosstalk. Xtalk computes the statistical significance of the average length of multiple short paths that connect receptors in one pathway to the transcription factors in another. By design, Xtalk reports the precise interactions and mechanisms that support the identified crosstalk. We applied Xtalk to signaling pathways in the KEGG and NCI-PID databases. We manually curated a gold standard set of 132 crosstalking pathway pairs and a set of 140 pairs that did not crosstalk, for which Xtalk achieved an area under the receiver operator characteristic curve of 0.65, a 12% improvement over the closest competing approach. The area under the receiver operator characteristic curve varied with the pathway, suggesting that crosstalk should be evaluated on a pathway-by-pathway level. We also analyzed an extended set of 658 pathway pairs in KEGG and to a set of more than 7000 pathway pairs in NCI-PID. For the top-ranking pairs, we found substantial support in the literature (81% for KEGG and 78% for NCI-PID). We provide examples of networks computed by Xtalk that accurately recovered known mechanisms of crosstalk. Availability and implementation: The XTALK software is available at http://bioinformatics.cs.vt.edu/~murali/software. Crosstalk networks are available at http://graphspace.org/graphs?tags=2015-bioinformatics-xtalk. Contact: ategge@vt.edu, murali@cs.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26400040

Discovery of Novel Virulence Factors of Biothreat Agents: Validation of the Phosphoproteome-Based Approach

DTIC Science & Technology

2008-06-30

activated during bacterial infections of macrophage. During the first few minutes of infection these pathways focus on the production proteins that...will regulate pro- inflammatory cytokines, chemotaxis cytokines, apoptosis, and cytoskeleton rearrangement. The production of these proteins and...infection. IL-10 is an anti-inflammatory cytokine that further decreases the innate immune system response. The lack of proper cytokine production might

A Quantitative Systems Pharmacology Approach to Infer Pathways Involved in Complex Disease Phenotypes.

PubMed

Schurdak, Mark E; Pei, Fen; Lezon, Timothy R; Carlisle, Diane; Friedlander, Robert; Taylor, D Lansing; Stern, Andrew M

2018-01-01

Designing effective therapeutic strategies for complex diseases such as cancer and neurodegeneration that involve tissue context-specific interactions among multiple gene products presents a major challenge for precision medicine. Safe and selective pharmacological modulation of individual molecular entities associated with a disease often fails to provide efficacy in the clinic. Thus, development of optimized therapeutic strategies for individual patients with complex diseases requires a more comprehensive, systems-level understanding of disease progression. Quantitative systems pharmacology (QSP) is an approach to drug discovery that integrates computational and experimental methods to understand the molecular pathogenesis of a disease at the systems level more completely. Described here is the chemogenomic component of QSP for the inference of biological pathways involved in the modulation of the disease phenotype. The approach involves testing sets of compounds of diverse mechanisms of action in a disease-relevant phenotypic assay, and using the mechanistic information known for the active compounds, to infer pathways and networks associated with the phenotype. The example used here is for monogenic Huntington's disease (HD), which due to the pleiotropic nature of the mutant phenotype has a complex pathogenesis. The overall approach, however, is applicable to any complex disease.

Kinetics of protein–ligand unbinding: Predicting pathways, rates, and rate-limiting steps

PubMed Central

Tiwary, Pratyush; Limongelli, Vittorio; Salvalaglio, Matteo; Parrinello, Michele

2015-01-01

The ability to predict the mechanisms and the associated rate constants of protein–ligand unbinding is of great practical importance in drug design. In this work we demonstrate how a recently introduced metadynamics-based approach allows exploration of the unbinding pathways, estimation of the rates, and determination of the rate-limiting steps in the paradigmatic case of the trypsin–benzamidine system. Protein, ligand, and solvent are described with full atomic resolution. Using metadynamics, multiple unbinding trajectories that start with the ligand in the crystallographic binding pose and end with the ligand in the fully solvated state are generated. The unbinding rate koff is computed from the mean residence time of the ligand. Using our previously computed binding affinity we also obtain the binding rate kon. Both rates are in agreement with reported experimental values. We uncover the complex pathways of unbinding trajectories and describe the critical rate-limiting steps with unprecedented detail. Our findings illuminate the role played by the coupling between subtle protein backbone fluctuations and the solvation by water molecules that enter the binding pocket and assist in the breaking of the shielded hydrogen bonds. We expect our approach to be useful in calculating rates for general protein–ligand systems and a valid support for drug design. PMID:25605901

Microbial production of natural and non-natural flavonoids: Pathway engineering, directed evolution and systems/synthetic biology.

PubMed

Pandey, Ramesh Prasad; Parajuli, Prakash; Koffas, Mattheos A G; Sohng, Jae Kyung

2016-01-01

In this review, we address recent advances made in pathway engineering, directed evolution, and systems/synthetic biology approaches employed in the production and modification of flavonoids from microbial cells. The review is divided into two major parts. In the first, various metabolic engineering and system/synthetic biology approaches used for production of flavonoids and derivatives are discussed broadly. All the manipulations/engineering accomplished on the microorganisms since 2000 are described in detail along with the biosynthetic pathway enzymes, their sources, structures of the compounds, and yield of each product. In the second part of the review, post-modifications of flavonoids by four major reactions, namely glycosylations, methylations, hydroxylations and prenylations using recombinant strains are described. Copyright © 2016 Elsevier Inc. All rights reserved.

Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms

PubMed Central

Bianco, Luca; Riccadonna, Samantha; Lavezzo, Enrico; Falda, Marco; Formentin, Elide; Cavalieri, Duccio; Toppo, Stefano

2017-01-01

Abstract Summary: Pathway Inspector is an easy-to-use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology. Availability and Implementation: Pathway Inspector is available at the website http://admiral.fmach.it/PI and has been developed in Python, making use of the Django Web Framework. Contact: paolo.fontana@fmach.it PMID:28158604

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

PubMed

Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb

2016-01-01

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Network based transcription factor analysis of regenerating axolotl limbs

PubMed Central

2011-01-01

Background Studies on amphibian limb regeneration began in the early 1700's but we still do not completely understand the cellular and molecular events of this unique process. Understanding a complex biological process such as limb regeneration is more complicated than the knowledge of the individual genes or proteins involved. Here we followed a systems biology approach in an effort to construct the networks and pathways of protein interactions involved in formation of the accumulation blastema in regenerating axolotl limbs. Results We used the human orthologs of proteins previously identified by our research team as bait to identify the transcription factor (TF) pathways and networks that regulate blastema formation in amputated axolotl limbs. The five most connected factors, c-Myc, SP1, HNF4A, ESR1 and p53 regulate ~50% of the proteins in our data. Among these, c-Myc and SP1 regulate 36.2% of the proteins. c-Myc was the most highly connected TF (71 targets). Network analysis showed that TGF-β1 and fibronectin (FN) lead to the activation of these TFs. We found that other TFs known to be involved in epigenetic reprogramming, such as Klf4, Oct4, and Lin28 are also connected to c-Myc and SP1. Conclusions Our study provides a systems biology approach to how different molecular entities inter-connect with each other during the formation of an accumulation blastema in regenerating axolotl limbs. This approach provides an in silico methodology to identify proteins that are not detected by experimental methods such as proteomics but are potentially important to blastema formation. We found that the TFs, c-Myc and SP1 and their target genes could potentially play a central role in limb regeneration. Systems biology has the potential to map out numerous other pathways that are crucial to blastema formation in regeneration-competent limbs, to compare these to the pathways that characterize regeneration-deficient limbs and finally, to identify stem cell markers in regeneration. PMID:21418574

Cost comparison of orthopaedic fracture pathways using discrete event simulation in a Glasgow hospital.

PubMed

Anderson, Gillian H; Jenkins, Paul J; McDonald, David A; Van Der Meer, Robert; Morton, Alec; Nugent, Margaret; Rymaszewski, Lech A

2017-09-07

Healthcare faces the continual challenge of improving outcome while aiming to reduce cost. The aim of this study was to determine the micro cost differences of the Glasgow non-operative trauma virtual pathway in comparison to a traditional pathway. Discrete event simulation was used to model and analyse cost and resource utilisation with an activity-based costing approach. Data for a full comparison before the process change was unavailable so we used a modelling approach, comparing a virtual fracture clinic (VFC) with a simulated traditional fracture clinic (TFC). The orthopaedic unit VFC pathway pioneered at Glasgow Royal Infirmary has attracted significant attention and interest and is the focus of this cost study. Our study focused exclusively on patients with non-operative trauma attending emergency department or the minor injuries unit and the subsequent step in the patient pathway. Retrospective studies of patient outcomes as a result of the protocol introductions for specific injuries are presented in association with activity costs from the models. Patients are satisfied with the new pathway, the information provided and the outcome of their injuries (Evidence Level IV). There was a 65% reduction in the number of first outpatient face-to-face (f2f) attendances in orthopaedics. In the VFC pathway, the resources required per day were significantly lower for all staff groups (p≤0.001). The overall cost per patient of the VFC pathway was £22.84 (95% CI 21.74 to 23.92) per patient compared with £36.81 (95% CI 35.65 to 37.97) for the TFC pathway. Our results give a clearer picture of the cost comparison of the virtual pathway over a wholly traditional f2f clinic system. The use of simulation-based stochastic costings in healthcare economic analysis has been limited to date, but this study provides evidence for adoption of this method as a basis for its application in other healthcare settings. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model.

PubMed

Kogelman, Lisette J A; Cirera, Susanna; Zhernakova, Daria V; Fredholm, Merete; Franke, Lude; Kadarmideen, Haja N

2014-09-30

Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P < 0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be associated with obesity in humans and rodents, e.g. CSF1R and MARC2. To our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory genes related to obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis.

From generic pathways to ICT-supported horizontally integrated care: the SmartCare approach and convergence with future Internet assembly.

PubMed

Urošević, Vladimir; Mitić, Marko

2014-01-01

Successful service integration in policy and practice requires both technology innovation and service process innovation being pursued and implemented at the same time. The SmartCare project (partially EC-funded under CIP ICT PSP Program) aims to achieve this through development, piloting and evaluation of ICT-based services, horizontally integrating health and social care in ten pilot regions, including Kraljevo region in Serbia. The project has identified and adopted two generic highest-level common thematic pathways in joint consolidation phase - integrated support for long-term care and integrated support after hospital discharge. A common set of standard functional specifications for an open ICT platform enabling the delivery of integrated care is being defined, around the challenges of data sharing, coordination and communication in these two formalized pathways. Implementation and system integration on technology and architecture level are to be based on open standards, multivendor interoperability, and leveraging on the current evolving open specification technology foundations developed in relevant projects across the European Research Area.

Pathway-GPS and SIGORA: identifying relevant pathways based on the over-representation of their gene-pair signatures

PubMed Central

Foroushani, Amir B.K.; Brinkman, Fiona S.L.

2013-01-01

Motivation. Predominant pathway analysis approaches treat pathways as collections of individual genes and consider all pathway members as equally informative. As a result, at times spurious and misleading pathways are inappropriately identified as statistically significant, solely due to components that they share with the more relevant pathways. Results. We introduce the concept of Pathway Gene-Pair Signatures (Pathway-GPS) as pairs of genes that, as a combination, are specific to a single pathway. We devised and implemented a novel approach to pathway analysis, Signature Over-representation Analysis (SIGORA), which focuses on the statistically significant enrichment of Pathway-GPS in a user-specified gene list of interest. In a comparative evaluation of several published datasets, SIGORA outperformed traditional methods by delivering biologically more plausible and relevant results. Availability. An efficient implementation of SIGORA, as an R package with precompiled GPS data for several human and mouse pathway repositories is available for download from http://sigora.googlecode.com/svn/. PMID:24432194

Development of a risk-based index for source water protection planning, which supports the reduction of pathogens from agricultural activity entering water resources.

PubMed

Goss, Michael; Richards, Charlene

2008-06-01

Source water protection planning (SWPP) is an approach to prevent contamination of ground and surface water in watersheds where these resources may be abstracted for drinking or used for recreation. For SWPP the hazards within a watershed that could contribute to water contamination are identified together with the pathways that link them to the water resource. In rural areas, farms are significant potential sources of pathogens. A risk-based index can be used to support the assessment of the potential for contamination following guidelines on safety and operational efficacy of processes and practices developed as beneficial approaches to agricultural land management. Evaluation of the health risk for a target population requires knowledge of the strength of the hazard with respect to the pathogen load (massxconcentration). Manure handling and on-site wastewater treatment systems form the most important hazards, and both can comprise confined and unconfined source elements. There is also a need to understand the modification of pathogen numbers (attenuation) together with characteristics of the established pathways (surface or subsurface), which allow the movement of the contaminant species from a source to a receptor (water source). Many practices for manure management have not been fully evaluated for their impact on pathogen survival and transport in the environment. A key component is the identification of potential pathways of contaminant transport. This requires the development of a suitable digital elevation model of the watershed for surface movement and information on local groundwater aquifer systems for subsurface flows. Both require detailed soils and geological information. The pathways to surface and groundwater resources can then be identified. Details of land management, farm management practices (including animal and manure management) and agronomic practices have to be obtained, possibly from questionnaires completed by each producer within the watershed. To confirm that potential pathways are active requires some microbial source tracking. One possibility is to identify the molecular types of Escherichia coli present in each hazard on a farm. An essential part of any such index is the identification of mitigation strategies and practices that can reduce the magnitude of the hazard or block open pathways.

From data towards knowledge: revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data.

PubMed

Lu, Songjian; Jin, Bo; Cowart, L Ashley; Lu, Xinghua

2013-01-01

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal; and 3) revealing the architecture of a signaling system by organizing signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis has led to many new hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architecture of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which can be readily translated into computable knowledge in the form of rules regarding the yeast signaling system, such as "if genes involved in the MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed."

Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere1

PubMed Central

Hill, Kristine; Porco, Silvana; Lobet, Guillaume; Zappala, Susan; Mooney, Sacha; Draye, Xavier; Bennett, Malcolm J.

2013-01-01

Genetic and genomic approaches in model organisms have advanced our understanding of root biology over the last decade. Recently, however, systems biology and modeling have emerged as important approaches, as our understanding of root regulatory pathways has become more complex and interpreting pathway outputs has become less intuitive. To relate root genotype to phenotype, we must move beyond the examination of interactions at the genetic network scale and employ multiscale modeling approaches to predict emergent properties at the tissue, organ, organism, and rhizosphere scales. Understanding the underlying biological mechanisms and the complex interplay between systems at these different scales requires an integrative approach. Here, we describe examples of such approaches and discuss the merits of developing models to span multiple scales, from network to population levels, and to address dynamic interactions between plants and their environment. PMID:24143806

Using uncertainty to link and rank evidence from biomedical literature for model curation

PubMed Central

Zerva, Chrysoula; Batista-Navarro, Riza; Day, Philip; Ananiadou, Sophia

2017-01-01

Abstract Motivation In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models. Results We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research. Availability and implementation The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material. Contact sophia.ananiadou@manchester.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29036627

Using uncertainty to link and rank evidence from biomedical literature for model curation.

PubMed

Zerva, Chrysoula; Batista-Navarro, Riza; Day, Philip; Ananiadou, Sophia

2017-12-01

In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models. We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research. The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material. sophia.ananiadou@manchester.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

Candidate genes, pathways and mechanisms for alcoholism: an expanded convergent functional genomics approach.

PubMed

Rodd, Z A; Bertsch, B A; Strother, W N; Le-Niculescu, H; Balaraman, Y; Hayden, E; Jerome, R E; Lumeng, L; Nurnberger, J I; Edenberg, H J; McBride, W J; Niculescu, A B

2007-08-01

We describe a comprehensive translational approach for identifying candidate genes for alcoholism. The approach relies on the cross-matching of animal model brain gene expression data with human genetic linkage data, as well as human tissue data and biological roles data, an approach termed convergent functional genomics. An analysis of three animal model paradigms, based on inbred alcohol-preferring (iP) and alcohol-non-preferring (iNP) rats, and their response to treatments with alcohol, was used. A comprehensive analysis of microarray gene expression data from five key brain regions (frontal cortex, amygdala, caudate-putamen, nucleus accumbens and hippocampus) was carried out. The Bayesian-like integration of multiple independent lines of evidence, each by itself lacking sufficient discriminatory power, led to the identification of high probability candidate genes, pathways and mechanisms for alcoholism. These data reveal that alcohol has pleiotropic effects on multiple systems, which may explain the diverse neuropsychiatric and medical pathology in alcoholism. Some of the pathways identified suggest avenues for pharmacotherapy of alcoholism with existing agents, such as angiotensin-converting enzyme (ACE) inhibitors. Experiments we carried out in alcohol-preferring rats with an ACE inhibitor show a marked modulation of alcohol intake. Other pathways are new potential targets for drug development. The emergent overall picture is that physical and physiological robustness may permit alcohol-preferring individuals to withstand the aversive effects of alcohol. In conjunction with a higher reactivity to its rewarding effects, they may able to ingest enough of this nonspecific drug for a strong hedonic and addictive effect to occur.

A novel personal health system with integrated decision support and guidance for the management of chronic liver disease.

PubMed

Kiefer, Stephan; Schäfer, Michael; Bransch, Marco; Brimmers, Peter; Bartolomé, Diego; Baños, Janie; Orr, James; Jones, Dave; Jara, Maximilian; Stockmann, Martin

2014-01-01

A personal health system platform for the management of patients with chronic liver disease that incorporates a novel approach to integrate decision support and guidance through care pathways for patients and their doctors is presented in this paper. The personal health system incorporates an integrated decision support engine that guides patients and doctors through the management of the disease by issuing tasks and providing recommendations to both the care team and the patient and by controlling the execution of a Care Flow Plan based on the results of tasks and the monitored health status of the patient. This Care Flow Plan represents a formal, business process based model of disease management designed off-line by domain experts on the basis of clinical guidelines, knowledge of care pathways and an organisational model for integrated, patient-centred care. In this way, remote monitoring and treatment are dynamically adapted to the patient's actual condition and clinical symptoms and allow flexible delivery of care with close integration of specialists, therapists and care-givers.

SYSTEMS APPROACH TO CHARACTERIZING AND PREDICTING THYROID TOXICITY

EPA Science Inventory

A systems approach is being undertaken in which in vivo and in vitro assays are integrated to understand the mechanisms of thyroid hormone mediated pathways controlling frog metamorphosis, and more generally the regulation and control of the HPT axis.

A Transdiagnostic Minority Stress Treatment Approach for Gay and Bisexual Men’s Syndemic Health Conditions

PubMed Central

Pachankis, John E.

2015-01-01

Developing and deploying separate treatments for separate conditions seems ill-suited to intervening upon the co-occurring, and possibly functionally similar, psychosocial conditions facing gay and bisexual men. This article argues for the need to create transdiagnostic interventions that reduce multiple syndemic conditions facing gay and bisexual men at the level of their shared source in minority stress pathways. This article first reviews psychosocial syndemic conditions affecting gay and bisexual men, then suggests pathways that might link minority stress to psychosocial syndemics based on recent advancements in emotion science, psychiatric nosology, and cognitive-affective neuroscience, and finally suggests cross-cutting psychosocial treatment principles to reduce minority stress–syndemic pathways among gay and bisexual men. Because minority stress serves as a common basis of all psychosocial syndemic conditions reviewed here, locating the pathways through which minority stress generates psychosocial syndemics and employing overarching treatment principles capable of simultaneously alleviating these pathways will ultimately create a transdiagnostic approach to improving gay and bisexual men’s health. Clinical research and training approaches are suggested to further validate the pathways suggested here, establish the efficacy of treatment approaches tied to those pathways, and generate effective methods for disseminating a transdiagnostic minority stress treatment approach for gay and bisexual men’s psychosocial syndemic health. PMID:26123065

A Peptidomics Strategy to Elucidate the Proteolytic Pathways that Inactivate Peptide Hormones

PubMed Central

Tinoco, Arthur D.; Kim, Yun-Gon; Tagore, Debarati M.; Wiwczar, Jessica; Lane, William S.; Danial, Nika N.; Saghatelian, Alan

2011-01-01

Proteolysis plays a key role in regulating the levels and activity of peptide hormones. Characterization of the proteolytic pathways that cleave peptide hormones is of basic interest and can, in some cases, spur the development of novel therapeutics. The lack, however, of an efficient approach to identify endogenous fragments of peptide hormones has hindered the elucidation of these proteolytic pathways. Here, we apply a mass spectrometry (MS)-based peptidomics approach to characterize the intestinal fragments of peptide histidine isoleucine (PHI), a hormone that promotes glucose-stimulated insulin secretion (GSIS). Our approach reveals a proteolytic pathway in the intestine that truncates PHI at its C-terminus to produce a PHI fragment that is inactive in a GSIS assay—a result that provides a potential mechanism of PHI regulation in vivo. Differences between these in vivo peptidomics studies and in vitro lysate experiments, which showed N- and C-terminal processing of PHI, underscore the effectiveness of this approach to discover physiologically relevant proteolytic pathways. Moreover, integrating this peptidomics approach with bioassays (i.e. GSIS) provides a general strategy to reveal proteolytic pathways that may regulate the activity of peptide hormones. PMID:21299233

Rock climbing: A local-global algorithm to compute minimum energy and minimum free energy pathways.

PubMed

Templeton, Clark; Chen, Szu-Hua; Fathizadeh, Arman; Elber, Ron

2017-10-21

The calculation of minimum energy or minimum free energy paths is an important step in the quantitative and qualitative studies of chemical and physical processes. The computations of these coordinates present a significant challenge and have attracted considerable theoretical and computational interest. Here we present a new local-global approach to study reaction coordinates, based on a gradual optimization of an action. Like other global algorithms, it provides a path between known reactants and products, but it uses a local algorithm to extend the current path in small steps. The local-global approach does not require an initial guess to the path, a major challenge for global pathway finders. Finally, it provides an exact answer (the steepest descent path) at the end of the calculations. Numerical examples are provided for the Mueller potential and for a conformational transition in a solvated ring system.

Small-molecule inhibitors of DNA damage-repair pathways: an approach to overcome tumor resistance to alkylating anticancer drugs

PubMed Central

Srinivasan, Ajay; Gold, Barry

2013-01-01

A major challenge in the future development of cancer therapeutics is the identification of biological targets and pathways, and the subsequent design of molecules to combat the drug-resistant cells hiding in virtually all cancers. This therapeutic approach is justified based upon the limited advances in cancer cures over the past 30 years, despite the development of many novel chemotherapies and earlier detection, which often fail due to drug resistance. Among the various targets to overcome tumor resistance are the DNA repair systems that can reverse the cytotoxicity of many clinically used DNA-damaging agents. Some progress has already been made but much remains to be done. We explore some components of the DNA-repair process, which are involved in repair of alkylation damage of DNA, as targets for the development of novel and effective molecules designed to improve the efficacy of existing anticancer drugs. PMID:22709253

Interpreter of maladies: redescription mining applied to biomedical data analysis.

PubMed

Waltman, Peter; Pearlman, Alex; Mishra, Bud

2006-04-01

Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed. The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease. As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention's Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathways in the hypothalamic-pituitary-adrenal axis affect CFS patients.

Functional annotation of regulatory pathways.

PubMed

Pandey, Jayesh; Koyutürk, Mehmet; Kim, Yohan; Szpankowski, Wojciech; Subramaniam, Shankar; Grama, Ananth

2007-07-01

Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level. We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations. Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/.

Study to elucidate formation pathways of selected roast-smelling odorants upon extrusion cooking.

PubMed

Davidek, Tomas; Festring, Daniel; Dufossé, Thierry; Novotny, Ondrej; Blank, Imre

2013-10-30

The formation pathways of the N-containing roast-smelling compounds 2-acetyl-1-pyrroline, 2-acetyl-1(or 3),4,5,6-tetrahydropyridine, and their structural analogues 2-propionyl-1-pyrroline and 2-propionyl-1(or 3),4,5,6-tetrahydropyridine were studied upon extrusion cooking using the CAMOLA approach. The samples were produced under moderate extrusion conditions (135 °C, 20% moisture, 400 rpm) employing a rice-based model recipe enriched with flavor precursors ([U-(13)C6]-D-glucose, D-glucose, glycine, L-proline, and L-ornithine). The obtained data indicate that the formation of these compounds upon extrusion follows pathways similar to those reported for nonsheared model systems containing D-glucose and L-proline. 2-Acetyl-1-pyrroline is formed (i) by acylation of 1-pyrroline via C2 sugar fragments (major pathway) and (ii) via ring-opening of 1-pyrroline incorporating C3 sugar fragments (minor pathway), whereas 2-propionyl-1-pyrroline incorporates exclusively C3 sugar fragments. 2-Acetyl-1(or 3),4,5,6-tetrahydropyridine and the corresponding propionyl analogue incorporate C3 and C4 sugar fragments, respectively. In addition, it has been shown that the formation of 2-acetyl-1-pyrroline in low-moisture systems depends on the pH value of the reaction mixture.

Design of Adaptive Policy Pathways under Deep Uncertainties

NASA Astrophysics Data System (ADS)

Babovic, Vladan

2013-04-01

The design of large-scale engineering and infrastructural systems today is growing in complexity. Designers need to consider sociotechnical uncertainties, intricacies, and processes in the long- term strategic deployment and operations of these systems. In this context, water and spatial management is increasingly challenged not only by climate-associated changes such as sea level rise and increased spatio-temporal variability of precipitation, but also by pressures due to population growth and particularly accelerating rate of urbanisation. Furthermore, high investment costs and long term-nature of water-related infrastructure projects requires long-term planning perspective, sometimes extending over many decades. Adaptation to such changes is not only determined by what is known or anticipated at present, but also by what will be experienced and learned as the future unfolds, as well as by policy responses to social and water events. As a result, a pathway emerges. Instead of responding to 'surprises' and making decisions on ad hoc basis, exploring adaptation pathways into the future provide indispensable support in water management decision-making. In this contribution, a structured approach for designing a dynamic adaptive policy based on the concepts of adaptive policy making and adaptation pathways is introduced. Such an approach provides flexibility which allows change over time in response to how the future unfolds, what is learned about the system, and changes in societal preferences. The introduced flexibility provides means for dealing with complexities of adaptation under deep uncertainties. It enables engineering systems to change in the face of uncertainty to reduce impacts from downside scenarios while capitalizing on upside opportunities. This contribution presents comprehensive framework for development and deployment of adaptive policy pathway framework, and demonstrates its performance under deep uncertainties on a case study related to urban water catchment in Singapore. Ingredients of this approach are: (a) transient scenarios (time series of various uncertain developments such as climate change, economic developments, societal changes), (b) a methodology for exploring many options and sequences of these options across different futures, and (c) a stepwise policy analysis. The strategy is applied on case of flexible deployment of novel, so-called Next Generation Infrastructure, and assessed in context of the proposed. Results of the study show that flexible design alternatives deliver much enhanced performance compared to systems optimized under deterministic forecasts of the future. The work also demonstrates that explicit incorporation of uncertainty and flexibility into decision-making process reduces capital expenditures while allowing decision makers to learn about system evolution throughout the lifetime of the project.

A metal-organic tetrahedron as a redox vehicle to encapsulate organic dyes for photocatalytic proton reduction.

PubMed

Jing, Xu; He, Cheng; Yang, Yang; Duan, Chunying

2015-03-25

The design of artificial systems that mimic highly evolved and finely tuned natural photosynthetic systems is a subject of intensive research. We report herein a new approach to constructing supramolecular systems for the photocatalytic generation of hydrogen from water by encapsulating an organic dye molecule into the pocket of a redox-active metal-organic polyhedron. The assembled neutral Co4L4 tetrahedron consists of four ligands and four cobalt ions that connect together in alternating fashion. The cobalt ions are coordinated by three thiosemicarbazone NS chelators and exhibit a redox potential suitable for electrochemical proton reduction. The close proximity between the redox site and the photosensitizer encapsulated in the pocket enables photoinduced electron transfer from the excited state of the photosensitizer to the cobalt-based catalytic sites via a powerful pseudo-intramolecular pathway. The modified supramolecular system exhibits TON values comparable to the highest values reported for related cobalt/fluorescein systems. Control experiments based on a smaller tetrahedral analogue of the vehicle with a filled pocket and a mononuclear compound resembling the cobalt corner of the tetrahedron suggest an enzymatic dynamics behavior. The new, well-elucidated reaction pathways and the increased molarity of the reaction within the confined space render these supramolecular systems superior to other relevant systems.

Structured plant metabolomics for the simultaneous exploration of multiple factors.

PubMed

Vasilev, Nikolay; Boccard, Julien; Lang, Gerhard; Grömping, Ulrike; Fischer, Rainer; Goepfert, Simon; Rudaz, Serge; Schillberg, Stefan

2016-11-17

Multiple factors act simultaneously on plants to establish complex interaction networks involving nutrients, elicitors and metabolites. Metabolomics offers a better understanding of complex biological systems, but evaluating the simultaneous impact of different parameters on metabolic pathways that have many components is a challenging task. We therefore developed a novel approach that combines experimental design, untargeted metabolic profiling based on multiple chromatography systems and ionization modes, and multiblock data analysis, facilitating the systematic analysis of metabolic changes in plants caused by different factors acting at the same time. Using this method, target geraniol compounds produced in transgenic tobacco cell cultures were grouped into clusters based on their response to different factors. We hypothesized that our novel approach may provide more robust data for process optimization in plant cell cultures producing any target secondary metabolite, based on the simultaneous exploration of multiple factors rather than varying one factor each time. The suitability of our approach was verified by confirming several previously reported examples of elicitor-metabolite crosstalk. However, unravelling all factor-metabolite networks remains challenging because it requires the identification of all biochemically significant metabolites in the metabolomics dataset.

Incorporating zebrafish omics into chemical biology and toxicology.

PubMed

Sukardi, Hendrian; Ung, Choong Yong; Gong, Zhiyuan; Lam, Siew Hong

2010-03-01

In this communication, we describe the general aspects of omics approaches for analyses of transcriptome, proteome, and metabolome, and how they can be strategically incorporated into chemical screening and perturbation studies using the zebrafish system. Pharmacological efficacy and selectivity of chemicals can be evaluated based on chemical-induced phenotypic effects; however, phenotypic observation has limitations in identifying mechanistic action of chemicals. We suggest adapting gene-expression-based high-throughput screening as a complementary strategy to zebrafish-phenotype-based screening for mechanistic insights about the mode of action and toxicity of a chemical, large-scale predictive applications and comparative analysis of chemical-induced omics signatures, which are useful to identify conserved biological responses, signaling pathways, and biomarkers. The potential mechanistic, predictive, and comparative applications of omics approaches can be implemented in the zebrafish system. Examples of these using the omics approaches in zebrafish, including data of ours and others, are presented and discussed. Omics also facilitates the translatability of zebrafish studies across species through comparison of conserved chemical-induced responses. This review is intended to update interested readers with the current omics approaches that have been applied in chemical studies on zebrafish and their potential in enhancing discovery in chemical biology.

Engineering of protein folding and secretion-strategies to overcome bottlenecks for efficient production of recombinant proteins.

PubMed

Delic, Marizela; Göngrich, Rebecca; Mattanovich, Diethard; Gasser, Brigitte

2014-07-20

Recombinant protein production has developed into a huge market with enormous positive implications for human health and for the future direction of a biobased economy. Limitations in the economic and technical feasibility of production processes are often related to bottlenecks of in vivo protein folding. Based on cell biological knowledge, some major bottlenecks have been overcome by the overexpression of molecular chaperones and other folding related proteins, or by the deletion of deleterious pathways that may lead to misfolding, mistargeting, or degradation. While important success could be achieved by this strategy, the list of reported unsuccessful cases is disappointingly long and obviously dependent on the recombinant protein to be produced. Singular engineering of protein folding steps may not lead to desired results if the pathway suffers from several limitations. In particular, the connection between folding quality control and proteolytic degradation needs further attention. Based on recent understanding that multiple steps in the folding and secretion pathways limit productivity, synergistic combinations of the cell engineering approaches mentioned earlier need to be explored. In addition, systems biology-based whole cell analysis that also takes energy and redox metabolism into consideration will broaden the knowledge base for future rational engineering strategies.

Estimating pathway-specific contributions to biodegradation in aquifers based on dual isotope analysis: theoretical analysis and reactive transport simulations.

PubMed

Centler, Florian; Heße, Falk; Thullner, Martin

2013-09-01

At field sites with varying redox conditions, different redox-specific microbial degradation pathways contribute to total contaminant degradation. The identification of pathway-specific contributions to total contaminant removal is of high practical relevance, yet difficult to achieve with current methods. Current stable-isotope-fractionation-based techniques focus on the identification of dominant biodegradation pathways under constant environmental conditions. We present an approach based on dual stable isotope data to estimate the individual contributions of two redox-specific pathways. We apply this approach to carbon and hydrogen isotope data obtained from reactive transport simulations of an organic contaminant plume in a two-dimensional aquifer cross section to test the applicability of the method. To take aspects typically encountered at field sites into account, additional simulations addressed the effects of transverse mixing, diffusion-induced stable-isotope fractionation, heterogeneities in the flow field, and mixing in sampling wells on isotope-based estimates for aerobic and anaerobic pathway contributions to total contaminant biodegradation. Results confirm the general applicability of the presented estimation method which is most accurate along the plume core and less accurate towards the fringe where flow paths receive contaminant mass and associated isotope signatures from the core by transverse dispersion. The presented method complements the stable-isotope-fractionation-based analysis toolbox. At field sites with varying redox conditions, it provides a means to identify the relative importance of individual, redox-specific degradation pathways. © 2013.

LDRD Final Report for''Tactical Laser Weapons for Defense'' SI (Tracking Code 01-SI-011)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beach, R; Zapata, L

The focus of this project was a convincing demonstration of two new technological approaches to high beam quality; high average power solid-state laser systems that would be of interest for tactical laser weapon applications. Two pathways had been identified to such systems that built on existing thin disk and fiber laser technologies. This SI was used as seed funding to further develop and vet these ideas. Significantly, the LLNL specific enhancements to these proposed technology paths were specifically addressed for devising systems scaleable to the 100 kW average power level. In the course of performing this work we have establishedmore » an intellectual property base that protects and distinguishes us from other competitive approaches to the same end.« less

Complement in autoimmune diseases.

PubMed

Vignesh, Pandiarajan; Rawat, Amit; Sharma, Madhubala; Singh, Surjit

2017-02-01

The complement system is an ancient and evolutionary conserved element of the innate immune mechanism. It comprises of more than 20 serum proteins most of which are synthesized in the liver. These proteins are synthesized as inactive precursor proteins which are activated by appropriate stimuli. The activated forms of these proteins act as proteases and cleave other components successively in amplification pathways leading to exponential generation of final effectors. Three major pathways of complement pathways have been described, namely the classical, alternative and lectin pathways which are activated by different stimuli. However, all the 3 pathways converge on Complement C3. Cleavage of C3 and C5 successively leads to the production of the membrane attack complex which is final common effector. Excessive and uncontrolled activation of the complement has been implicated in the host of autoimmune diseases. But the complement has also been bemusedly described as the proverbial "double edged sword". On one hand, complement is the final effector of tissue injury in autoimmune diseases and on the other, deficiencies of some components of the complement can result in autoimmune diseases. Currently available tools such as enzyme based immunoassays for functional assessment of complement pathways, flow cytometry, next generation sequencing and proteomics-based approaches provide an exciting opportunity to study this ancient yet mysterious element of innate immunity. Copyright © 2017 Elsevier B.V. All rights reserved.

Simultaneous In Vitro Characterisation of DNA Deaminase Function and Associated DNA Repair Pathways

PubMed Central

Franchini, Don-Marc; Incorvaia, Elisabetta; Rangam, Gopinath; Coker, Heather A.; Petersen-Mahrt, Svend K.

2013-01-01

During immunoglobulin (Ig) diversification, activation-induced deaminase (AID) initiates somatic hypermutation and class switch recombination by catalysing the conversion of cytosine to uracil. The synergy between AID and DNA repair pathways is fundamental for the introduction of mutations, however the molecular and biochemical mechanisms underlying this process are not fully elucidated. We describe a novel method to efficiently decipher the composition and activity of DNA repair pathways that are activated by AID-induced lesions. The in vitro resolution (IVR) assay combines AID based deamination and DNA repair activities from a cellular milieu in a single assay, thus avoiding synthetically created DNA-lesions or genetic-based readouts. Recombinant GAL4-AID fusion protein is targeted to a plasmid containing GAL4 binding sites, allowing for controlled cytosine deamination within a substrate plasmid. Subsequently, the Xenopus laevis egg extract provides a source of DNA repair proteins and functional repair pathways. Our results demonstrated that DNA repair pathways which are in vitro activated by AID-induced lesions are reminiscent of those found during AID-induced in vivo Ig diversification. The comparative ease of manipulation of this in vitro systems provides a new approach to dissect the complex DNA repair pathways acting on defined physiologically lesions, can be adapted to use with other DNA damaging proteins (e.g. APOBECs), and provide a means to develop and characterise pharmacological agents to inhibit these potentially oncogenic processes. PMID:24349193

Expression analysis in response to drought stress in soybean: Shedding light on the regulation of metabolic pathway genes.

PubMed

Guimarães-Dias, Fábia; Neves-Borges, Anna Cristina; Viana, Antonio Americo Barbosa; Mesquita, Rosilene Oliveira; Romano, Eduardo; de Fátima Grossi-de-Sá, Maria; Nepomuceno, Alexandre Lima; Loureiro, Marcelo Ehlers; Alves-Ferreira, Márcio

2012-06-01

Metabolomics analysis of wild type Arabidopsis thaliana plants, under control and drought stress conditions revealed several metabolic pathways that are induced under water deficit. The metabolic response to drought stress is also associated with ABA dependent and independent pathways, allowing a better understanding of the molecular mechanisms in this model plant. Through combining an in silico approach and gene expression analysis by quantitative real-time PCR, the present work aims at identifying genes of soybean metabolic pathways potentially associated with water deficit. Digital expression patterns of Arabidopsis genes, which were selected based on the basis of literature reports, were evaluated under drought stress condition by Genevestigator. Genes that showed strong induction under drought stress were selected and used as bait to identify orthologs in the soybean genome. This allowed us to select 354 genes of putative soybean orthologs of 79 Arabidopsis genes belonging to 38 distinct metabolic pathways. The expression pattern of the selected genes was verified in the subtractive libraries available in the GENOSOJA project. Subsequently, 13 genes from different metabolic pathways were selected for validation by qPCR experiments. The expression of six genes was validated in plants undergoing drought stress in both pot-based and hydroponic cultivation systems. The results suggest that the metabolic response to drought stress is conserved in Arabidopsis and soybean plants.

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity.

PubMed

Kogelman, Lisette J A; Zhernakova, Daria V; Westra, Harm-Jan; Cirera, Susanna; Fredholm, Merete; Franke, Lude; Kadarmideen, Haja N

2015-10-20

Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data. Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B. To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways

NASA Astrophysics Data System (ADS)

Yang, Yunlai; Arouri, Khaled

2016-03-01

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations.

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways

PubMed Central

Yang, Yunlai; Arouri, Khaled

2016-01-01

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations. PMID:26965479

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways.

PubMed

Yang, Yunlai; Arouri, Khaled

2016-03-11

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations.

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis.

PubMed

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-11-07

To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s).

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis

PubMed Central

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-01-01

Background To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. Results We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. Conclusion PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s). PMID:14604444

Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis.

PubMed

Kammen, Alexandra; Law, Meng; Tjan, Bosco S; Toga, Arthur W; Shi, Yonggang

2016-01-15

Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer's loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

­Understanding Information Flow Interaction along Separable Causal Paths in Environmental Signals

NASA Astrophysics Data System (ADS)

Jiang, P.; Kumar, P.

2017-12-01

Multivariate environmental signals reflect the outcome of complex inter-dependencies, such as those in ecohydrologic systems. Transfer entropy and information partitioning approaches have been used to characterize such dependencies. However, these approaches capture net information flow occurring through a multitude of pathways involved in the interaction and as a result mask our ability to discern the causal interaction within an interested subsystem through specific pathways. We build on recent developments of momentary information transfer along causal paths proposed by Runge [2015] to develop a framework for quantifying information decomposition along separable causal paths. Momentary information transfer along causal paths captures the amount of information flow between any two variables lagged at two specific points in time. Our approach expands this concept to characterize the causal interaction in terms of synergistic, unique and redundant information flow through separable causal paths. Multivariate analysis using this novel approach reveals precise understanding of causality and feedback. We illustrate our approach with synthetic and observed time series data. We believe the proposed framework helps better delineate the internal structure of complex systems in geoscience where huge amounts of observational datasets exist, and it will also help the modeling community by providing a new way to look at the complexity of real and modeled systems. Runge, Jakob. "Quantifying information transfer and mediation along causal pathways in complex systems." Physical Review E 92.6 (2015): 062829.

Current limitations and recommendations to improve testing ...

EPA Pesticide Factsheets

In this paper existing regulatory frameworks and test systems for assessing potential endocrine-active chemicals are described, and associated challenges discussed, along with proposed approaches to address these challenges. Regulatory frameworks vary somewhat across organizations, but all basically evaluate whether a chemical possesses endocrine activity and whether this activity can result in adverse outcomes either to humans or the environment. Current test systems include in silico, in vitro and in vivo techniques focused on detecting potential endocrine activity, and in vivo tests that collect apical data to detect possible adverse effects. These test systems are currently designed to robustly assess endocrine activity and/or adverse effects in the estrogen, androgen, and thyroid hormonal pathways; however, there are some limitations of current test systems for evaluating endocrine hazard and risk. These limitations include a lack of certainty regarding: 1)adequately sensitive species and life-stages, 2) mechanistic endpoints that are diagnostic for endocrine pathways of concern, and 3) the linkage between mechanistic responses and apical, adverse outcomes. Furthermore, some existing test methods are resource intensive in regard to time, cost, and use of animals. However, based on recent experiences, there are opportunities to improve approaches to, and guidance for existing test methods, and to reduce uncertainty. For example, in vitro high throughput

Integration of Genome-Scale Modeling and Transcript Profiling Reveals Metabolic Pathways Underlying Light and Temperature Acclimation in Arabidopsis[C][W

PubMed Central

Töpfer, Nadine; Caldana, Camila; Grimbs, Sergio; Willmitzer, Lothar; Fernie, Alisdair R.; Nikoloski, Zoran

2013-01-01

Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism. PMID:23613196

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

Assessment of air quality and climate co-benefits of decarbonisation of the UK energy system using remote sensing and model simulations - the case for prioritizing end uses in urban areas

NASA Astrophysics Data System (ADS)

Sobral Mourao, Zenaida; Konadu, Daniel Dennis; Damoah, Richard; Li, Pei-hao

2017-04-01

The UK has a binding obligation to reduce GHG emission by 80% (based on 1990 levels) by 2050. Meeting this target requires extensive decarbonisation of the UK energy system. Different pathways that achieve this target at the lowest system costs are being explored at different levels of policy and decisions on future energy infrastructure. Whilst benefits of decarbonisation are mainly focused on the impacts on climate change, there are other potential environmental and health impacts such as air-quality. In particular, a decrease in fossil fuel use by directly substituting current systems with low-carbon technologies could lead to significant reductions in the concentrations of SO2, NOX, CO and other atmospheric pollutants. So far, the proposed decarbonisation pathways tend to target the electricity sector first, followed by a transition in transport and heating technologies and use. However, the spatial dimension of where short term changes in the energy sector occur in relation to high density population areas is not taken into account when defining the energy transition strategies. This may lead to limited short-term improvements in air quality within urban areas, where use of fossil fuels for heating and transport is the main contribution to overall atmospheric pollutant levels. It is therefore imperative to explore decarbonisation strategies that prioritise transition in sectors of the energy system that produce immediate improvements in air quality in key regions of the UK. This study aims to use a combination of Remote Sensing observations and atmospheric chemistry/transport modelling approaches to estimate and map the impact on NOx of the traditional approach of decarbonising electricity first compared to a slower transition in the electricity sector, but faster change in the transport sector. This is done by generating a set of alternative energy system pathways with a higher share of zero emissions vehicles in 2030 than the energy system optimization model would choose if the only goal was the 80% GHG emissions reduction. Our overarching goal is to provide an additional standard to compare future energy system pathways beyond the traditional metrics of cost and GHG emissions reductions.

Improving Hospital-wide Patient Scheduling Decisions by Clinical Pathway Mining.

PubMed

Gartner, Daniel; Arnolds, Ines V; Nickel, Stefan

2015-01-01

Recent research has highlighted the need for solving hospital-wide patient scheduling problems. Inpatient scheduling, patient activities have to be scheduled on scarce hospital resources such that temporal relations between activities (e.g. for recovery times) are ensured. Common objectives are, among others, the minimization of the length of stay (LOS). In this paper, we consider a hospital-wide patient scheduling problem with LOS minimization based on uncertain clinical pathways. We approach the problem in three stages: First, we learn most likely clinical pathways using a sequential pattern mining approach. Second, we provide a mathematical model for patient scheduling and finally, we combine the two approaches. In an experimental study carried out using real-world data, we show that our approach outperforms baseline approaches on two metrics.

13C-based metabolic flux analysis: fundamentals and practice.

PubMed

Yang, Tae Hoon

2013-01-01

Isotope-based metabolic flux analysis is one of the emerging technologies applied to system level metabolic phenotype characterization in metabolic engineering. Among the developed approaches, (13)C-based metabolic flux analysis has been established as a standard tool and has been widely applied to quantitative pathway characterization of diverse biological systems. To implement (13)C-based metabolic flux analysis in practice, comprehending the underlying mathematical and computational modeling fundamentals is of importance along with carefully conducted experiments and analytical measurements. Such knowledge is also crucial when designing (13)C-labeling experiments and properly acquiring key data sets essential for in vivo flux analysis implementation. In this regard, the modeling fundamentals of (13)C-labeling systems and analytical data processing are the main topics we will deal with in this chapter. Along with this, the relevant numerical optimization techniques are addressed to help implementation of the entire computational procedures aiming at (13)C-based metabolic flux analysis in vivo.

Topological analysis of metabolic control.

PubMed

Sen, A K

1990-12-01

A topological approach is presented for the analysis of control and regulation in metabolic pathways. In this approach, the control structure of a metabolic pathway is represented by a weighted directed graph. From an inspection of the topology of the graph, the control coefficients of the enzymes are evaluated in a heuristic manner in terms of the enzyme elasticities. The major advantage of the topological approach is that it provides a visual framework for (1) calculating the control coefficients of the enzymes, (2) analyzing the cause-effect relationships of the individual enzymes, (3) assessing the relative importance of the enzymes in metabolic regulation, and (4) simplifying the structure of a given pathway, from a regulatory viewpoint. Results are obtained for (a) an unbranched pathway in the absence of feedback the feedforward regulation and (b) an unbranched pathway with feedback inhibition. Our formulation is based on the metabolic control theory of Kacser and Burns (1973) and Heinrich and Rapoport (1974).

Impact of Next Generation District Heating Systems on Distribution Network Heat Losses: A Case Study Approach

NASA Astrophysics Data System (ADS)

Li, Yu; Rezgui, Yacine

2018-01-01

District heating (DH) is a promising energy pathway to alleviate environmental negative impacts induced by fossil fuels. Improving the performance of DH systems is one of the major challenges facing its wide adoption. This paper discusses the heat losses of the next generation DH based on the constructed Simulink model. Results show that lower distribution temperature and advanced insulation technology greatly reduce network heat losses. Also, the network heat loss can be further minimized by a reduction of heat demand in buildings.

Characterizability of metabolic pathway systems from time series data.

PubMed

Voit, Eberhard O

2013-12-01

Over the past decade, the biomathematical community has devoted substantial effort to the complicated challenge of estimating parameter values for biological systems models. An even more difficult issue is the characterization of functional forms for the processes that govern these systems. Most parameter estimation approaches tacitly assume that these forms are known or can be assumed with some validity. However, this assumption is not always true. The recently proposed method of Dynamic Flux Estimation (DFE) addresses this problem in a genuinely novel fashion for metabolic pathway systems. Specifically, DFE allows the characterization of fluxes within such systems through an analysis of metabolic time series data. Its main drawback is the fact that DFE can only directly be applied if the pathway system contains as many metabolites as unknown fluxes. This situation is unfortunately rare. To overcome this roadblock, earlier work in this field had proposed strategies for augmenting the set of unknown fluxes with independent kinetic information, which however is not always available. Employing Moore-Penrose pseudo-inverse methods of linear algebra, the present article discusses an approach for characterizing fluxes from metabolic time series data that is applicable even if the pathway system is underdetermined and contains more fluxes than metabolites. Intriguingly, this approach is independent of a specific modeling framework and unaffected by noise in the experimental time series data. The results reveal whether any fluxes may be characterized and, if so, which subset is characterizable. They also help with the identification of fluxes that, if they could be determined independently, would allow the application of DFE. Copyright © 2013 Elsevier Inc. All rights reserved.

Characterizability of Metabolic Pathway Systems from Time Series Data

PubMed Central

Voit, Eberhard O.

2013-01-01

Over the past decade, the biomathematical community has devoted substantial effort to the complicated challenge of estimating parameter values for biological systems models. An even more difficult issue is the characterization of functional forms for the processes that govern these systems. Most parameter estimation approaches tacitly assume that these forms are known or can be assumed with some validity. However, this assumption is not always true. The recently proposed method of Dynamic Flux Estimation (DFE) addresses this problem in a genuinely novel fashion for metabolic pathway systems. Specifically, DFE allows the characterization of fluxes within such systems through an analysis of metabolic time series data. Its main drawback is the fact that DFE can only directly be applied if the pathway system contains as many metabolites as unknown fluxes. This situation is unfortunately rare. To overcome this roadblock, earlier work in this field had proposed strategies for augmenting the set of unknown fluxes with independent kinetic information, which however is not always available. Employing Moore-Penrose pseudo-inverse methods of linear algebra, the present article discusses an approach for characterizing fluxes from metabolic time series data that is applicable even if the pathway system is underdetermined and contains more fluxes than metabolites. Intriguingly, this approach is independent of a specific modeling framework and unaffected by noise in the experimental time series data. The results reveal whether any fluxes may be characterized and, if so, which subset is characterizable. They also help with the identification of fluxes that, if they could be determined independently, would allow the application of DFE. PMID:23391489

Agent-Based Modeling in Molecular Systems Biology.

PubMed

Soheilypour, Mohammad; Mofrad, Mohammad R K

2018-07-01

Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease. © 2018 WILEY Periodicals, Inc.

The Need for Integrated Approaches in Metabolic Engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lechner, Anna; Brunk, Elizabeth; Keasling, Jay D.

This review highlights state-of-the-art procedures for heterologous small-molecule biosynthesis, the associated bottlenecks, and new strategies that have the potential to accelerate future accomplishments in metabolic engineering. We emphasize that a combination of different approaches over multiple time and size scales must b e considered for successful pathway engineering in a heterologous host. We have classified these optimization procedures based on the "system" that is being manipulated: transcriptome, translatome, proteome, or reactome. By bridging multiple disciplines, including molecular biology, biochemistry, biophysics, and computational sciences, we can create an integral framework for the discovery and implementation of novel biosynthetic production routes.

A Systems Biology View of Responses to Lignin Biosynthesis Perturbations in Arabidopsis[W

PubMed Central

Vanholme, Ruben; Storme, Véronique; Vanholme, Bartel; Sundin, Lisa; Christensen, Jørgen Holst; Goeminne, Geert; Halpin, Claire; Rohde, Antje; Morreel, Kris; Boerjan, Wout

2012-01-01

Lignin engineering is an attractive strategy to improve lignocellulosic biomass quality for processing to biofuels and other bio-based products. However, lignin engineering also results in profound metabolic consequences in the plant. We used a systems biology approach to study the plant’s response to lignin perturbations. To this end, inflorescence stems of 20 Arabidopsis thaliana mutants, each mutated in a single gene of the lignin biosynthetic pathway (phenylalanine ammonia-lyase1 [PAL1], PAL2, cinnamate 4-hydroxylase [C4H], 4-coumarate:CoA ligase1 [4CL1], 4CL2, caffeoyl-CoA O-methyltransferase1 [CCoAOMT1], cinnamoyl-CoA reductase1 [CCR1], ferulate 5-hydroxylase [F5H1], caffeic acid O-methyltransferase [COMT], and cinnamyl alcohol dehydrogenase6 [CAD6], two mutant alleles each), were analyzed by transcriptomics and metabolomics. A total of 566 compounds were detected, of which 187 could be tentatively identified based on mass spectrometry fragmentation and many were new for Arabidopsis. Up to 675 genes were differentially expressed in mutants that did not have any obvious visible phenotypes. Comparing the responses of all mutants indicated that c4h, 4cl1, ccoaomt1, and ccr1, mutants that produced less lignin, upregulated the shikimate, methyl-donor, and phenylpropanoid pathways (i.e., the pathways supplying the monolignols). By contrast, f5h1 and comt, mutants that provoked lignin compositional shifts, downregulated the very same pathways. Reductions in the flux to lignin were associated with the accumulation of various classes of 4-O- and 9-O-hexosylated phenylpropanoids. By combining metabolomic and transcriptomic data in a correlation network, system-wide consequences of the perturbations were revealed and genes with a putative role in phenolic metabolism were identified. Together, our data provide insight into lignin biosynthesis and the metabolic network it is embedded in and provide a systems view of the plant’s response to pathway perturbations. PMID:23012438

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow

DOE PAGES

Brunk, Elizabeth; George, Kevin W.; Alonso-Gutierrez, Jorge; ...

2016-05-19

Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proofmore » of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks.« less

The Global Food System as a Transport Pathway for Hazardous Chemicals: The Missing Link between Emissions and Exposure.

PubMed

Ng, Carla A; von Goetz, Natalie

2017-01-01

Food is a major pathway for human exposure to hazardous chemicals. The modern food system is becoming increasingly complex and globalized, but models for food-borne exposure typically assume locally derived diets or use concentrations directly measured in foods without accounting for food origin. Such approaches may not reflect actual chemical intakes because concentrations depend on food origin, and representative analysis is seldom available. Processing, packaging, storage, and transportation also impart different chemicals to food and are not yet adequately addressed. Thus, the link between environmental emissions and realistic human exposure is effectively broken. We discuss the need for a fully integrated treatment of the modern industrialized food system, and we propose strategies for using existing models and relevant supporting data sources to track chemicals during production, processing, packaging, storage, and transport. Fate and bioaccumulation models describe how chemicals distribute in the environment and accumulate through local food webs. Human exposure models can use concentrations in food to determine body burdens based on individual or population characteristics. New models now include the impacts of processing and packaging but are far from comprehensive. We propose to close the gap between emissions and exposure by utilizing a wider variety of models and data sources, including global food trade data, processing, and packaging models. A comprehensive approach that takes into account the complexity of the modern global food system is essential to enable better prediction of human exposure to chemicals in food, sound risk assessments, and more focused risk abatement strategies. Citation: Ng CA, von Goetz N. 2017. The global food system as a transport pathway for hazardous chemicals: the missing link between emissions and exposure. Environ Health Perspect 125:1-7; http://dx.doi.org/10.1289/EHP168.

Manipulation of the carbon storage regulator system for metabolite remodeling and biofuel production in Escherichia coli

PubMed Central

2012-01-01

Background Microbial engineering strategies that elicit global metabolic perturbations have the capacity to increase organism robustness for targeted metabolite production. In particular, perturbations to regulators of cellular systems that impact glycolysis and amino acid production while simultaneously decreasing fermentation by-products such as acetate and CO2 make ideal targets. Intriguingly, perturbation of the Carbon Storage Regulator (Csr) system has been previously implicated in large changes in central carbon metabolism in E. coli. Therefore, we hypothesized that perturbation of the Csr system through the CsrA-CsrB ribonucleoprotein complex might increase production of biofuels and their intermediates from heterologous pathways. Results We engaged the CsrA-CsrB ribonucleoprotein complex of E. coli via overexpression of CsrB. CsrB is a 350-nucleotide non-coding RNA that antagonizes CsrA, an RNA-binding protein that regulates translation of specific mRNA targets. By using shotgun proteomics and targeted metabolomics we established that elevation of CsrB levels leads to alterations in metabolite and protein levels in glycolysis, the TCA cycle and amino acid levels. Consequently, we show that such changes can be suitably applied to improve the production of desired compounds through the native fatty acid and heterologous n-butanol and isoprenoid pathways by up to two-fold. We also observed concomitant decreases in undesirable fermentation by-products such as acetate and CO2. Conclusions We have demonstrated that simple engineering of the RNA-based Csr global regulatory system constitutes a novel approach to obtaining pathway-independent improvements within engineered hosts. Additionally, since Csr is conserved across most prokaryotic species, this approach may also be amenable to a wide variety of production hosts. PMID:22694848

Manipulation of the carbon storage regulator system for metabolite remodeling and biofuel production in Escherichia coli.

PubMed

McKee, Adrienne E; Rutherford, Becky J; Chivian, Dylan C; Baidoo, Edward K; Juminaga, Darmawi; Kuo, Dwight; Benke, Peter I; Dietrich, Jeffrey A; Ma, Suzanne M; Arkin, Adam P; Petzold, Christopher J; Adams, Paul D; Keasling, Jay D; Chhabra, Swapnil R

2012-06-13

Microbial engineering strategies that elicit global metabolic perturbations have the capacity to increase organism robustness for targeted metabolite production. In particular, perturbations to regulators of cellular systems that impact glycolysis and amino acid production while simultaneously decreasing fermentation by-products such as acetate and CO(2) make ideal targets. Intriguingly, perturbation of the Carbon Storage Regulator (Csr) system has been previously implicated in large changes in central carbon metabolism in E. coli. Therefore, we hypothesized that perturbation of the Csr system through the CsrA-CsrB ribonucleoprotein complex might increase production of biofuels and their intermediates from heterologous pathways. We engaged the CsrA-CsrB ribonucleoprotein complex of E. coli via overexpression of CsrB. CsrB is a 350-nucleotide non-coding RNA that antagonizes CsrA, an RNA-binding protein that regulates translation of specific mRNA targets. By using shotgun proteomics and targeted metabolomics we established that elevation of CsrB levels leads to alterations in metabolite and protein levels in glycolysis, the TCA cycle and amino acid levels. Consequently, we show that such changes can be suitably applied to improve the production of desired compounds through the native fatty acid and heterologous n-butanol and isoprenoid pathways by up to two-fold. We also observed concomitant decreases in undesirable fermentation by-products such as acetate and CO(2). We have demonstrated that simple engineering of the RNA-based Csr global regulatory system constitutes a novel approach to obtaining pathway-independent improvements within engineered hosts. Additionally, since Csr is conserved across most prokaryotic species, this approach may also be amenable to a wide variety of production hosts.

Accelerating Adverse Outcome Pathway Development via Systems Approaches

EPA Science Inventory

The Adverse Outcome Pathway has emerged as an internationally harmonized mechanism for organizing biological information in a chemical agnostic manner. This construct is valuable for interpreting the results from high-throughput toxicity (HTT) assessment by providing a mechanisti...

Systems Toxicology: Real World Applications and Opportunities.

PubMed

Hartung, Thomas; FitzGerald, Rex E; Jennings, Paul; Mirams, Gary R; Peitsch, Manuel C; Rostami-Hodjegan, Amin; Shah, Imran; Wilks, Martin F; Sturla, Shana J

2017-04-17

Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized from empirical end points to describing modes of action as adverse outcome pathways and perturbed networks. Toward this aim, Systems Toxicology entails the integration of in vitro and in vivo toxicity data with computational modeling. This evolving approach depends critically on data reliability and relevance, which in turn depends on the quality of experimental models and bioanalysis techniques used to generate toxicological data. Systems Toxicology involves the use of large-scale data streams ("big data"), such as those derived from omics measurements that require computational means for obtaining informative results. Thus, integrative analysis of multiple molecular measurements, particularly acquired by omics strategies, is a key approach in Systems Toxicology. In recent years, there have been significant advances centered on in vitro test systems and bioanalytical strategies, yet a frontier challenge concerns linking observed network perturbations to phenotypes, which will require understanding pathways and networks that give rise to adverse responses. This summary perspective from a 2016 Systems Toxicology meeting, an international conference held in the Alps of Switzerland, describes the limitations and opportunities of selected emerging applications in this rapidly advancing field. Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized, from empirical end points to pathways of toxicity. This requires the integration of in vitro and in vivo data with computational modeling. Test systems and bioanalytical technologies have made significant advances, but ensuring data reliability and relevance is an ongoing concern. The major challenge facing the new pathway approach is determining how to link observed network perturbations to phenotypic toxicity.

Systems Toxicology: Real World Applications and Opportunities

PubMed Central

2017-01-01

Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized from empirical end points to describing modes of action as adverse outcome pathways and perturbed networks. Toward this aim, Systems Toxicology entails the integration of in vitro and in vivo toxicity data with computational modeling. This evolving approach depends critically on data reliability and relevance, which in turn depends on the quality of experimental models and bioanalysis techniques used to generate toxicological data. Systems Toxicology involves the use of large-scale data streams (“big data”), such as those derived from omics measurements that require computational means for obtaining informative results. Thus, integrative analysis of multiple molecular measurements, particularly acquired by omics strategies, is a key approach in Systems Toxicology. In recent years, there have been significant advances centered on in vitro test systems and bioanalytical strategies, yet a frontier challenge concerns linking observed network perturbations to phenotypes, which will require understanding pathways and networks that give rise to adverse responses. This summary perspective from a 2016 Systems Toxicology meeting, an international conference held in the Alps of Switzerland, describes the limitations and opportunities of selected emerging applications in this rapidly advancing field. Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized, from empirical end points to pathways of toxicity. This requires the integration of in vitro and in vivo data with computational modeling. Test systems and bioanalytical technologies have made significant advances, but ensuring data reliability and relevance is an ongoing concern. The major challenge facing the new pathway approach is determining how to link observed network perturbations to phenotypic toxicity. PMID:28362102

Developing care pathways--lessons from the Steele Review implementation in England.

PubMed

Rooney, Eric

2014-02-01

This paper sets out to discuss the concept of care pathways, review their definition, features and implementation and using an example from the NHS dental system in England guide the development of an elder care pathway. Care pathways have developed from quality management approaches in industry and focus on a number of steps which are intended to lead to expected outcomes. The existing definition and descriptors of care pathways serve well, but miss the complex process underlying the development of pathways, their structure, implementation and evaluation. The literature identifies key features of clinical pathways and from the developing field of implementation science, the factors likely to support pathway implementation. Pathways must be generic enough to enable them to be applicable broadly, but specific enough for them to be locally relevant and population specific. The development of care pathways in the National Health Service (NHS) Dental Service in England is described and when compared with the implementation science literature exhibits features identified as positive factors for implementation. As a result a contribution to the pathway definition literature is offered. Learning from the literature and the practical experience described from England, the process for developing dental care pathways for dependent elders should begin with the creation of a high level pathway, which is cognisant of the clinical and implementation science evidence base. © 2014 John Wiley & Sons A/S and The Gerodontology Society. Published by John Wiley & Sons Ltd.

MRI-based dynamic tracking of an untethered ferromagnetic microcapsule navigating in liquid

NASA Astrophysics Data System (ADS)

Dahmen, Christian; Belharet, Karim; Folio, David; Ferreira, Antoine; Fatikow, Sergej

2016-04-01

The propulsion of ferromagnetic objects by means of MRI gradients is a promising approach to enable new forms of therapy. In this work, necessary techniques are presented to make this approach work. This includes path planning algorithms working on MRI data, ferromagnetic artifact imaging and a tracking algorithm which delivers position feedback for the ferromagnetic objects, and a propulsion sequence to enable interleaved magnetic propulsion and imaging. Using a dedicated software environment, integrating path-planning methods and real-time tracking, a clinical MRI system is adapted to provide this new functionality for controlled interventional targeted therapeutic applications. Through MRI-based sensing analysis, this article aims to propose a framework to plan a robust pathway to enhance the navigation ability to reach deep locations in the human body. The proposed approaches are validated with different experiments.

Elucidating Ligand-Modulated Conformational Landscape of GPCRs Using Cloud-Computing Approaches.

PubMed

Shukla, Diwakar; Lawrenz, Morgan; Pande, Vijay S

2015-01-01

G-protein-coupled receptors (GPCRs) are a versatile family of membrane-bound signaling proteins. Despite the recent successes in obtaining crystal structures of GPCRs, much needs to be learned about the conformational changes associated with their activation. Furthermore, the mechanism by which ligands modulate the activation of GPCRs has remained elusive. Molecular simulations provide a way of obtaining detailed an atomistic description of GPCR activation dynamics. However, simulating GPCR activation is challenging due to the long timescales involved and the associated challenge of gaining insights from the "Big" simulation datasets. Here, we demonstrate how cloud-computing approaches have been used to tackle these challenges and obtain insights into the activation mechanism of GPCRs. In particular, we review the use of Markov state model (MSM)-based sampling algorithms for sampling milliseconds of dynamics of a major drug target, the G-protein-coupled receptor β2-AR. MSMs of agonist and inverse agonist-bound β2-AR reveal multiple activation pathways and how ligands function via modulation of the ensemble of activation pathways. We target this ensemble of conformations with computer-aided drug design approaches, with the goal of designing drugs that interact more closely with diverse receptor states, for overall increased efficacy and specificity. We conclude by discussing how cloud-based approaches present a powerful and broadly available tool for studying the complex biological systems routinely. © 2015 Elsevier Inc. All rights reserved.

A Discrete Dynamical System Approach to Pathway Activation Profiles of Signaling Cascades.

PubMed

Catozzi, S; Sepulchre, J-A

2017-08-01

In living organisms, cascades of covalent modification cycles are one of the major intracellular signaling mechanisms, allowing to transduce physical or chemical stimuli of the external world into variations of activated biochemical species within the cell. In this paper, we develop a novel method to study the stimulus-response of signaling cascades and overall the concept of pathway activation profile which is, for a given stimulus, the sequence of activated proteins at each tier of the cascade. Our approach is based on a correspondence that we establish between the stationary states of a cascade and pieces of orbits of a 2D discrete dynamical system. The study of its possible phase portraits in function of the biochemical parameters, and in particular of the contraction/expansion properties around the fixed points of this discrete map, as well as their bifurcations, yields a classification of the cascade tiers into three main types, whose biological impact within a signaling network is examined. In particular, our approach enables to discuss quantitatively the notion of cascade amplification/attenuation from this new perspective. The method allows also to study the interplay between forward and "retroactive" signaling, i.e., the upstream influence of an inhibiting drug bound to the last tier of the cascade.

Small-angle neutron scattering reveals the assembly mode and oligomeric architecture of TET, a large, dodecameric aminopeptidase

DOE Office of Scientific and Technical Information (OSTI.GOV)

Appolaire, Alexandre; Girard, Eric; Colombo, Matteo

2014-11-01

The present work illustrates that small-angle neutron scattering, deuteration and contrast variation, combined with in vitro particle reconstruction, constitutes a very efficient approach to determine subunit architectures in large, symmetric protein complexes. In the case of the 468 kDa heterododecameric TET peptidase machine, it was demonstrated that the assembly of the 12 subunits is a highly controlled process and represents a way to optimize the catalytic efficiency of the enzyme. The specific self-association of proteins into oligomeric complexes is a common phenomenon in biological systems to optimize and regulate their function. However, de novo structure determination of these important complexesmore » is often very challenging for atomic-resolution techniques. Furthermore, in the case of homo-oligomeric complexes, or complexes with very similar building blocks, the respective positions of subunits and their assembly pathways are difficult to determine using many structural biology techniques. Here, an elegant and powerful approach based on small-angle neutron scattering is applied, in combination with deuterium labelling and contrast variation, to elucidate the oligomeric organization of the quaternary structure and the assembly pathways of 468 kDa, hetero-oligomeric and symmetric Pyrococcus horikoshii TET2–TET3 aminopeptidase complexes. The results reveal that the topology of the PhTET2 and PhTET3 dimeric building blocks within the complexes is not casual but rather suggests that their quaternary arrangement optimizes the catalytic efficiency towards peptide substrates. This approach bears important potential for the determination of quaternary structures and assembly pathways of large oligomeric and symmetric complexes in biological systems.« less

An Open Metadata Schema for Clinical Pathway (openCP) in China.

PubMed

Xu, Wei; Zhu, Yanxin; Wang, Xia

2017-01-01

China has issued and implemented standard clinical pathways (Chinese standard CPs) since 2009; however, they are still paper-based CPs. The aim of the study is to reorganize Chinese standard CPs based on related Chinese medical standards, by using archetype approach, and develop an Open platform for CP (openCP) in China.

A thermosensory pathway that controls body temperature

PubMed Central

Nakamura, Kazuhiro; Morrison, Shaun F.

2008-01-01

Defending body temperature against environmental thermal challenges is one of the most fundamental homeostatic functions governed by the nervous system. Here we show a novel somatosensory pathway, which essentially constitutes the afferent arm of the thermoregulatory reflex triggered by cutaneous sensation of environmental temperature changes. Using rat in vivo electrophysiological and anatomical approaches, we revealed that lateral parabrachial neurons play a pivotal role in this pathway by glutamatergically transmitting cutaneous thermosensory signals received from spinal somatosensory neurons directly to the thermoregulatory command center, preoptic area. This feedforward pathway mediates not only sympathetic and shivering thermogenic responses but also metabolic and cardiac responses to skin cooling challenges. Notably, this ‘thermoregulatory afferent’ pathway exists in parallel with the spinothalamocortical somatosensory pathway mediating temperature perception. These findings make an important contribution to our understanding of both the somatosensory system and thermal homeostasis—two mechanisms fundamental to the nervous system and to our survival. PMID:18084288

A thermosensory pathway that controls body temperature.

PubMed

Nakamura, Kazuhiro; Morrison, Shaun F

2008-01-01

Defending body temperature against environmental thermal challenges is one of the most fundamental homeostatic functions that are governed by the nervous system. Here we describe a somatosensory pathway that essentially constitutes the afferent arm of the thermoregulatory reflex that is triggered by cutaneous sensation of environmental temperature changes. Using in vivo electrophysiological and anatomical approaches in the rat, we found that lateral parabrachial neurons are pivotal in this pathway by glutamatergically transmitting cutaneous thermosensory signals received from spinal somatosensory neurons directly to the thermoregulatory command center, the preoptic area. This feedforward pathway mediates not only sympathetic and shivering thermogenic responses but also metabolic and cardiac responses to skin cooling challenges. Notably, this 'thermoregulatory afferent' pathway exists in parallel with the spinothalamocortical somatosensory pathway that mediates temperature perception. These findings make an important contribution to our understanding of both the somatosensory system and thermal homeostasis -- two mechanisms that are fundamental to the nervous system and to our survival.

Diagnostic Pathway with Multiparametric Magnetic Resonance Imaging Versus Standard Pathway: Results from a Randomized Prospective Study in Biopsy-naïve Patients with Suspected Prostate Cancer.

PubMed

Porpiglia, Francesco; Manfredi, Matteo; Mele, Fabrizio; Cossu, Marco; Bollito, Enrico; Veltri, Andrea; Cirillo, Stefano; Regge, Daniele; Faletti, Riccardo; Passera, Roberto; Fiori, Cristian; De Luca, Stefano

2017-08-01

An approach based on multiparametric magnetic resonance imaging (mpMRI) might increase the detection rate (DR) of clinically significant prostate cancer (csPCa). To compare an mpMRI-based pathway with the standard approach for the detection of prostate cancer (PCa) and csPCa. Between November 2014 and April 2016, 212 biopsy-naïve patients with suspected PCa (prostate specific antigen level ≤15 ng/ml and negative digital rectal examination results) were included in this randomized clinical trial. Patients were randomized into a prebiopsy mpMRI group (arm A, n=107) or a standard biopsy (SB) group (arm B, n=105). In arm A, patients with mpMRI evidence of lesions suspected for PCa underwent mpMRI/transrectal ultrasound fusion software-guided targeted biopsy (TB) (n=81). The remaining patients in arm A (n=26) with negative mpMRI results and patients in arm B underwent 12-core SB. The primary end point was comparison of the DR of PCa and csPCa between the two arms of the study; the secondary end point was comparison of the DR between TB and SB. The overall DRs were higher in arm A versus arm B for PCa (50.5% vs 29.5%, respectively; p=0.002) and csPCa (43.9% vs 18.1%, respectively; p<0.001). Concerning the biopsy approach, that is, TB in arm A, SB in arm A, and SB in arm B, the overall DRs were significantly different for PCa (60.5% vs 19.2% vs 29.5%, respectively; p<0.001) and for csPCa (56.8% vs 3.8% vs 18.1%, respectively; p<0.001). The reproducibility of the study could have been affected by the single-center nature. A diagnostic pathway based on mpMRI had a higher DR than the standard pathway in both PCa and csPCa. In this randomized trial, a pathway for the diagnosis of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI) was compared with the standard pathway based on random biopsy. The mpMRI-based pathway had better performance than the standard pathway. Copyright © 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model.

PubMed

Fang, Yilin; Scheibe, Timothy D; Mahadevan, Radhakrishnan; Garg, Srinath; Long, Philip E; Lovley, Derek R

2011-03-25

The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The modeling system is designed in such a way that constraint-based models targeting different microorganisms or competing organism communities can be easily plugged into the system. Constraint-based modeling is very costly given the size of a genome-scale reaction network. To save computation time, a binary tree is traversed to examine the concentration and solution pool generated during the simulation in order to decide whether the constraint-based model should be called. We also show preliminary results from the integrated model including a comparison of the direct and indirect coupling approaches and evaluated the ability of the approach to simulate field experiment. Published by Elsevier B.V.

Application of stem cell/growth factor system, as a multimodal therapy approach in regenerative medicine to improve cell therapy yields.

PubMed

Pourrajab, Fatemeh; Babaei Zarch, Mojtaba; Baghi Yazdi, Mohammad; Rahimi Zarchi, Abolfazl; Vakili Zarch, Abbas

2014-04-15

Stem cells hold a great promise for regenerative medicine, especially for replacing cells in infarcted organ that hardly have any intrinsic renewal capacity, including heart and brain. Signaling pathways that regulate pluripotency or lineage-specific gene and protein expression have been the major focus of stem cell research. Between them, there are some well known signaling pathways such as GF/GFR systems, SDF-1α/CXC4 ligand receptor interaction and PI3K/Akt signaling, and cytokines may regulate cell fate decisions, and can be utilized to positively influence cell therapy outcomes or accentuate synergistic compliance. For example, contributing factors in the progression of heart failure are both the loss of cardiomyocytes after myocardial infarction, and the absence of an adequate endogenous repair signaling. Combining cell engraftment with therapeutic signaling factor delivery is more exciting in terms of host progenitor/donor stem cell survival and proliferation. Thus stem cell-based therapy, besides triggering signaling pathways through GF/GFR systems can become a realistic option in regenerative processes for replacing lost cells and reconstituting the damaged organ, as before. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Applying the Tuple Space-Based Approach to the Simulation of the Caspases, an Essential Signalling Pathway.

PubMed

Cárdenas-García, Maura; González-Pérez, Pedro Pablo

2013-03-01

Apoptotic cell death plays a crucial role in development and homeostasis. This process is driven by mitochondrial permeabilization and activation of caspases. In this paper we adopt a tuple spaces-based modelling and simulation approach, and show how it can be applied to the simulation of this intracellular signalling pathway. Specifically, we are working to explore and to understand the complex interaction patterns of the caspases apoptotic and the mitochondrial role. As a first approximation, using the tuple spacesbased in silico approach, we model and simulate both the extrinsic and intrinsic apoptotic signalling pathways and the interactions between them. During apoptosis, mitochondrial proteins, released from mitochondria to cytosol are decisively involved in the process. If the decision is to die, from this point there is normally no return, cancer cells offer resistance to the mitochondrial induction.

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

PubMed

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

Network Reconstruction Using Nonparametric Additive ODE Models

PubMed Central

Henderson, James; Michailidis, George

2014-01-01

Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative estimation. PMID:24732037

Nanotubule and Tour Molecule Based Molecular Electronics: Suggestion for a Hybrid Approach

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Saini, Subhash (Technical Monitor)

1998-01-01

Recent experimental and theoretical attempts and results indicate two distinct broad pathways towards future molecular electronic devices and architectures. The first is the approach via Tour type ladder molecules and their junctions which can be fabricated with solution phase chemical approaches. Second are fullerenes or nanotubules and their junctions which may have better conductance, switching and amplifying characteristics but can not be made through well controlled and defined chemical means. A hybrid approach combining the two pathways to take advantage of the characteristics of both is suggested. Dimension and scale of such devices would be somewhere in between isolated molecule and nanotubule based devices but it maybe possible to use self-assembly towards larger functional and logicalunits.

Correlation analysis of targeted proteins and metabolites to assess and engineer microbial isopentenol production.

PubMed

George, Kevin W; Chen, Amy; Jain, Aakriti; Batth, Tanveer S; Baidoo, Edward E K; Wang, George; Adams, Paul D; Petzold, Christopher J; Keasling, Jay D; Lee, Taek Soon

2014-08-01

The ability to rapidly assess and optimize heterologous pathway function is critical for effective metabolic engineering. Here, we develop a systematic approach to pathway analysis based on correlations between targeted proteins and metabolites and apply it to the microbial production of isopentenol, a promising biofuel. Starting with a seven-gene pathway, we performed a correlation analysis to reduce pathway complexity and identified two pathway proteins as the primary determinants of efficient isopentenol production. Aided by the targeted quantification of relevant pathway intermediates, we constructed and subsequently validated a conceptual model of isopentenol pathway function. Informed by our analysis, we assembled a strain which produced isopentenol at a titer 1.5 g/L, or 46% of theoretical yield. Our engineering approach allowed us to accurately identify bottlenecks and determine appropriate pathway balance. Paired with high-throughput cloning techniques and analytics, this strategy should prove useful for the analysis and optimization of increasingly complex heterologous pathways. © 2014 Wiley Periodicals, Inc.

Plant MetGenMAP: an integrative analysis system for plant systems biology

USDA-ARS?s Scientific Manuscript database

We have developed a web-based system, Plant MetGenMAP, which can identify significantly altered biochemical pathways and highly affected biological processes, predict functional roles of pathway genes, and potential pathway-related regulatory motifs from transcript and metabolite profile datasets. P...

Astragaloside IV Attenuates Glutamate-Induced Neurotoxicity in PC12 Cells through Raf-MEK-ERK Pathway.

PubMed

Yue, Rongcai; Li, Xia; Chen, Bingyang; Zhao, Jing; He, Weiwei; Yuan, Hu; Yuan, Xing; Gao, Na; Wu, Guozhen; Jin, Huizi; Shan, Lei; Zhang, Weidong

2015-01-01

Astragaloside IV (AGS-IV) is a main active ingredient of Astragalus membranaceus Bunge, a medicinal herb prescribed as an immunostimulant, hepatoprotective, antiperspirant, a diuretic or a tonic as documented in Chinese Materia Medica. In the present study, we employed a high-throughput comparative proteomic approach based on 2D-nano-LC-MS/MS to investigate the possible mechanism of action involved in the neuroprotective effect of AGS-IV against glutamate-induced neurotoxicity in PC12 cells. Differential proteins were identified, among which 13 proteins survived the stringent filter criteria and were further included for functional discussion. Two proteins (vimentin and Gap43) were randomly selected, and their expression levels were further confirmed by western blots analysis. The results matched well with those of proteomics. Furthermore, network analysis of protein-protein interactions (PPI) and pathways enrichment with AGS-IV associated proteins were carried out to illustrate its underlying molecular mechanism. Proteins associated with signal transduction, immune system, signaling molecules and interaction, and energy metabolism play important roles in neuroprotective effect of AGS-IV and Raf-MEK-ERK pathway was involved in the neuroprotective effect of AGS-IV against glutamate-induced neurotoxicity in PC12 cells. This study demonstrates that comparative proteomics based on shotgun approach is a valuable tool for molecular mechanism studies, since it allows the simultaneously evaluate the global proteins alterations.

SYSTEMS BIOLOGY ANALYSES OF GENE EXPRESSION AND GENOME WIDE ASSOCIATION STUDY DATA IN OBSTRUCTIVE SLEEP APNEA

PubMed Central

LIU, YU; PATEL, SANJAY; NIBBE, ROD; MAXWELL, SEAN; CHOWDHURY, SALIM A.; KOYUTURK, MEHMET; ZHU, XIAOFENG; LARKIN, EMMA K.; BUXBAUM, SARAH G; PUNJABI, NARESH M.; GHARIB, SINA A.; REDLINE, SUSAN; CHANCE, MARK R.

2015-01-01

The precise molecular etiology of obstructive sleep apnea (OSA) is unknown; however recent research indicates that several interconnected aberrant pathways and molecular abnormalities are contributors to OSA. Identifying the genes and pathways associated with OSA can help to expand our understanding of the risk factors for the disease as well as provide new avenues for potential treatment. Towards these goals, we have integrated relevant high dimensional data from various sources, such as genome-wide expression data (microarray), protein-protein interaction (PPI) data and results from genome-wide association studies (GWAS) in order to define sub-network elements that connect some of the known pathways related to the disease as well as define novel regulatory modules related to OSA. Two distinct approaches are applied to identify sub-networks significantly associated with OSA. In the first case we used a biased approach based on sixty genes/proteins with known associations with sleep disorders and/or metabolic disease to seed a search using commercial software to discover networks associated with disease followed by information theoretic (mutual information) scoring of the sub-networks. In the second case we used an unbiased approach and generated an interactome constructed from publicly available gene expression profiles and PPI databases, followed by scoring of the network with p-values from GWAS data derived from OSA patients to uncover sub-networks significant for the disease phenotype. A comparison of the approaches reveals a number of proteins that have been previously known to be associated with OSA or sleep. In addition, our results indicate a novel association of Phosphoinositide 3-kinase, the STAT family of proteins and its related pathways with OSA. PMID:21121029

Toward a Systems Approach to Enteric Pathogen Transmission: From Individual Independence to Community Interdependence

PubMed Central

Eisenberg, Joseph N.S.; Trostle, James; Sorensen, Reed J.D.; Shields, Katherine F.

2012-01-01

Diarrheal disease is still a major cause of mortality and morbidity worldwide; thus a large body of research has been produced describing its risks. We review more than four decades of literature on diarrheal disease epidemiology. These studies detail a progression in the conceptual understanding of transmission of enteric pathogens and demonstrate that diarrheal disease is caused by many interdependent pathways. However, arguments by diarrheal disease researchers in favor of attending to interaction and interdependencies have only recently yielded more formal systems-level approaches. Therefore, interdependence has not yet been highlighted in significant new research initiatives or policy decisions. We argue for a systems-level framework that will contextualize transmission and inform prevention and control efforts so that they can integrate transmission pathways. These systems approaches should be employed to account for community effects (i.e., interactions among individuals and/or households). PMID:22224881

Pathway-based analyses.

PubMed

Kent, Jack W

2016-02-03

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing. The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge. Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data. The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

A proposed clinical research support career pathway for noninvestigators.

PubMed

Smith, Sheree; Gullick, Janice; Ballard, Jacqueline; Perry, Lin

2018-06-01

To discuss the international experience of clinical research support for noninvestigator roles and to propose a new pathway for Australia, to promote a sustainable research support workforce capable of delivering high-quality clinical research. Noninvestigator research support roles are currently characterized by an ad hoc approach to training, with limited role delineation and perceived professional isolation with implications for study completion rates and participant safety. A focused approach to developing and implementing research support pathways has improved patient recruitment, study completion, job satisfaction, and research governance. The Queensland and New South Wales state-based Nurses' Awards, the Australian Qualifications Framework, and a University Professional (Research) Staff Award. Research nurses in the clinical environment improve study coordination, adherence to study protocol, patient safety, and clinical care. A career pathway that guides education and outlines position descriptions and skill sets would enhance development of the research support workforce. This pathway could contribute to changing the patient outcomes through coordination and study completion of high-quality research. A wide consultative approach is required to determine a cost-effective and feasible approach to implementation and evaluation of the proposed pathway. © 2018 John Wiley & Sons Australia, Ltd.

American Society for Enhanced Recovery (ASER) and Perioperative Quality Initiative (POQI) joint consensus statement on measurement to maintain and improve quality of enhanced recovery pathways for elective colorectal surgery.

PubMed

Moonesinghe, S Ramani; Grocott, Michael P W; Bennett-Guerrero, Elliott; Bergamaschi, Roberto; Gottumukkala, Vijaya; Hopkins, Thomas J; McCluskey, Stuart; Gan, Tong J; Mythen, Michael Monty G; Shaw, Andrew D; Miller, Timothy E

2017-01-01

This article sets out a framework for measurement of quality of care relevant to enhanced recovery pathways (ERPs) in elective colorectal surgery. The proposed framework is based on established measurement systems and/or theories, and provides an overview of the different approaches for improving clinical monitoring, and enhancing quality improvement or research in varied settings with different levels of available resources. Using a structure-process-outcome framework, we make recommendations for three hierarchical tiers of data collection. Core, Quality Improvement, and Best Practice datasets are proposed. The suggested datasets incorporate patient data to describe case-mix, process measures to describe delivery of enhanced recovery and clinical outcomes. The fundamental importance of routine collection of data for the initiation, maintenance, and enhancement of enhanced recovery pathways is emphasized.

Electronic SSKIN pathway: reducing device-related pressure ulcers.

PubMed

Campbell, Natalie

2016-08-11

This article describes how an interprofessional project in a London NHS Foundation Trust was undertaken to develop an intranet-based medical device-related pressure ulcer prevention and management pathway for clinical staff working across an adult critical care directorate, where life-threatening events require interventions using medical devices. The aim of this project was to improve working policies and processes to define key prevention strategies and provide clinicians with a clear, standardised approach to risk and skin assessment, equipment use, documentation and reporting clinical data using the Trust's CareVue (electronic medical records), Datix (incident reporting and risk-management tool) and eTRACE (online clinical protocol ordering) systems. The process included the development, trial and local implementation of the pathway using collaborative teamwork and the SSKIN care bundle tool. The experience of identifying issues, overcoming challenges, defining best practice and cascading SSKIN awareness training is shared.

t4 Workshop Report*

PubMed Central

Kleensang, Andre; Maertens, Alexandra; Rosenberg, Michael; Fitzpatrick, Suzanne; Lamb, Justin; Auerbach, Scott; Brennan, Richard; Crofton, Kevin M.; Gordon, Ben; Fornace, Albert J.; Gaido, Kevin; Gerhold, David; Haw, Robin; Henney, Adriano; Ma’ayan, Avi; McBride, Mary; Monti, Stefano; Ochs, Michael F.; Pandey, Akhilesh; Sharan, Roded; Stierum, Rob; Tugendreich, Stuart; Willett, Catherine; Wittwehr, Clemens; Xia, Jianguo; Patton, Geoffrey W.; Arvidson, Kirk; Bouhifd, Mounir; Hogberg, Helena T.; Luechtefeld, Thomas; Smirnova, Lena; Zhao, Liang; Adeleye, Yeyejide; Kanehisa, Minoru; Carmichael, Paul; Andersen, Melvin E.; Hartung, Thomas

2014-01-01

Summary Despite wide-spread consensus on the need to transform toxicology and risk assessment in order to keep pace with technological and computational changes that have revolutionized the life sciences, there remains much work to be done to achieve the vision of toxicology based on a mechanistic foundation. A workshop was organized to explore one key aspect of this transformation – the development of Pathways of Toxicity (PoT) as a key tool for hazard identification based on systems biology. Several issues were discussed in depth in the workshop: The first was the challenge of formally defining the concept of a PoT as distinct from, but complementary to, other toxicological pathway concepts such as mode of action (MoA). The workshop came up with a preliminary definition of PoT as “A molecular definition of cellular processes shown to mediate adverse outcomes of toxicants”. It is further recognized that normal physiological pathways exist that maintain homeostasis and these, sufficiently perturbed, can become PoT. Second, the workshop sought to define the adequate public and commercial resources for PoT information, including data, visualization, analyses, tools, and use-cases, as well as the kinds of efforts that will be necessary to enable the creation of such a resource. Third, the workshop explored ways in which systems biology approaches could inform pathway annotation, and which resources are needed and available that can provide relevant PoT information to the diverse user communities. PMID:24127042

Enabling locally-developed content for access through the infobutton by means of automated concept annotation.

PubMed

Hulse, Nathan C; Long, Jie; Xu, Xiaomin; Tao, Cui

2014-01-01

Infobuttons have proven to be an increasingly important resource in providing a standardized approach to integrating useful educational materials at the point of care in electronic health records (EHRs). They provide a simple, uniform pathway for both patients and providers to receive pertinent education materials in a quick fashion from within EHRs and Personalized Health Records (PHRs). In recent years, the international standards organization Health Level Seven has balloted and approved a standards-based pathway for requesting and receiving data for infobuttons, simplifying some of the barriers for their adoption in electronic medical records and amongst content providers. Local content, developed by the hosting organization themselves, still needs to be indexed and annotated with appropriate metadata and terminologies in order to be fully accessible via the infobutton. In this manuscript we present an approach for automating the annotation of internally-developed patient education sheets with standardized terminologies and compare and contrast the approach with manual approaches used previously. We anticipate that a combination of system-generated and human reviewed annotations will provide the most comprehensive and effective indexing strategy, thereby allowing best access to internally-created content via the infobutton.

The catchment based approach using catchment system engineering

NASA Astrophysics Data System (ADS)

Jonczyk, Jennine; Quinn, Paul; Barber, Nicholas; Wilkinson, Mark

2015-04-01

The catchment based approach (CaBa) has been championed as a potential mechanism for delivery of environmental directives such as the Water Framework Directive in the UK. However, since its launch in 2013, there has been only limited progress towards achieving sustainable, holistic management, with only a few of examples of good practice ( e.g. from the Tyne Rivers trust). Common issues with developing catchment plans over a national scale include limited data and resources to identify issues and source of those issues, how to systematically identify suitable locations for measures or suites of measures that will have the biggest downstream impact and how to overcome barriers for implementing solutions. Catchment System Engineering (CSE) is an interventionist approach to altering the catchment scale runoff regime through the manipulation of hydrological flow pathways throughout the catchment. A significant component of the runoff generation can be managed by targeting hydrological flow pathways at source, such as overland flow, field drain and ditch function, greatly reducing erosive soil losses. Coupled with management of farm nutrients at source, many runoff attenuation features or measures can be co-located to achieve benefits for water quality and biodiversity. A catchment, community-led mitigation measures plan using the CSE approach will be presented from a catchment in Northumberland, Northern England that demonstrate a generic framework for identification of multi-purpose features that slow, store and filter runoff at strategic locations in the landscape. Measures include within-field barriers, edge of field traps and within-ditch measures. Progress on the implementation of measures will be reported alongside potential impacts on the runoff regime at both local and catchment scale and costs.

Novel approaches to effects-based monitoring: 21st century tools for bio-effects prediction and surveillance

EPA Science Inventory

Effects-based monitoring (EBM) has been employed as a complement to chemical monitoring to help address knowledge gaps between chemical occurrence and biological effects. We have piloted several pathway-based approaches to EBM, that utilize modern bioinformatic and high throughpu...

Career Pathways: Aligning Public Resources to Support Individual and Regional Economic Advancement in the Knowledge Economy

ERIC Educational Resources Information Center

Jenkins, Davis

2006-01-01

This paper describes career pathways, a framework or approach by which regions can better align publicly supported systems and programs to build a knowledge-economy workforce customized to the needs of local labor markets. A career pathway is a series of connected education and training programs and support services that enable individuals to…

A Multi-omics Approach to Understand the Microbial Transformation of Lignocellulosic Materials in the Digestive System of the Wood-Feeding Beetle Odontotaenius disjunctus

NASA Astrophysics Data System (ADS)

Ceja Navarro, J. A.; Karaoz, U.; White, R. A., III; Lipton, M. S.; Adkins, J.; Mayali, X.; Blackwell, M.; Pett-Ridge, J.; Brodie, E.; Hao, Z.

2015-12-01

Odontotaenius disjuctus is a wood feeding beetle that processes large amounts of hardwoods and plays an important role in forest carbon cycling. In its gut, plant material is transformed into simple molecules by sequential processing during passage through the insect's digestive system. In this study, we used multiple 'omics approaches to analyze the distribution of microbial communities and their specific functions in lignocellulose deconstruction within the insect's gut. Fosmid clones were selected and sequenced from a pool of clones based on their expression of plant polymer degrading enzymes, allowing the identification of a wide range of carbohydrate degrading enzymes. Comparison of metagenomes of all gut regions demonstrated the distribution of genes across the beetle gut. Cellulose, starch, and xylan degradation genes were particularly abundant in the midgut and posterior hindgut. Genes involved in hydrogenotrophic production of methane and nitrogenases were more abundant in the anterior hindgut. Assembled contigs were binned into 127 putative genomes representing Bacteria, Archaea, Fungi and Nematodes. Eleven complete genomes were reconstructed allowing to identify linked functions/traits, including organisms with cellulosomes, and a combined potential for cellulose, xylan and starch hydrolysis and nitrogen fixation. A metaproteomic study was conducted to test the expression of the pathways identified in the metagenomic study. Preliminary analyses suggest enrichment of pathways related to hemicellulosic degradation. A complete xylan degradation pathway was reconstructed and GC-MS/MS based metabolomics identified xylobiose and xylose as major metabolite pools. To relate microbial identify to function in the beetle gut, Chip-SIP isotope tracing was conducted with RNA extracted from beetles fed 13C-cellulose. Multiple 13C enriched bacterial groups were detected, mainly in the midgut. Our multi-omics approach has allowed us to characterize the contribution of the gut microbiota to the transformation of woody biomass and the distribution of microbial-driven function in the beetle's gut. Through the study of such highly evolved polymer deconstruction and fermentation system we want to identify criteria for design of improved lignocellulosic fuel production processes.

A framework for managing runoff and pollution in the rural landscape using a Catchment Systems Engineering approach.

PubMed

Wilkinson, M E; Quinn, P F; Barber, N J; Jonczyk, J

2014-01-15

Intense farming plays a key role in increasing local scale runoff and erosion rates, resulting in water quality issues and flooding problems. There is potential for agricultural management to become a major part of improved strategies for controlling runoff. Here, a Catchment Systems Engineering (CSE) approach has been explored to solve the above problem. CSE is an interventionist approach to altering the catchment scale runoff regime through the manipulation of hydrological flow pathways throughout the catchment. By targeting hydrological flow pathways at source, such as overland flow, field drain and ditch function, a significant component of the runoff generation can be managed in turn reducing soil nutrient losses. The Belford catchment (5.7 km(2)) is a catchment scale study for which a CSE approach has been used to tackle a number of environmental issues. A variety of Runoff Attenuation Features (RAFs) have been implemented throughout the catchment to address diffuse pollution and flooding issues. The RAFs include bunds disconnecting flow pathways, diversion structures in ditches to spill and store high flows, large wood debris structure within the channel, and riparian zone management. Here a framework for applying a CSE approach to the catchment is shown in a step by step guide to implementing mitigation measures in the Belford Burn catchment. The framework is based around engagement with catchment stakeholders and uses evidence arising from field science. Using the framework, the flooding issue has been addressed at the catchment scale by altering the runoff regime. Initial findings suggest that RAFs have functioned as designed to reduce/attenuate runoff locally. However, evidence suggested that some RAFs needed modification and new RAFs be created to address diffuse pollution issues during storm events. Initial findings from these modified RAFs are showing improvements in sediment trapping capacities and reductions in phosphorus, nitrate and suspended sediment losses during storm events. © 2013.

On-line metabolic pathway analysis based on metabolic signal flow diagram.

PubMed

Shi, H; Shimizu, K

In this work, an integrated modeling approach based on a metabolic signal flow diagram and cellular energetics was used to model the metabolic pathway analysis for the cultivation of yeast on glucose. This approach enables us to make a clear analysis of the flow direction of the carbon fluxes in the metabolic pathways as well as of the degree of activation of a particular pathway for the synthesis of biomaterials for cell growth. The analyses demonstrate that the main metabolic pathways of Saccharomyces cerevisiae change significantly during batch culture. Carbon flow direction is toward glycolysis to satisfy the increase of requirement for precursors and energy. The enzymatic activation of TCA cycle seems to always be at normal level, which may result in the overflow of ethanol due to its limited capacity. The advantage of this approach is that it adopts both virtues of the metabolic signal flow diagram and the simple network analysis method, focusing on the investigation of the flow directions of carbon fluxes and the degree of activation of a particular pathway or reaction loop. All of the variables used in the model equations were determined on-line; the information obtained from the calculated metabolic coefficients may result in a better understanding of cell physiology and help to evaluate the state of the cell culture process. Copyright 1998 John Wiley & Sons, Inc.

Simulation of dual carbon-bromine stable isotope fractionation during 1,2-dibromoethane degradation.

PubMed

Jin, Biao; Nijenhuis, Ivonne; Rolle, Massimo

2018-06-01

We performed a model-based investigation to simultaneously predict the evolution of concentration, as well as stable carbon and bromine isotope fractionation during 1,2-dibromoethane (EDB, ethylene dibromide) transformation in a closed system. The modelling approach considers bond-cleavage mechanisms during different reactions and allows evaluating dual carbon-bromine isotopic signals for chemical and biotic reactions, including aerobic and anaerobic biological transformation, dibromoelimination by Zn(0) and alkaline hydrolysis. The proposed model allowed us to accurately simulate the evolution of concentrations and isotope data observed in a previous laboratory study and to successfully identify different reaction pathways. Furthermore, we illustrated the model capabilities in degradation scenarios involving complex reaction systems. Specifically, we examined (i) the case of sequential multistep transformation of EDB and the isotopic evolution of the parent compound, the intermediate and the reaction product and (ii) the case of parallel competing abiotic pathways of EDB transformation in alkaline solution.

Boosting functionality of synthetic DNA circuits with tailored deactivation

PubMed Central

Montagne, Kevin; Gines, Guillaume; Fujii, Teruo; Rondelez, Yannick

2016-01-01

Molecular programming takes advantage of synthetic nucleic acid biochemistry to assemble networks of reactions, in vitro, with the double goal of better understanding cellular regulation and providing information-processing capabilities to man-made chemical systems. The function of molecular circuits is deeply related to their topological structure, but dynamical features (rate laws) also play a critical role. Here we introduce a mechanism to tune the nonlinearities associated with individual nodes of a synthetic network. This mechanism is based on programming deactivation laws using dedicated saturable pathways. We demonstrate this approach through the conversion of a single-node homoeostatic network into a bistable and reversible switch. Furthermore, we prove its generality by adding new functions to the library of reported man-made molecular devices: a system with three addressable bits of memory, and the first DNA-encoded excitable circuit. Specific saturable deactivation pathways thus greatly enrich the functional capability of a given circuit topology. PMID:27845324

Creation and Execution of a Novel Anesthesia Perioperative Care Service at a Veterans Affairs Hospital.

PubMed

Alvis, Bret D; King, Adam B; Pandharipande, Pratik P; Weavind, Liza M; Avila, Katelin; Leisy, Philip J; Ajmal, Muhammad; McHugh, Michael; Keegan, Kirk A; Baker, David A; Walia, Ann; Hughes, Christopher G

2017-11-01

Physician-led perioperative surgical home models are developing as a method for improving the American health care system. These models are novel, team-based approaches that help to provide continuity of care throughout the perioperative period. Another avenue for improving care for surgical patients is the use of enhanced recovery after surgery pathways. These are well-described methods that have shown to improve perioperative outcomes. An established perioperative surgical home model can help implementation, efficiency, and adherence to enhanced recovery after surgery pathways. For these reasons, the Tennessee Valley Healthcare System, Nashville Veterans Affairs Medical Center created an Anesthesiology Perioperative Care Service that provides comprehensive care to surgical patients from their preoperative period through the continuum of their hospital course and postdischarge follow-up. In this brief report, we describe the development, implementation, and preliminary outcomes of the service.

A Chemical Biology Approach to Interrogate Quorum Sensing Regulated Behaviors at the Molecular and Cellular Level

PubMed Central

Lowery, Colin A.; Matamouros, Susana; Niessen, Sherry; Zhu, Jie; Scolnick, Jonathan A.; Mee, Jenny M.; Cravatt, Benjamin F.; Miller, Samuel I.; Kaufmann, Gunnar F.; Janda, Kim D.

2013-01-01

SUMMARY Small molecule probes have been employed extensively to explore biological systems and elucidate cellular signaling pathways. In this study, we utilize an inhibitor of bacterial communication to monitor changes in the proteome of Salmonella enterica serovar Typhimurium with the aim of discovering new processes regulated by AI-2-based quorum sensing (QS), a mechanism of bacterial intracellular communication that allows for the coordination of gene expression in a cell density-dependent manner. In S. typhimurium, this system regulates the uptake and catabolism of intracellular signals and has been implicated in pathogenesis, including the invasion of host epithelial cells. We demonstrate that our QS antagonist is capable of selectively inhibiting the expression of known QS-regulated proteins in S. typhimurium, thus attesting that QS inhibitors may be used to confirm proposed and elucidate previously unidentified QS pathways without relying on genetic manipulation. PMID:23890008

A chemical biology approach to interrogate quorum-sensing regulated behaviors at the molecular and cellular level.

PubMed

Lowery, Colin A; Matamouros, Susana; Niessen, Sherry; Zhu, Jie; Scolnick, Jonathan; Lively, Jenny M; Cravatt, Benjamin F; Miller, Samuel I; Kaufmann, Gunnar F; Janda, Kim D

2013-07-25

Small molecule probes have been used extensively to explore biologic systems and elucidate cellular signaling pathways. In this study, we use an inhibitor of bacterial communication to monitor changes in the proteome of Salmonella enterica serovar Typhimurium with the aim of discovering unrecognized processes regulated by AI-2-based quorum-sensing (QS), a mechanism of bacterial intercellular communication that allows for the coordination of gene expression in a cell density-dependent manner. In S. typhimurium, this system regulates the uptake and catabolism of intercellular signals and has been implicated in pathogenesis, including the invasion of host epithelial cells. We demonstrate that our QS antagonist is capable of selectively inhibiting the expression of known QS-regulated proteins in S. typhimurium, thus attesting that QS inhibitors may be used to confirm proposed and elucidate previously unidentified QS pathways without relying on genetic manipulation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Visual agnosia and focal brain injury.

PubMed

Martinaud, O

Visual agnosia encompasses all disorders of visual recognition within a selective visual modality not due to an impairment of elementary visual processing or other cognitive deficit. Based on a sequential dichotomy between the perceptual and memory systems, two different categories of visual object agnosia are usually considered: 'apperceptive agnosia' and 'associative agnosia'. Impaired visual recognition within a single category of stimuli is also reported in: (i) visual object agnosia of the ventral pathway, such as prosopagnosia (for faces), pure alexia (for words), or topographagnosia (for landmarks); (ii) visual spatial agnosia of the dorsal pathway, such as cerebral akinetopsia (for movement), or orientation agnosia (for the placement of objects in space). Focal brain injuries provide a unique opportunity to better understand regional brain function, particularly with the use of effective statistical approaches such as voxel-based lesion-symptom mapping (VLSM). The aim of the present work was twofold: (i) to review the various agnosia categories according to the traditional visual dual-pathway model; and (ii) to better assess the anatomical network underlying visual recognition through lesion-mapping studies correlating neuroanatomical and clinical outcomes. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure.

PubMed

Chen, Langdong; Cao, Yan; Zhang, Hai; Lv, Diya; Zhao, Yahong; Liu, Yanjun; Ye, Guan; Chai, Yifeng

2018-01-31

Yangxinshi tablet (YXST) is an effective treatment for heart failure and myocardial infarction; it consists of 13 herbal medicines formulated according to traditional Chinese Medicine (TCM) practices. It has been used for the treatment of cardiovascular disease for many years in China. In this study, a network pharmacology-based strategy was used to elucidate the mechanism of action of YXST for the treatment of heart failure. Cardiovascular disease-related protein target and compound databases were constructed for YXST. A molecular docking platform was used to predict the protein targets of YXST. The affinity between proteins and ingredients was determined using surface plasmon resonance (SPR) assays. The action modes between targets and representative ingredients were calculated using Glide docking, and the related pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A protein target database containing 924 proteins was constructed; 179 compounds in YXST were identified, and 48 compounds with high relevance to the proteins were defined as representative ingredients. Thirty-four protein targets of the 48 representative ingredients were analyzed and classified into two categories: immune and cardiovascular systems. The SPR assay and molecular docking partly validated the interplay between protein targets and representative ingredients. Moreover, 28 pathways related to heart failure were identified, which provided directions for further research on YXST. This study demonstrated that the cardiovascular protective effect of YXST mainly involved the immune and cardiovascular systems. Through the research strategy based on network pharmacology, we analysis the complex system of YXST and found 48 representative compounds, 34 proteins and 28 related pathways of YXST, which could help us understand the underlying mechanism of YSXT's anti-heart failure effect. The network-based investigation could help researchers simplify the complex system of YXSY. It may also offer a feasible approach to decipher the chemical and pharmacological bases of other TCM formulas. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of artifical intelligence principles to the analysis of "crazy" speech.

PubMed

Garfield, D A; Rapp, C

1994-04-01

Artificial intelligence computer simulation methods can be used to investigate psychotic or "crazy" speech. Here, symbolic reasoning algorithms establish semantic networks that schematize speech. These semantic networks consist of two main structures: case frames and object taxonomies. Node-based reasoning rules apply to object taxonomies and pathway-based reasoning rules apply to case frames. Normal listeners may recognize speech as "crazy talk" based on violations of node- and pathway-based reasoning rules. In this article, three separate segments of schizophrenic speech illustrate violations of these rules. This artificial intelligence approach is compared and contrasted with other neurolinguistic approaches and is discussed as a conceptual link between neurobiological and psychodynamic understandings of psychopathology.

Power Systems of the Future: A 21st Century Power Partnership Thought Leadership Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zinaman, Owen; Miller, Mackay; Adil, Ali

This report summarizes key forces driving transformation in the power sector around the world, presents a framework for evaluating decisions regarding extent and pace of change, and defines pathways for transformation. Powerful trends in technology, policy environments, financing, and business models are driving change in power sectors globally. In light of these trends, the question is no longer whether power systems will be transformed, but rather how these transformations will occur. Three approaches to policy and technology decision-making can guide these transformations: adaptive, reconstructive, and evolutionary. Within these approaches, we explore the five pathways that have emerged as viable modelsmore » for power system transformation.« less

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

PubMed

Ozerov, Ivan V; Lezhnina, Ksenia V; Izumchenko, Evgeny; Artemov, Artem V; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N; Labat, Ivan; West, Michael D; Buzdin, Anton; Cantor, Charles R; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-11-16

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

PubMed Central

Ozerov, Ivan V.; Lezhnina, Ksenia V.; Izumchenko, Evgeny; Artemov, Artem V.; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N.; Labat, Ivan; West, Michael D.; Buzdin, Anton; Cantor, Charles R.; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-01-01

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy. PMID:27848968

Revealing the favorable dissociation pathway of type II kinase inhibitors via enhanced sampling simulations and two-end-state calculations.

PubMed

Sun, Huiyong; Tian, Sheng; Zhou, Shunye; Li, Youyong; Li, Dan; Xu, Lei; Shen, Mingyun; Pan, Peichen; Hou, Tingjun

2015-02-13

How does a type II inhibitor bind to/unbind from a kinase target is still a confusing question because the small molecule occupies both the ATP pocket and the allosteric pocket of the kinase binding site. Here, by using enhanced sampling simulations (umbrella sampling, US) and two-end-state free energy calculations (MM/GSBA), we systemically studied the dissociation processes of two distinct small molecules escaping from the binding pocket of p38 MAP kinase through the allosteric channel and the ATP channel. The results show that the unbinding pathways along the allosteric channel have much lower PMF depths than those along the ATP channel, suggesting that the allosteric channel is more favorable for the dissociations of the two inhibitors and thereby supporting the general understanding that the largest channel of a target is usually the entry/exit pathway for the binding/dissociation of small molecules. Interestingly, the MM/GBSA approach yielded similar PMF profiles compared with those based on US, a much time consuming approach, indicating that for a general study, such as detecting the important transition state of a ligand binding/unbinding process, MM/GBSA may be a feasible choice.

A three-tiered approach for linking pharmacokinetic considerations to the adverse outcome pathway framework for chemical-specific risk assessment

EPA Science Inventory

The power of the adverse outcome pathway (AOP) framework arises from its utilization of pathway-based data to describe the initial interaction of a chemical with a molecular target (molecular initiating event; (MIE), followed by a progression through a series of key events that l...

GOSAP: Gene Ontology-Based Semantic Alignment of Biological Pathways.

PubMed

Gamalielsson, Jonas; Olsson, Bjorn

2008-01-01

We present a new method for semantic comparison of biological pathways, aiming to discover evolutionary conservation of pathways between species. Our method uses all three sub-ontologies of Gene Ontology (GO) and a measure of semantic similarity to calculate match scores between gene products. These scores are used for finding local pairwise pathway alignments. This approach has the advantage of being applicable to all types of pathways where nodes are gene products, e.g., regulatory pathways, signalling pathways and metabolic enzyme-to-enzyme pathways. We demonstrate the usefulness of the method using regulatory and metabolic pathways from E. coli and S. cerevisiae as examples.

Dynamic optimal control of homeostasis: an integrative system approach for modeling of the central nitrogen metabolism in Saccharomyces cerevisiae.

PubMed

van Riel, N A; Giuseppin, M L; Verrips, C T

2000-01-01

The theory of dynamic optimal metabolic control (DOMC), as developed by Giuseppin and Van Riel (Metab. Eng., 2000), is applied to model the central nitrogen metabolism (CNM) in Saccharomyces cerevisiae. The CNM represents a typical system encountered in advanced metabolic engineering. The CNM is the source of the cellular amino acids and proteins, including flavors and potentially valuable biomolecules; therefore, it is also of industrial interest. In the DOMC approach the cell is regarded as an optimally controlled system. Given the metabolic genotype, the cell faces a control problem to maintain an optimal flux distribution in a changing environment. The regulation is based on strategies and balances feedback control of homeostasis and feedforward regulation for adaptation. The DOMC approach is an integrative, holistic approach, not based on mechanistic descriptions and (therefore) not biased by the variation present in biochemical and molecular biological data. It is an effective tool to structure the rapidly increasing amount of data on the function of genes and pathways. The DOMC model is used successfully to predict the responses of pulses of ammonia and glutamine to nitrogen-limited continuous cultures of a wild-type strain and a glutamine synthetase-negative mutant. The simulation results are validated with experimental data.

Metabolic Pathway Assignment of Plant Genes based on Phylogenetic Profiling–A Feasibility Study

PubMed Central

Weißenborn, Sandra; Walther, Dirk

2017-01-01

Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes that share a common co-occurrence pattern across different genomes, has gained renewed attention as it promises to annotate gene functions based on presence/absence calls alone. We applied phylogenetic profiling to the problem of metabolic pathway assignments of plant genes with a particular focus on secondary metabolism pathways. We determined phylogenetic profiles for 40,960 metabolic pathway enzyme genes with assigned EC numbers from 24 plant species based on sequence and pathway annotation data from KEGG and Ensembl Plants. For gene sequence family assignments, needed to determine the presence or absence of particular gene functions in the given plant species, we included data of all 39 species available at the Ensembl Plants database and established gene families based on pairwise sequence identities and annotation information. Aside from performing profiling comparisons, we used machine learning approaches to predict pathway associations from phylogenetic profiles alone. Selected metabolic pathways were indeed found to be composed of gene families of greater than expected phylogenetic profile similarity. This was particularly evident for primary metabolism pathways, whereas for secondary pathways, both the available annotation in different species as well as the abstraction of functional association via distinct pathways proved limiting. While phylogenetic profile similarity was generally not found to correlate with gene co-expression, direct physical interactions of proteins were reflected by a significantly increased profile similarity suggesting an application of phylogenetic profiling methods as a filtering step in the identification of protein-protein interactions. This feasibility study highlights the potential and challenges associated with phylogenetic profiling methods for the detection of functional relationships between genes as well as the need to enlarge the set of plant genes with proven secondary metabolism involvement as well as the limitations of distinct pathways as abstractions of relationships between genes. PMID:29163570

A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats

NASA Astrophysics Data System (ADS)

Hogri, Roni; Bamford, Simeon A.; Taub, Aryeh H.; Magal, Ari; Giudice, Paolo Del; Mintz, Matti

2015-02-01

Neuroprostheses could potentially recover functions lost due to neural damage. Typical neuroprostheses connect an intact brain with the external environment, thus replacing damaged sensory or motor pathways. Recently, closed-loop neuroprostheses, bidirectionally interfaced with the brain, have begun to emerge, offering an opportunity to substitute malfunctioning brain structures. In this proof-of-concept study, we demonstrate a neuro-inspired model-based approach to neuroprostheses. A VLSI chip was designed to implement essential cerebellar synaptic plasticity rules, and was interfaced with cerebellar input and output nuclei in real time, thus reproducing cerebellum-dependent learning in anesthetized rats. Such a model-based approach does not require prior system identification, allowing for de novo experience-based learning in the brain-chip hybrid, with potential clinical advantages and limitations when compared to existing parametric ``black box'' models.

User-centered evaluation of Arizona BioPathway: an information extraction, integration, and visualization system.

PubMed

Quiñones, Karin D; Su, Hua; Marshall, Byron; Eggers, Shauna; Chen, Hsinchun

2007-09-01

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This paper presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature-viewing method. Relation aggregation significantly contributes to knowledge-acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multiview, relation-based interface that supports user-controlled exploration of pathway information across multiple granularities.

The role of technology in reducing health care costs. Final project report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sill, A.E.; Warren, S.; Dillinger, J.D.

1997-08-01

Sandia National Laboratories applied a systems approach to identifying innovative biomedical technologies with the potential to reduce U.S. health care delivery costs while maintaining care quality. This study was conducted by implementing both top-down and bottom-up strategies. The top-down approach used prosperity gaming methodology to identify future health care delivery needs. This effort provided roadmaps for the development and integration of technology to meet perceived care delivery requirements. The bottom-up approach identified and ranked interventional therapies employed in existing care delivery systems for a host of health-related conditions. Economic analysis formed the basis for development of care pathway interaction modelsmore » for two of the most pervasive, chronic disease/disability conditions: coronary artery disease (CAD) and benign prostatic hypertrophy (BPH). Societal cost-benefit relationships based on these analyses were used to evaluate the effect of emerging technology in these treatment areas. 17 figs., 48 tabs.« less

Supporting self-care in general practice

PubMed Central

Greaves, Colin J; Campbell, John L

2007-01-01

There is both a clear need and a political will to improve self-care in long-term conditions: demand for self-care support interventions is rising. This article discusses current approaches to supporting self-care in primary care, evidence in favour of self-care support, and issues for GPs to consider in planning self-care support systems. In planning care pathways, important choices need to be made about whether to use individual or group-based approaches and what intensity of intervention is appropriate to match patient needs. Investment may also be needed in both health professional competences and practice systems to optimise their ability to support patient self-care. Self-care support is a key approach for the future of UK health care. Practices that are well trained and well organised to support self-care will respond better to the complex challenges of achieving improvements in the outcomes of long-term conditions. PMID:17925140

Multi-level evaluation of Escherichia coli polyphosphate related mutants using global transcriptomic, proteomic and phenomic analyses.

PubMed

Varas, Macarena; Valdivieso, Camilo; Mauriaca, Cecilia; Ortíz-Severín, Javiera; Paradela, Alberto; Poblete-Castro, Ignacio; Cabrera, Ricardo; Chávez, Francisco P

2017-04-01

Polyphosphate (polyP) is a linear biopolymer found in all living cells. In bacteria, mutants lacking polyphosphate kinase 1 (PPK1), the enzyme responsible for synthesis of most polyP, have many structural and functional defects. However, little is known about the causes of these pleiotropic alterations. The link between ppk1 deletion and those numerous phenotypes observed can be the result of complex molecular interactions that can be elucidated via a systems biology approach. By integrating different omics levels (transcriptome, proteome and phenome), we described the functioning of various metabolic pathways among Escherichia coli polyphosphate mutant strains (Δppk1, Δppx, and ΔpolyP). Bioinformatic analyses reveal the complex metabolic and regulatory bases of the phenotypes unique to polyP mutants. Our results suggest that during polyP deficiency (Δppk1 mutant), metabolic pathways needed for energy supply are up-regulated, including fermentation, aerobic and anaerobic respiration. Transcriptomic and q-proteomic contrasting changes between Δppk1 and Δppx mutant strains were observed in those central metabolic pathways and confirmed by using Phenotypic microarrays. In addition, our results suggest a regulatory connection between polyP, second messenger metabolism, alternative Sigma/Anti-Sigma factors and type-II toxin-antitoxin (TA) systems. We suggest a broader role for polyP via regulation of ATP-dependent proteolysis of type II toxin-antitoxin system and alternative Sigma/Anti-Sigma factors, that could explain the multiple structural and functional deficiencies described due to alteration of polyP metabolism. Understanding the interplay of polyP in bacterial metabolism using a systems biology approach can help to improve design of novel antimicrobials toward pathogens. Copyright © 2017 Elsevier B.V. All rights reserved.

Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation

PubMed Central

Murabito, Ettore; Verma, Malkhey; Bekker, Martijn; Bellomo, Domenico; Westerhoff, Hans V.; Teusink, Bas; Steuer, Ralf

2014-01-01

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models. PMID:25268481

Current limitations and recommendations to improve testing for the environmental assessment of endocrine active substances

USGS Publications Warehouse

Coady, Katherine K.; Biever, Ronald C.; Denslow, Nancy D.; Gross, Melanie; Guiney, Patrick D.; Holbech, Henrik; Karouna-Renier, Natalie K.; Katsiadaki, Ioanna; Krueger, Hank; Levine, Steven L.; Maack, Gerd; Williams, Mike; Wolf, Jeffrey C.; Ankley, Gerald T.

2017-01-01

In the present study, existing regulatory frameworks and test systems for assessing potential endocrine active chemicals are described, and associated challenges are discussed, along with proposed approaches to address these challenges. Regulatory frameworks vary somewhat across geographies, but all basically evaluate whether a chemical possesses endocrine activity and whether this activity can result in adverse outcomes either to humans or to the environment. Current test systems include in silico, in vitro, and in vivo techniques focused on detecting potential endocrine activity, and in vivo tests that collect apical data to detect possible adverse effects. These test systems are currently designed to robustly assess endocrine activity and/or adverse effects in the estrogen, androgen, and thyroid hormone signaling pathways; however, there are some limitations of current test systems for evaluating endocrine hazard and risk. These limitations include a lack of certainty regarding: 1) adequately sensitive species and life stages; 2) mechanistic endpoints that are diagnostic for endocrine pathways of concern; and 3) the linkage between mechanistic responses and apical, adverse outcomes. Furthermore, some existing test methods are resource intensive with regard to time, cost, and use of animals. However, based on recent experiences, there are opportunities to improve approaches to and guidance for existing test methods and to reduce uncertainty. For example, in vitro high-throughput screening could be used to prioritize chemicals for testing and provide insights as to the most appropriate assays for characterizing hazard and risk. Other recommendations include adding endpoints for elucidating connections between mechanistic effects and adverse outcomes, identifying potentially sensitive taxa for which test methods currently do not exist, and addressing key endocrine pathways of possible concern in addition to those associated with estrogen, androgen, and thyroid signaling. 

Food sovereignty, food security and health equity: a meta-narrative mapping exercise

PubMed Central

Weiler, Anelyse M.; Hergesheimer, Chris; Brisbois, Ben; Wittman, Hannah; Yassi, Annalee; Spiegel, Jerry M.

2015-01-01

There has been growing policy interest in social justice issues related to both health and food. We sought to understand the state of knowledge on relationships between health equity—i.e. health inequalities that are socially produced—and food systems, where the concepts of ‘food security’ and ‘food sovereignty’ are prominent. We undertook exploratory scoping and mapping stages of a ‘meta-narrative synthesis’ on pathways from global food systems to health equity outcomes. The review was oriented by a conceptual framework delineating eight pathways to health (in)equity through the food system: 1—Multi-Scalar Environmental, Social Context; 2—Occupational Exposures; 3—Environmental Change; 4—Traditional Livelihoods, Cultural Continuity; 5—Intake of Contaminants; 6—Nutrition; 7—Social Determinants of Health and 8—Political, Economic and Regulatory context. The terms ‘food security’ and ‘food sovereignty’ were, respectively, paired with a series of health equity-related terms. Combinations of health equity and food security (1414 citations) greatly outnumbered pairings with food sovereignty (18 citations). Prominent crosscutting themes that were observed included climate change, biotechnology, gender, racialization, indigeneity, poverty, citizenship and HIV as well as institutional barriers to reducing health inequities in the food system. The literature indicates that food sovereignty-based approaches to health in specific contexts, such as advancing healthy school food systems, promoting soil fertility, gender equity and nutrition, and addressing structural racism, can complement the longer-term socio-political restructuring processes that health equity requires. Our conceptual model offers a useful starting point for identifying interventions with strong potential to promote health equity. A research agenda to explore project-based interventions in the food system along these pathways can support the identification of ways to strengthen both food sovereignty and health equity. PMID:25288515

Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes, and Cytokine Signaling Genes in Crohn's Disease

PubMed Central

Carbonetto, Peter; Stephens, Matthew

2013-01-01

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study. PMID:24098138

Assessing natural direct and indirect effects through multiple pathways.

PubMed

Lange, Theis; Rasmussen, Mette; Thygesen, Lau Caspar

2014-02-15

Within the fields of epidemiology, interventions research and social sciences researchers are often faced with the challenge of decomposing the effect of an exposure into different causal pathways working through defined mediator variables. The goal of such analyses is often to understand the mechanisms of the system or to suggest possible interventions. The case of a single mediator, thus implying only 2 causal pathways (direct and indirect) from exposure to outcome, has been extensively studied. By using the framework of counterfactual variables, researchers have established theoretical properties and developed powerful tools. However, in practical problems, it is not uncommon to have several distinct causal pathways from exposure to outcome operating through different mediators. In this article, we suggest a widely applicable approach to quantifying and ranking different causal pathways. The approach is an extension of the natural effect models proposed by Lange et al. (Am J Epidemiol. 2012;176(3):190-195). By allowing the analysis of distinct multiple pathways, the suggested approach adds to the capabilities of modern mediation techniques. Furthermore, the approach can be implemented using standard software, and we have included with this article implementation examples using R (R Foundation for Statistical Computing, Vienna, Austria) and Stata software (StataCorp LP, College Station, Texas).

Evaluating Effects of Marine Energy Devices on the Marine Environment - A Risk-Based and In-Water Testing Approach

NASA Astrophysics Data System (ADS)

Harker-Klimes, G.; Copping, A. E.

2016-02-01

The portfolio of emerging renewables includes generating power from offshore winds, tides, waves, and ocean currents, as well as seawater temperature and salinity differentials. These new systems are collectively known as marine renewable energy (MRE). MRE development worldwide is in the early stages of design, deployment, and commercialization. A major barrier to bringing these systems into commercial use is the need to overcome uncertainties in environmental effects that slow siting and permitting of devices. Using a risk-based approach, this paper will discuss pathways for evaluating potential effects of tidal turbines and wave energy converters (WECs) on marine animals, habitats, and ecosystem processes. Using basic biological principles and knowledge of specific MRE technologies, the Environmental Risk Evaluation System has been used to narrow pertinent risks from devices, enabling laboratory and field studies to focus on the most important interactions. These interactions, include: potential collisions and behavioral disturbances of marine mammals, fish and other organisms; effects of underwater sound on animal communication and navigation; changes in sediment transport, benthic habitats, and water quality constituents; and effects of electromagnetic fields on animals. It is then necessary to apply these findings to the projects themselves. Another uncertainty is how to measure these key interactions in high-energy locations where MRE deployment is desirable. Consequently, new systems are being developed: instrumentation, innovative platforms for deployment, and new management strategies for collecting and analyzing very large data streams. Inherent in this development pathway is the need to test, deploy, and calibrate these monitoring systems. The Triton initiative is designed to enable this development, and has initiated testing of devices in Washington State to move the MRE industry forward while protecting marine animals, habitats and processes.

The RNA world in the 21st century-a systems approach to finding non-coding keys to clinical questions.

PubMed

Schmitz, Ulf; Naderi-Meshkin, Hojjat; Gupta, Shailendra K; Wolkenhauer, Olaf; Vera, Julio

2016-05-01

There was evidence that RNAs are a functionally rich class of molecules not only since the arrival of the next-generation sequencing technology. Non-coding RNAs (ncRNA) could be the key to accelerated diagnosis and enhanced prediction of disease and therapy outcomes as well as the design of advanced therapeutic strategies to overcome yet unsatisfactory approaches.In this review, we discuss the state of the art in RNA systems biology with focus on the application in the systems biomedicine field. We propose guidelines for analysing the role of microRNAs and long non-coding RNAs in human pathologies. We introduce RNA expression profiling and network approaches for the identification of stable and effective RNomics-based biomarkers, providing insights into the role of ncRNAs in disease regulation. Towards this, we discuss ways to model the dynamics of gene regulatory networks and signalling pathways that involve ncRNAs. We also describe data resources and computational methods for finding putative mechanisms of action of ncRNAs. Finally, we discuss avenues for the computer-aided design of novel RNA-based therapeutics. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Modular and Stochastic Approaches to Molecular Pathway Models of ATM, TGF beta, and WNT Signaling

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; O'Neill, Peter; Ponomarev, Artem; Carra, Claudio; Whalen, Mary; Pluth, Janice M.

2009-01-01

Deterministic pathway models that describe the biochemical interactions of a group of related proteins, their complexes, activation through kinase, etc. are often the basis for many systems biology models. Low dose radiation effects present a unique set of challenges to these models including the importance of stochastic effects due to the nature of radiation tracks and small number of molecules activated, and the search for infrequent events that contribute to cancer risks. We have been studying models of the ATM, TGF -Smad and WNT signaling pathways with the goal of applying pathway models to the investigation of low dose radiation cancer risks. Modeling challenges include introduction of stochastic models of radiation tracks, their relationships to more than one substrate species that perturb pathways, and the identification of a representative set of enzymes that act on the dominant substrates. Because several pathways are activated concurrently by radiation the development of modular pathway approach is of interest.

Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach.

PubMed

Koutroumpas, Konstantinos; Ballarini, Paolo; Votsi, Irene; Cournède, Paul-Henry

2016-09-01

Likelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC-SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels. In this article, we employ Dirichlet process mixtures (DPMs) to design optimal transition kernels and we present an ABC-SMC algorithm with DPM kernels. We illustrate the use of the proposed methodology using real data for the canonical Wnt signaling pathway. A multi-compartment model of the pathway is developed and it is compared to an existing model. The results indicate that DPMs are more efficient in the exploration of the parameter space and can significantly improve ABC-SMC performance. In comparison to alternative sampling schemes that are commonly used, the proposed approach can bring potential benefits in the estimation of complex multimodal distributions. The method is used to estimate the parameters and the initial state of two models of the Wnt pathway and it is shown that the multi-compartment model fits better the experimental data. Python scripts for the Dirichlet Process Gaussian Mixture model and the Gibbs sampler are available at https://sites.google.com/site/kkoutroumpas/software konstantinos.koutroumpas@ecp.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Knowledge-driven genomic interactions: an application in ovarian cancer.

PubMed

Kim, Dokyoon; Li, Ruowang; Dudek, Scott M; Frase, Alex T; Pendergrass, Sarah A; Ritchie, Marylyn D

2014-01-01

Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner. Thus, we propose a novel approach for identifying knowledge-driven genomic interactions and applying it to discover models associated with cancer clinical phenotypes using grammatical evolution neural networks (GENN). In order to demonstrate the utility of the proposed approach, an ovarian cancer data from the Cancer Genome Atlas (TCGA) was used for predicting clinical stage as a pilot project. We identified knowledge-driven genomic interactions associated with cancer stage from single knowledge bases such as sources of pathway-pathway interaction, but also knowledge-driven genomic interactions across different sets of knowledge bases such as pathway-protein family interactions by integrating different types of information. Notably, an integration model from different sources of biological knowledge achieved 78.82% balanced accuracy and outperformed the top models with gene expression or single knowledge-based data types alone. Furthermore, the results from the models are more interpretable because they are framed in the context of specific biological pathways or other expert knowledge. The success of the pilot study we have presented herein will allow us to pursue further identification of models predictive of clinical cancer survival and recurrence. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different biological knowledge sources has the potential for providing more effective screening strategies and therapeutic targets for many types of cancer.

System-based approaches to decode the molecular links in Parkinson's disease and diabetes.

PubMed

Santiago, Jose A; Potashkin, Judith A

2014-12-01

A growing body of evidence indicates an increased risk for developing Parkinson's disease (PD) among people with type 2 diabetes (T2DM). The relationship between the etiology and development of both chronic diseases is beginning to be uncovered and recent studies show that PD and T2DM share remarkably similar dysregulated pathways. It has been proposed that a cascade of events including mitochondrial dysfunction, impaired insulin signaling, and metabolic inflammation trigger neurodegeneration in T2DM models. Network-based approaches have elucidated a potential molecular framework linking both diseases. Further, transcriptional signatures that modulate the neurodegenerative phenotype in T2DM have been identified. Here we contextualize the current experimental approaches to dissect the mechanisms underlying the association between PD and T2DM and discuss the existing challenges toward the understanding of the coexistence of these devastating aging diseases. Copyright © 2014 Elsevier Inc. All rights reserved.

Whole-brain structural connectivity in dyskinetic cerebral palsy and its association with motor and cognitive function.

PubMed

Ballester-Plané, Júlia; Schmidt, Ruben; Laporta-Hoyos, Olga; Junqué, Carme; Vázquez, Élida; Delgado, Ignacio; Zubiaurre-Elorza, Leire; Macaya, Alfons; Póo, Pilar; Toro, Esther; de Reus, Marcel A; van den Heuvel, Martijn P; Pueyo, Roser

2017-09-01

Dyskinetic cerebral palsy (CP) has long been associated with basal ganglia and thalamus lesions. Recent evidence further points at white matter (WM) damage. This study aims to identify altered WM pathways in dyskinetic CP from a standardized, connectome-based approach, and to assess structure-function relationship in WM pathways for clinical outcomes. Individual connectome maps of 25 subjects with dyskinetic CP and 24 healthy controls were obtained combining a structural parcellation scheme with whole-brain deterministic tractography. Graph theoretical metrics and the network-based statistic were applied to compare groups and to correlate WM state with motor and cognitive performance. Results showed a widespread reduction of WM volume in CP subjects compared to controls and a more localized decrease in degree (number of links per node) and fractional anisotropy (FA), comprising parieto-occipital regions and the hippocampus. However, supramarginal gyrus showed a significantly higher degree. At the network level, CP subjects showed a bilateral pathway with reduced FA, comprising sensorimotor, intraparietal and fronto-parietal connections. Gross and fine motor functions correlated with FA in a pathway comprising the sensorimotor system, but gross motor also correlated with prefrontal, temporal and occipital connections. Intelligence correlated with FA in a network with fronto-striatal and parieto-frontal connections, and visuoperception was related to right occipital connections. These findings demonstrate a disruption in structural brain connectivity in dyskinetic CP, revealing general involvement of posterior brain regions with relative preservation of prefrontal areas. We identified pathways in which WM integrity is related to clinical features, including but not limited to the sensorimotor system. Hum Brain Mapp 38:4594-4612, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Diagram-based Analysis of Causal Systems (DACS): elucidating inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa.

PubMed

Rehfuess, Eva A; Best, Nicky; Briggs, David J; Joffe, Mike

2013-12-06

Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa. Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings.Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality. Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong assumptions can be reversed following empirical testing. This approach is well-suited to dealing with complex systems, in particular where data are scarce.

Diagram-based Analysis of Causal Systems (DACS): elucidating inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa

PubMed Central

2013-01-01

Background Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa. Results Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings. Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality. Conclusions Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong assumptions can be reversed following empirical testing. This approach is well-suited to dealing with complex systems, in particular where data are scarce. PMID:24314302

Spatio-temporal Assessment Of The Land Use Implications Of Solar PV And Bioenergy Deployment In The UK TM Energy Model

NASA Astrophysics Data System (ADS)

Sobral Mourao, Z.; Konadu, D. D.; Skelton, S.; Lupton, R.

2015-12-01

The UK TIMES model (UKTM) succeeds the UK MARKAL as the underlying model of the UK Department of Energy and Climate Change (DECC) for long term energy system planning and policy development. It generates energy system pathways which achieve the 80% greenhouse gas (GHG) emissions reduction target by 2050, stipulated in the UK Climate Change Act (2008), at the least possible cost. Some of these pathways prescribe large-scale deployment of solar PV and indigenously sourced bioenergy, which are land intensive and could result in significant land use transitions; but would this create competition and stress for UK land use? To answer the above question, this study uses an integrated spatio-temporal modelling approach, ForeseerTM, which characterises the interdependencies between the energy and land systems by evaluating the land required under each pathways for solar PV and bioenergy, based on scenarios of a range of PV conversion efficiencies, and energy crop yield projections. The outcome is compared with availability of suitable locations for solar PV and sustainable limits of agricultural land appropriation for bioenergy production to assess potential stresses and competition with other land use services. Preliminary results show UKTM pathways could pose significant impact on the UK land use system. Bioenergy deployment could potentially compete with other land services by taking up a significant part of the available UK agricultural land thus competing directly with food production, most notably livestock production. For pathways with significant solar PV deployment, direct competition would not be focussed on the high quality land used for food crop production but rather for land used for livestock production and other ecosystem services.

Stochastic models for regulatory networks of the genetic toggle switch.

PubMed

Tian, Tianhai; Burrage, Kevin

2006-05-30

Bistability arises within a wide range of biological systems from the lambda phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Stochastic models for regulatory networks of the genetic toggle switch

PubMed Central

Tian, Tianhai; Burrage, Kevin

2006-01-01

Bistability arises within a wide range of biological systems from the λ phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks. PMID:16714385

A new glaucoma hypothesis: a role of glymphatic system dysfunction.

PubMed

Wostyn, Peter; Van Dam, Debby; Audenaert, Kurt; Killer, Hanspeter Esriel; De Deyn, Peter Paul; De Groot, Veva

2015-06-29

In a recent review article titled "A new look at cerebrospinal fluid circulation", Brinker et al. comprehensively described novel insights from molecular and cellular biology as well as neuroimaging research, which indicate that cerebrospinal fluid (CSF) physiology is much more complex than previously believed. The glymphatic system is a recently defined brain-wide paravascular pathway for CSF and interstitial fluid exchange that facilitates efficient clearance of interstitial solutes, including amyloid-β, from the brain. Although further studies are needed to substantiate the functional significance of the glymphatic concept, one implication is that glymphatic pathway dysfunction may contribute to the deficient amyloid-β clearance in Alzheimer's disease. In this paper, we review several lines of evidence suggesting that the glymphatic system may also have potential clinical relevance for the understanding of glaucoma. As a clinically acceptable MRI-based approach to evaluate glymphatic pathway function in humans has recently been developed, a unique opportunity now exists to investigate whether suppression of the glymphatic system contributes to the development of glaucoma. The observation of a dysfunctional glymphatic system in patients with glaucoma would provide support for the hypothesis recently proposed by our group that CSF circulatory dysfunction may play a contributory role in the pathogenesis of glaucomatous damage. This would suggest a new hypothesis for glaucoma, which, just like Alzheimer's disease, might be considered then as an imbalance between production and clearance of neurotoxins, including amyloid-β.

R1 autonomic nervous system in acute kidney injury.

PubMed

Hering, Dagmara; Winklewski, Pawel J

2017-02-01

Acute kidney injury (AKI) is a rapid loss of kidney function resulting in accumulation of end metabolic products and associated abnormalities in fluid, electrolyte and acid-base homeostasis. The pathophysiology of AKI is complex and multifactorial involving numerous vascular, tubular and inflammatory pathways. Neurohumoral activation with heightened activity of the sympathetic nervous system and renin-angiotensin-aldosterone system play a critical role in this scenario. Inflammation and/or local renal ischaemia are underlying mechanisms triggering renal tissue hypoxia and resultant renal microcirculation dysfunction; a common feature of AKI occurring in numerous clinical conditions leading to a high morbidity and mortality rate. The contribution of renal nerves to the pathogenesis of AKI has been extensively demonstrated in a series of experimental models over the past decades. While this has led to better knowledge of the pathogenesis of human AKI, therapeutic approaches to improve patient outcomes are scarce. Restoration of autonomic regulatory function with vagal nerve stimulation resulting in anti-inflammatory effects and modulation of centrally-mediated mechanisms could be of clinical relevance. Evidence from experimental studies suggests that a therapeutic splenic ultrasound approach may prevent AKI via activation of the cholinergic anti-inflammatory pathway. This review briefly summarizes renal nerve anatomy, basic insights into neural control of renal function in the physiological state and the involvement of the autonomic nervous system in the pathophysiology of AKI chiefly due to sepsis, cardiopulmonary bypass and ischaemia/reperfusion experimental model. Finally, potentially preventive experimental pre-clinical approaches for the treatment of AKI aimed at sympathetic inhibition and/or parasympathetic stimulation are presented. © 2016 John Wiley & Sons Australia, Ltd.

Design and analysis of metabolic pathways supporting formatotrophic growth for electricity-dependent cultivation of microbes.

PubMed

Bar-Even, Arren; Noor, Elad; Flamholz, Avi; Milo, Ron

2013-01-01

Electrosynthesis is a promising approach that enables the biological production of commodities, like fuels and fine chemicals, using renewably produced electricity. Several techniques have been proposed to mediate the transfer of electrons from the cathode to living cells. Of these, the electroproduction of formate as a mediator seems especially promising: formate is readily soluble, of low toxicity and can be produced at relatively high efficiency and at reasonable current density. While organisms that are capable of formatotrophic growth, i.e. growth on formate, exist naturally, they are generally less suitable for bulk cultivation and industrial needs. Hence, it may be helpful to engineer a model organism of industrial relevance, such as E. coli, for growth on formate. There are numerous metabolic pathways that can potentially support formatotrophic growth. Here we analyze these diverse pathways according to various criteria including biomass yield, thermodynamic favorability, chemical motive force, kinetics and the practical challenges posed by their expression. We find that the reductive glycine pathway, composed of the tetrahydrofolate system, the glycine cleavage system, serine hydroxymethyltransferase and serine deaminase, is a promising candidate to support electrosynthesis in E. coli. The approach presented here exemplifies how combining different computational approaches into a systematic analysis methodology provides assistance in redesigning metabolism. This article is part of a Special Issue entitled: Metals in Bioenergetics and Biomimetics Systems. Copyright © 2012 Elsevier B.V. All rights reserved.

Systems biology of the modified branched Entner-Doudoroff pathway in Sulfolobus solfataricus

PubMed Central

Figueiredo, Ana Sofia; Esser, Dominik; Haferkamp, Patrick; Wieloch, Patricia; Schomburg, Dietmar; Siebers, Bettina; Schaber, Jörg

2017-01-01

Sulfolobus solfataricus is a thermoacidophilic Archaeon that thrives in terrestrial hot springs (solfatares) with optimal growth at 80°C and pH 2–4. It catabolizes specific carbon sources, such as D-glucose, to pyruvate via the modified Entner-Doudoroff (ED) pathway. This pathway has two parallel branches, the semi-phosphorylative and the non-phosphorylative. However, the strategy of S.solfataricus to endure in such an extreme environment in terms of robustness and adaptation is not yet completely understood. Here, we present the first dynamic mathematical model of the ED pathway parameterized with quantitative experimental data. These data consist of enzyme activities of the branched pathway at 70°C and 80°C and of metabolomics data at the same temperatures for the wild type and for a metabolic engineered knockout of the semi-phosphorylative branch. We use the validated model to address two questions: 1. Is this system more robust to perturbations at its optimal growth temperature? 2. Is the ED robust to deletion and perturbations? We employed a systems biology approach to answer these questions and to gain further knowledge on the emergent properties of this biological system. Specifically, we applied deterministic and stochastic approaches to study the sensitivity and robustness of the system, respectively. The mathematical model we present here, shows that: 1. Steady state metabolite concentrations of the ED pathway are consistently more robust to stochastic internal perturbations at 80°C than at 70°C; 2. These metabolite concentrations are highly robust when faced with the knockout of either branch. Connected with this observation, these two branches show different properties at the level of metabolite production and flux control. These new results reveal how enzyme kinetics and metabolomics synergizes with mathematical modelling to unveil new systemic properties of the ED pathway in S.solfataricus in terms of its adaptation and robustness. PMID:28692669

The social context of carbon sequestration: considerations from a multi-scale environmental history of the Old Peanut Basin of Senegal

USGS Publications Warehouse

Tschakert, P.; Tappan, G.

2004-01-01

This paper presents the results of a multi-scale investigation of environmental change in the Old Peanut Basin of Senegal throughout the 20th century. Based on historical accounts, ethnographies, aerial photos, satellite images, field and household surveys as well as various participatory research activities with farmers in selected villages, the study attempts to make explicit layered scales of analysis, both temporally and spatially. It shows that, despite some general trends of resource degradation in the Old Peanut Basin, local farming systems have embarked on different pathways of change to adapt to their evolving environment. It also illustrates that high diversity with respect to soil fertility management exists at the farm and household level. Finally, the paper proposes a farmer-oriented approach to carbon sequestration in order to integrate recommended technical options more efficiently into the complex and dynamic livelihoods of smallholders in dryland environments. This approach includes pathway-specific land use and management options at the level of farming systems and, at the level of individual households, a basket of possible practices from which farmers can choose depending on their multiple needs, capacities, and adaptive strategies to cope with risk and uncertainty.

Nervous system regulation of the cancer genome

PubMed Central

Cole, Steven W.

2012-01-01

Genomics-based analyses have provided deep insight into the basic biology of cancer and are now clarifying the molecular pathways by which psychological and social factors can regulate tumor cell gene expression and genome evolution. This review summarizes basic and clinical research on neural and endocrine regulation of the cancer genome and its interactions with the surrounding tumor microenvironment, including the specific types of genes subject to neural and endocrine regulation, the signal transduction pathways that mediate such effects, and therapeutic approaches that might be deployed to mitigate their impact. Beta-adrenergic signaling from the sympathetic nervous system has been found to up-regulated a diverse array of genes that contribute to tumor progression and metastasis, whereas glucocorticoid-regulated genes can inhibit DNA repair and promote cancer cell survival and resistance to chemotherapy. Relationships between socio-environmental risk factors, neural and endocrine signaling to the tumor microenvironment, and transcriptional responses by cancer cells and surrounding stromal cells are providing new mechanistic insights into the social epidemiology of cancer, new therapeutic approaches for protecting the health of cancer patients, and new molecular biomarkers for assessing the impact of behavioral and pharmacologic interventions. PMID:23207104

Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics

PubMed Central

Puniya, Bhanwar Lal; Allen, Laura; Hochfelder, Colleen; Majumder, Mahbubul; Helikar, Tomáš

2016-01-01

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model’s components was perturbed under both loss-of-function and gain-of-function mutations. Using 1,300 simulations under both types of perturbations across various extracellular conditions, we identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. The most influential components under all environmental conditions were enriched with several biological processes. The inositol pathway was found as most influential under inactivating perturbations, whereas the kinase and small lung cancer pathways were identified as the most influential under activating perturbations. The most influential components were enriched with essential genes and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor-suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Finally, our approach shows a potential to identify and prioritize therapeutic targets through systemic perturbation analysis of large-scale computational models of signal transduction. Although some components of the presented computational results have been validated against independent gene expression data sets, more laboratory experiments are warranted to more comprehensively validate the presented results. PMID:26904540

Vulnerability-Based Critical Neurons, Synapses, and Pathways in the Caenorhabditis elegans Connectome

PubMed Central

Kim, Seongkyun; Kim, Hyoungkyu; Kralik, Jerald D.; Jeong, Jaeseung

2016-01-01

Determining the fundamental architectural design of complex nervous systems will lead to significant medical and technological advances. Yet it remains unclear how nervous systems evolved highly efficient networks with near optimal sharing of pathways that yet produce multiple distinct behaviors to reach the organism’s goals. To determine this, the nematode roundworm Caenorhabditis elegans is an attractive model system. Progress has been made in delineating the behavioral circuits of the C. elegans, however, many details are unclear, including the specific functions of every neuron and synapse, as well as the extent the behavioral circuits are separate and parallel versus integrative and serial. Network analysis provides a normative approach to help specify the network design. We investigated the vulnerability of the Caenorhabditis elegans connectome by performing computational experiments that (a) “attacked” 279 individual neurons and 2,990 weighted synaptic connections (composed of 6,393 chemical synapses and 890 electrical junctions) and (b) quantified the effects of each removal on global network properties that influence information processing. The analysis identified 12 critical neurons and 29 critical synapses for establishing fundamental network properties. These critical constituents were found to be control elements—i.e., those with the most influence over multiple underlying pathways. Additionally, the critical synapses formed into circuit-level pathways. These emergent pathways provide evidence for (a) the importance of backward locomotion, avoidance behavior, and social feeding behavior to the organism; (b) the potential roles of specific neurons whose functions have been unclear; and (c) both parallel and serial design elements in the connectome—i.e., specific evidence for a mixed architectural design. PMID:27540747

Sphingosine-1-Phosphate Lyase Deficient Cells as a Tool to Study Protein Lipid Interactions

PubMed Central

Gerl, Mathias J.; Bittl, Verena; Kirchner, Susanne; Sachsenheimer, Timo; Brunner, Hanna L.; Lüchtenborg, Christian; Özbalci, Cagakan; Wiedemann, Hannah; Wegehingel, Sabine; Nickel, Walter; Haberkant, Per; Schultz, Carsten; Krüger, Marcus; Brügger, Britta

2016-01-01

Cell membranes contain hundreds to thousands of individual lipid species that are of structural importance but also specifically interact with proteins. Due to their highly controlled synthesis and role in signaling events sphingolipids are an intensely studied class of lipids. In order to investigate their metabolism and to study proteins interacting with sphingolipids, metabolic labeling based on photoactivatable sphingoid bases is the most straightforward approach. In order to monitor protein-lipid-crosslink products, sphingosine derivatives containing a reporter moiety, such as a radiolabel or a clickable group, are used. In normal cells, degradation of sphingoid bases via action of the checkpoint enzyme sphingosine-1-phosphate lyase occurs at position C2-C3 of the sphingoid base and channels the resulting hexadecenal into the glycerolipid biosynthesis pathway. In case the functionalized sphingosine looses the reporter moiety during its degradation, specificity towards sphingolipid labeling is maintained. In case degradation of a sphingosine derivative does not remove either the photoactivatable or reporter group from the resulting hexadecenal, specificity towards sphingolipid labeling can be achieved by blocking sphingosine-1-phosphate lyase activity and thus preventing sphingosine derivatives to be channeled into the sphingolipid-to-glycerolipid metabolic pathway. Here we report an approach using clustered, regularly interspaced, short palindromic repeats (CRISPR)-associated nuclease Cas9 to create a sphingosine-1-phosphate lyase (SGPL1) HeLa knockout cell line to disrupt the sphingolipid-to-glycerolipid metabolic pathway. We found that the lipid and protein compositions as well as sphingolipid metabolism of SGPL1 knock-out HeLa cells only show little adaptations, which validates these cells as model systems to study transient protein-sphingolipid interactions. PMID:27100999

Evolution and Design Governing Signal Precision and Amplification in a Bacterial Chemosensory Pathway

PubMed Central

Espinosa, Leon; Baronian, Grégory; Molle, Virginie; Mauriello, Emilia M. F.; Brochier-Armanet, Céline; Mignot, Tâm

2015-01-01

Understanding the principles underlying the plasticity of signal transduction networks is fundamental to decipher the functioning of living cells. In Myxococcus xanthus, a particular chemosensory system (Frz) coordinates the activity of two separate motility systems (the A- and S-motility systems), promoting multicellular development. This unusual structure asks how signal is transduced in a branched signal transduction pathway. Using combined evolution-guided and single cell approaches, we successfully uncoupled the regulations and showed that the A-motility regulation system branched-off an existing signaling system that initially only controlled S-motility. Pathway branching emerged in part following a gene duplication event and changes in the circuit structure increasing the signaling efficiency. In the evolved pathway, the Frz histidine kinase generates a steep biphasic response to increasing external stimulations, which is essential for signal partitioning to the motility systems. We further show that this behavior results from the action of two accessory response regulator proteins that act independently to filter and amplify signals from the upstream kinase. Thus, signal amplification loops may underlie the emergence of new connectivity in signal transduction pathways. PMID:26291327

Perspectives on the Use of Clinical Pathways in Oncology Care.

PubMed

Chiang, Anne C; Ellis, Peter; Zon, Robin

2017-01-01

Pathways and guidelines are valuable tools to provide evidence-based care in oncology. Pathways may be more restrictive than guidelines because they attempt (where possible) to reduce cost, add efficiency, and remove unwarranted variability. Pathways offer an opportunity to measure, report, and improve quality of care; they can drive to evidence-based targeted therapy where appropriate; they can enhance efficiency through standardization; and, finally, they can be a vehicle to enhance participation in clinical trials. Pathway implementation requires understanding and commitment on the part of the physician and leadership as they may initially disrupt workflow, but ultimately have the ability to enhance patient care. ASCO criteria have been published for the development and implementation of high-quality oncology pathway programs. Future challenges for pathways include incorporation of molecular testing and appropriate targeted care in a real-time precision oncology approach.

Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life.

PubMed

Kaput, Jim; van Ommen, Ben; Kremer, Bas; Priami, Corrado; Monteiro, Jacqueline Pontes; Morine, Melissa; Pepping, Fre; Diaz, Zoey; Fenech, Michael; He, Yiwu; Albers, Ruud; Drevon, Christian A; Evelo, Chris T; Hancock, Robert E W; Ijsselmuiden, Carel; Lumey, L H; Minihane, Anne-Marie; Muller, Michael; Murgia, Chiara; Radonjic, Marijana; Sobral, Bruno; West, Keith P

2014-01-01

Nutrition research, like most biomedical disciplines, adopted and often uses experimental approaches based on Beadle and Tatum's one gene-one polypeptide hypothesis, thereby reducing biological processes to single reactions or pathways. Systems thinking is needed to understand the complexity of health and disease processes requiring measurements of physiological processes, as well as environmental and social factors, which may alter the expression of genetic information. Analysis of physiological processes with omics technologies to assess systems' responses has only become available over the past decade and remains costly. Studies of environmental and social conditions known to alter health are often not connected to biomedical research. While these facts are widely accepted, developing and conducting comprehensive research programs for health are often beyond financial and human resources of single research groups. We propose a new research program on essential nutrients for optimal underpinning of growth and health (ENOUGH) that will use systems approaches with more comprehensive measurements and biostatistical analysis of the many biological and environmental factors that influence undernutrition. Creating a knowledge base for nutrition and health is a necessary first step toward developing solutions targeted to different populations in diverse social and physical environments for the two billion undernourished people in developed and developing economies.

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection.

PubMed

Yoon, Jeongah; Si, Yaguang; Nolan, Ryan; Lee, Kyongbum

2007-09-15

The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism. Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism. Supplementary data are available at Bioinformatics online.

Omics/systems biology and cancer cachexia.

PubMed

Gallagher, Iain J; Jacobi, Carsten; Tardif, Nicolas; Rooyackers, Olav; Fearon, Kenneth

2016-06-01

Cancer cachexia is a complex syndrome generated by interaction between the host and tumour cells with a background of treatment effects and toxicity. The complexity of the physiological pathways likely involved in cancer cachexia necessitates a holistic view of the relevant biology. Emergent properties are characteristic of complex systems with the result that the end result is more than the sum of its parts. Recognition of the importance of emergent properties in biology led to the concept of systems biology wherein a holistic approach is taken to the biology at hand. Systems biology approaches will therefore play an important role in work to uncover key mechanisms with therapeutic potential in cancer cachexia. The 'omics' technologies provide a global view of biological systems. Genomics, transcriptomics, proteomics, lipidomics and metabolomics approaches all have application in the study of cancer cachexia to generate systems level models of the behaviour of this syndrome. The current work reviews recent applications of these technologies to muscle atrophy in general and cancer cachexia in particular with a view to progress towards integration of these approaches to better understand the pathology and potential treatment pathways in cancer cachexia. Copyright © 2016. Published by Elsevier Ltd.

NetPath: a public resource of curated signal transduction pathways

PubMed Central

2010-01-01

We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches. PMID:20067622

RNA-seq Analysis Reveals Unique Transcriptome Signatures in Systemic Lupus Erythematosus Patients with Distinct Autoantibody Specificities

PubMed Central

Rai, Richa; Chauhan, Sudhir Kumar; Singh, Vikas Vikram; Rai, Madhukar; Rai, Geeta

2016-01-01

Systemic lupus erythematosus (SLE) patients exhibit immense heterogeneity which is challenging from the diagnostic perspective. Emerging high throughput sequencing technologies have been proved to be a useful platform to understand the complex and dynamic disease processes. SLE patients categorised based on autoantibody specificities are reported to have differential immuno-regulatory mechanisms. Therefore, we performed RNA-seq analysis to identify transcriptomics of SLE patients with distinguished autoantibody specificities. The SLE patients were segregated into three subsets based on the type of autoantibodies present in their sera (anti-dsDNA+ group with anti-dsDNA autoantibody alone; anti-ENA+ group having autoantibodies against extractable nuclear antigens (ENA) only, and anti-dsDNA+ENA+ group having autoantibodies to both dsDNA and ENA). Global transcriptome profiling for each SLE patients subsets was performed using Illumina® Hiseq-2000 platform. The biological relevance of dysregulated transcripts in each SLE subsets was assessed by ingenuity pathway analysis (IPA) software. We observed that dysregulation in the transcriptome expression pattern was clearly distinct in each SLE patients subsets. IPA analysis of transcripts uniquely expressed in different SLE groups revealed specific biological pathways to be affected in each SLE subsets. Multiple cytokine signaling pathways were specifically dysregulated in anti-dsDNA+ patients whereas Interferon signaling was predominantly dysregulated in anti-ENA+ patients. In anti-dsDNA+ENA+ patients regulation of actin based motility by Rho pathway was significantly affected. The granulocyte gene signature was a common feature to all SLE subsets; however, anti-dsDNA+ group showed relatively predominant expression of these genes. Dysregulation of Plasma cell related transcripts were higher in anti-dsDNA+ and anti-ENA+ patients as compared to anti-dsDNA+ ENA+. Association of specific canonical pathways with the uniquely expressed transcripts in each SLE subgroup indicates that specific immunological disease mechanisms are operative in distinct SLE patients’ subsets. This ‘sub-grouping’ approach could further be useful for clinical evaluation of SLE patients and devising targeted therapeutics. PMID:27835693

Systematic understanding the mechanisms of vitiligo pathogenesis and its treatment by Qubaibabuqi formula.

PubMed

Pei, Tianli; Zheng, Chunli; Huang, Chao; Chen, Xuetong; Guo, Zihu; Fu, Yingxue; Liu, Jianling; Wang, Yonghua

2016-08-22

Vitiligo is a depigmentation disorder, which results in substantial cosmetic disfigurement and poses a detriment to patients' physical as well as mental. Now the molecular pathogenesis of vitiligo still remains unclear, which leads to a daunting challenge for vitiligo therapy in modern medicine. Herbal medicines, characterized by multi-compound and multi-target, have long been shown effective in treating vitiligo, but their molecular mechanisms of action also remain ambiguous. Here we proposed a systems pharmacology approach using a clinically effective herb formula as a tool to detect the molecular pathogenesis of vitiligo. This study provided an integrative analysis of active chemicals, drug targets and interacting pathways of the Uygur medicine Qubaibabuqi formula for curing Vitiligo. The results show that 56 active ingredients of Qubaibabuqi interacting with 83 therapeutic proteins were identified. And Qubaibabuqi probably participate in immunomodulation, neuromodulation and keratinocytes apoptosis inhibition in treatment of vitiligo by a synergistic/cooperative way. The drug-target network-based analysis and pathway-based analysis can provide a new approach for understanding the pathogenesis of vitiligo and uncovering the molecular mechanisms of Qubaibabuqi, which will also facilitate the application of traditional Chinese herbs in modern medicine. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Application of Signaling Pathway-Based Adverse Outcome Pathways and High Throughput Toxicokinetic-PBPK for Developmental Cardiac Malformations

EPA Science Inventory

Associating putative molecular initiating events (MIE) with downstream cell signaling pathways and modeling fetal exposure kinetics is an important challenge for integration in developmental systems toxicology. Here, we describe an integrative systems toxicology model for develop...

Mitochondrial pathways governing stress resistance, life, and death in the fungal aging model Podospora anserina.

PubMed

Osiewacz, Heinz D; Brust, Diana; Hamann, Andrea; Kunstmann, Birgit; Luce, Karin; Müller-Ohldach, Mathis; Scheckhuber, Christian Q; Servos, Jörg; Strobel, Ingmar

2010-06-01

Work from more than 50 years of research has unraveled a number of molecular pathways that are involved in controlling aging of the fungal model system Podospora anserina. Early research revealed that wild-type strain aging is linked to gross reorganization of the mitochondrial DNA. Later it was shown that aging of P. anserina does also take place, although at a slower pace, when the wild-type specific mitochondrial DNA rearrangements do not occur. Now it is clear that a network of different pathways is involved in the control of aging. Branches of these pathways appear to be connected and constitute a hierarchical system of responses. Although cross talk between the individual pathways seems to be fundamental in the coordination of the overall system, the precise underlying interactions remain to be unraveled. Such a systematic approach aims at a holistic understanding of the process of biological aging, the ultimate goal of modern systems biology.

Genetic heterogeneity in autism: From single gene to a pathway perspective.

PubMed

An, Joon Yong; Claudianos, Charles

2016-09-01

The extreme genetic heterogeneity of autism spectrum disorder (ASD) represents a major challenge. Recent advances in genetic screening and systems biology approaches have extended our knowledge of the genetic etiology of ASD. In this review, we discuss the paradigm shift from a single gene causation model to pathway perturbation model as a guide to better understand the pathophysiology of ASD. We discuss recent genetic findings obtained through next-generation sequencing (NGS) and examine various integrative analyses using systems biology and complex networks approaches that identify convergent patterns of genetic elements associated with ASD. Copyright © 2016 Elsevier Ltd. All rights reserved.

Inspiring a Broader Socio-Hydrological Negotiation Approach With Interdisciplinary Field-Based Experience

NASA Astrophysics Data System (ADS)

Massuel, S.; Riaux, J.; Molle, F.; Kuper, M.; Ogilvie, A.; Collard, A.-L.; Leduc, C.; Barreteau, O.

2018-04-01

Socio-hydrology advanced the field of hydrology by considering humans and their activities as part of the water cycle, rather than as external drivers. Models are used to infer reproducible trends in human interactions with water resources. However, defining and handling water problems in this way may restrict the scope of such modeling approaches. We propose an interdisciplinary socio-hydrological approach to overcome this limit and complement modeling approaches. It starts from concrete field-based situations, combines disciplinary as well as local knowledge on water-society relationships, with the aim of broadening the hydrocentric analysis and modeling of water systems. The paper argues that an analysis of social dynamics linked to water is highly complementary to traditional hydrological tools but requires a negotiated and contextualized interdisciplinary approach to the representation and analysis of socio-hydro systems. This reflection emerged from experience gained in the field where a water-budget modeling framework failed to adequately incorporate the multiplicity of (nonhydrological) factors that determine the volumes of withdrawals for irrigation. The pathway subsequently explored was to move away from the hydrologic view of the phenomena and, in collaboration with social scientists, to produce a shared conceptualization of a coupled human-water system through a negotiated approach. This approach changed the way hydrological research issues were addressed and limited the number of strong assumptions needed for simplification in modeling. The proposed socio-hydrological approach led to a deeper understanding of the mechanisms behind local water-related problems and to debates on the interactions between social and political decisions and the dynamics of these problems.

Quantitative proteomic analysis of milk fat globule membrane (MFGM) proteins in human and bovine colostrum and mature milk samples through iTRAQ labeling.

PubMed

Yang, Mei; Cong, Min; Peng, Xiuming; Wu, Junrui; Wu, Rina; Liu, Biao; Ye, Wenhui; Yue, Xiqing

2016-05-18

Milk fat globule membrane (MFGM) proteins have many functions. To explore the different proteomics of human and bovine MFGM, MFGM proteins were separated from human and bovine colostrum and mature milk, and analyzed by the iTRAQ proteomic approach. A total of 411 proteins were recognized and quantified. Among these, 232 kinds of differentially expressed proteins were identified. These differentially expressed proteins were analyzed based on multivariate analysis, gene ontology (GO) annotation and KEGG pathway. Biological processes involved were response to stimulus, localization, establishment of localization, and the immune system process. Cellular components engaged were the extracellular space, extracellular region parts, cell fractions, and vesicles. Molecular functions touched upon were protein binding, nucleotide binding, and enzyme inhibitor activity. The KEGG pathway analysis showed several pathways, including regulation of the actin cytoskeleton, focal adhesion, neurotrophin signaling pathway, leukocyte transendothelial migration, tight junction, complement and coagulation cascades, vascular endothelial growth factor signaling pathway, and adherens junction. These results enhance our understanding of different proteomes of human and bovine MFGM across different lactation phases, which could provide important information and potential directions for the infant milk powder and functional food industries.

Redesigning metabolism based on orthogonality principles

PubMed Central

Pandit, Aditya Vikram; Srinivasan, Shyam; Mahadevan, Radhakrishnan

2017-01-01

Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering. PMID:28555623

Advancing adverse outcome pathways for integrated toxicology and regulatory applications

EPA Science Inventory

Recent regulatory efforts in many countries have focused on a toxicological pathway-based vision for human health assessments relying on in vitro systems and predictive models to generate the toxicological data needed to evaluate chemical hazard. A pathway-based vision is equally...

Coupled near-field and far-field exposure assessment framework for chemicals in consumer products.

PubMed

Fantke, Peter; Ernstoff, Alexi S; Huang, Lei; Csiszar, Susan A; Jolliet, Olivier

2016-09-01

Humans can be exposed to chemicals in consumer products through product use and environmental emissions over the product life cycle. Exposure pathways are often complex, where chemicals can transfer directly from products to humans during use or exchange between various indoor and outdoor compartments until sub-fractions reach humans. To consistently evaluate exposure pathways along product life cycles, a flexible mass balance-based assessment framework is presented structuring multimedia chemical transfers in a matrix of direct inter-compartmental transfer fractions. By matrix inversion, we quantify cumulative multimedia transfer fractions and exposure pathway-specific product intake fractions defined as chemical mass taken in by humans per unit mass of chemical in a product. Combining product intake fractions with chemical mass in the product yields intake estimates for use in life cycle impact assessment and chemical alternatives assessment, or daily intake doses for use in risk-based assessment and high-throughput screening. Two illustrative examples of chemicals used in personal care products and flooring materials demonstrate how this matrix-based framework offers a consistent and efficient way to rapidly compare exposure pathways for adult and child users and for the general population. This framework constitutes a user-friendly approach to develop, compare and interpret multiple human exposure scenarios in a coupled system of near-field ('user' environment), far-field and human intake compartments, and helps understand the contribution of individual pathways to overall human exposure in various product application contexts to inform decisions in different science-policy fields for which exposure quantification is relevant. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A residence-time-based transport approach for the groundwater pathway in performance assessment models

NASA Astrophysics Data System (ADS)

Robinson, Bruce A.; Chu, Shaoping

2013-03-01

This paper presents the theoretical development and numerical implementation of a new modeling approach for representing the groundwater pathway in risk assessment or performance assessment model of a contaminant transport system. The model developed in the present study, called the Residence Time Distribution (RTD) Mixing Model (RTDMM), allows for an arbitrary distribution of fluid travel times to be represented, to capture the effects on the breakthrough curve of flow processes such as channelized flow and fast pathways and complex three-dimensional dispersion. Mathematical methods for constructing the model for a given RTD are derived directly from the theory of residence time distributions in flowing systems. A simple mixing model is presented, along with the basic equations required to enable an arbitrary RTD to be reproduced using the model. The practical advantages of the RTDMM include easy incorporation into a multi-realization probabilistic simulation; computational burden no more onerous than a one-dimensional model with the same number of grid cells; and straightforward implementation into available flow and transport modeling codes, enabling one to then utilize advanced transport features of that code. For example, in this study we incorporated diffusion into the stagnant fluid in the rock matrix away from the flowing fractures, using a generalized dual porosity model formulation. A suite of example calculations presented herein showed the utility of the RTDMM for the case of a radioactive decay chain, dual porosity transport and sorption.

Assessment of environmental co-benefits of energy system decarbonisation - the case of UK air quality using Remote Sensing and Model simulations

NASA Astrophysics Data System (ADS)

Sobral Mourao, Z.; Konadu, D. D.; Damoah, R.

2016-12-01

The UK has a binding obligation to reduce GHG emission by 80% (based on 1990 levels) by 2050. Meeting this target requires extensive decarbonisation of the UK energy system. Different pathways that achieve this target at the lowest system costs are being explored at different levels of policy and decisions on future energy infrastructure. Whilst benefits of decarbonisation are mainly focused on the impacts on climate change, there are other potential environmental and health impacts such as air-quality. In particular, a decrease in fossil fuel use by directly substituting current systems with low-carbon technologies could lead to significant reductions in the concentrations of SO2, NOX, CO and other atmospheric pollutants. So far, the proposed decarbonisation pathways tend to target the electricity sector first, followed by a transition in transport and heating technologies and use. However, the spatial dimension of where short term changes in the energy sector occur in relation to high density population areas is not taken into account when defining the energy transition strategies. This may lead to limited short-term improvements in air quality within urban areas, where use of fossil fuels for heating and transport is the main contribution to overall atmospheric pollutant levels. It is therefore imperative to explore decarbonisation strategies that prioritise transition in sectors of the energy system that produce immediate improvements in air quality in key regions of the UK. This study aims to use a combination of Remote Sensing observations and atmospheric chemistry/transport modelling approaches to estimate and map the atmospheric pollutants impact of the traditional approach of decarbonising electricity first compared to a slower transition in the electricity sector, but faster change in end use sectors (heating and transport). This would provide an additional standard to compare future energy system pathways beyond the traditional metrics of cost and GHG emissions reductions.

Fundamental Escherichia coli biochemical pathways for biomass and energy production: creation of overall flux states.

PubMed

Carlson, Ross; Srienc, Friedrich

2004-04-20

We have previously shown that the metabolism for most efficient cell growth can be realized by a combination of two types of elementary modes. One mode produces biomass while the second mode generates only energy. The identity of the four most efficient biomass and energy pathway pairs changes, depending on the degree of oxygen limitation. The identification of such pathway pairs for different growth conditions offers a pathway-based explanation of maintenance energy generation. For a given growth rate, experimental aerobic glucose consumption rates can be used to estimate the contribution of each pathway type to the overall metabolic flux pattern. All metabolic fluxes are then completely determined by the stoichiometries of involved pathways defining all nutrient consumption and metabolite secretion rates. We present here equations that permit computation of network fluxes on the basis of unique pathways for the case of optimal, glucose-limited Escherichia coli growth under varying levels of oxygen stress. Predicted glucose and oxygen uptake rates and some metabolite secretion rates are in remarkable agreement with experimental observations supporting the validity of the presented approach. The entire most efficient, steady-state, metabolic rate structure is explicitly defined by the developed equations without need for additional computer simulations. The approach should be generally useful for analyzing and interpreting genomic data by predicting concise, pathway-based metabolic rate structures. Copyright 2004 Wiley Periodicals, Inc.

Modular Approach to Launch Vehicle Design Based on a Common Core Element

NASA Technical Reports Server (NTRS)

Creech, Dennis M.; Threet, Grady E., Jr.; Philips, Alan D.; Waters, Eric D.; Baysinger, Mike

2010-01-01

With a heavy lift launch vehicle as the centerpiece of our nation's next exploration architecture's infrastructure, the Advanced Concepts Office at NASA's Marshall Space Flight Center initiated a study to examine the utilization of elements derived from a heavy lift launch vehicle for other potential launch vehicle applications. The premise of this study is to take a vehicle concept, which has been optimized for Lunar Exploration, and utilize the core stage with other existing or near existing stages and boosters to determine lift capabilities for alternative missions. This approach not only yields a vehicle matrix with a wide array of capabilities, but also produces an evolutionary pathway to a vehicle family based on a minimum development and production cost approach to a launch vehicle system architecture, instead of a purely performance driven approach. The upper stages and solid rocket booster selected for this study were chosen to reflect a cross-section of: modified existing assets in the form of a modified Delta IV upper stage and Castor-type boosters; potential near term launch vehicle component designs including an Ares I upper stage and 5-segment boosters; and longer lead vehicle components such as a Shuttle External Tank diameter upper stage. The results of this approach to a modular launch system are given in this paper.

Metabolic engineering of Bacillus subtilis fueled by systems biology: Recent advances and future directions.

PubMed

Liu, Yanfeng; Li, Jianghua; Du, Guocheng; Chen, Jian; Liu, Long

By combining advanced omics technology and computational modeling, systems biologists have identified and inferred thousands of regulatory events and system-wide interactions of the bacterium Bacillus subtilis, which is commonly used both in the laboratory and in industry. This dissection of the multiple layers of regulatory networks and their interactions has provided invaluable information for unraveling regulatory mechanisms and guiding metabolic engineering. In this review, we discuss recent advances in the systems biology and metabolic engineering of B. subtilis and highlight current gaps in our understanding of global metabolism and global pathway engineering in this organism. We also propose future perspectives in the systems biology of B. subtilis and suggest ways that this approach can be used to guide metabolic engineering. Specifically, although hundreds of regulatory events have been identified or inferred via systems biology approaches, systematic investigation of the functionality of these events in vivo has lagged, thereby preventing the elucidation of regulatory mechanisms and further rational pathway engineering. In metabolic engineering, ignoring the engineering of multilayer regulation hinders metabolic flux redistribution. Post-translational engineering, allosteric engineering, and dynamic pathway analyses and control will also contribute to the modulation and control of the metabolism of engineered B. subtilis, ultimately producing the desired cellular traits. We hope this review will aid metabolic engineers in making full use of available systems biology datasets and approaches for the design and perfection of microbial cell factories through global metabolism optimization. Copyright © 2016 Elsevier Inc. All rights reserved.

Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

PubMed

Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

2015-01-01

Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

Data-Derived Modeling Characterizes Plasticity of MAPK Signaling in Melanoma

PubMed Central

Bernardo-Faura, Marti; Massen, Stefan; Falk, Christine S.; Brady, Nathan R.; Eils, Roland

2014-01-01

The majority of melanomas have been shown to harbor somatic mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways, which play a major role in regulation of proliferation and survival. The prevalence of these mutations makes these kinase signal transduction pathways an attractive target for cancer therapy. However, tumors have generally shown adaptive resistance to treatment. This adaptation is achieved in melanoma through its ability to undergo neovascularization, migration and rearrangement of signaling pathways. To understand the dynamic, nonlinear behavior of signaling pathways in cancer, several computational modeling approaches have been suggested. Most of those models require that the pathway topology remains constant over the entire observation period. However, changes in topology might underlie adaptive behavior to drug treatment. To study signaling rearrangements, here we present a new approach based on Fuzzy Logic (FL) that predicts changes in network architecture over time. This adaptive modeling approach was used to investigate pathway dynamics in a newly acquired experimental dataset describing total and phosphorylated protein signaling over four days in A375 melanoma cell line exposed to different kinase inhibitors. First, a generalized strategy was established to implement a parameter-reduced FL model encoding non-linear activity of a signaling network in response to perturbation. Next, a literature-based topology was generated and parameters of the FL model were derived from the full experimental dataset. Subsequently, the temporal evolution of model performance was evaluated by leaving time-defined data points out of training. Emerging discrepancies between model predictions and experimental data at specific time points allowed the characterization of potential network rearrangement. We demonstrate that this adaptive FL modeling approach helps to enhance our mechanistic understanding of the molecular plasticity of melanoma. PMID:25188314

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering.

PubMed

Menolascina, Filippo; Bellomo, Domenico; Maiwald, Thomas; Bevilacqua, Vitoantonio; Ciminelli, Caterina; Paradiso, Angelo; Tommasi, Stefania

2009-10-15

Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments. We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification. We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine.

PubMed

Maier, Dieter; Kalus, Wenzel; Wolff, Martin; Kalko, Susana G; Roca, Josep; Marin de Mas, Igor; Turan, Nil; Cascante, Marta; Falciani, Francesco; Hernandez, Miguel; Villà-Freixa, Jordi; Losko, Sascha

2011-03-05

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

PubMed Central

2011-01-01

Background To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development. PMID:21375767

Uncovering transcription factor and microRNA risk regulatory pathways associated with osteoarthritis by network analysis.

PubMed

Song, Zhenhua; Zhang, Chi; He, Lingxiao; Sui, Yanfang; Lin, Xiafei; Pan, Jingjing

2018-06-12

Osteoarthritis (OA) is the most common form of joint disease. The development of inflammation have been considered to play a key role during the progression of OA. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, deciphering these risk regulatory pathways is critical for elucidating the mechanisms underlying OA. We constructed an OA-specific regulatory network by integrating comprehensive curated transcription and post-transcriptional resource involving transcription factor (TF) and microRNA (miRNA). To deepen our understanding of underlying molecular mechanisms of OA, we developed an integrated systems approach to identify OA-specific risk regulatory pathways. In this study, we identified 89 significantly differentially expressed genes between normal and inflamed areas of OA patients. We found the OA-specific regulatory network was a standard scale-free network with small-world properties. It significant enriched many immune response-related functions including leukocyte differentiation, myeloid differentiation and T cell activation. Finally, 141 risk regulatory pathways were identified based on OA-specific regulatory network, which contains some known regulator of OA. The risk regulatory pathways may provide clues for the etiology of OA and be a potential resource for the discovery of novel OA-associated disease genes. Copyright © 2018 Elsevier Inc. All rights reserved.

Characterization of potential driver mutations involved in human breast cancer by computational approaches

PubMed Central

Rajendran, Barani Kumar; Deng, Chu-Xia

2017-01-01

Breast cancer is the second most frequently occurring form of cancer and is also the second most lethal cancer in women worldwide. A genetic mutation is one of the key factors that alter multiple cellular regulatory pathways and drive breast cancer initiation and progression yet nature of these cancer drivers remains elusive. In this article, we have reviewed various computational perspectives and algorithms for exploring breast cancer driver mutation genes. Using both frequency based and mutational exclusivity based approaches, we identified 195 driver genes and shortlisted 63 of them as candidate drivers for breast cancer using various computational approaches. Finally, we conducted network and pathway analysis to explore their functions in breast tumorigenesis including tumor initiation, progression, and metastasis. PMID:28477017

Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease.

PubMed

Moreno-Moral, Aida; Pesce, Francesco; Behmoaras, Jacques; Petretto, Enrico

2017-01-01

Systems genetics stems from systems biology and similarly employs integrative modeling approaches to describe the perturbations and phenotypic effects observed in a complex system. However, in the case of systems genetics the main source of perturbation is naturally occurring genetic variation, which can be analyzed at the systems-level to explain the observed variation in phenotypic traits. In contrast with conventional single-variant association approaches, the success of systems genetics has been in the identification of gene networks and molecular pathways that underlie complex disease. In addition, systems genetics has proven useful in the discovery of master trans-acting genetic regulators of functional networks and pathways, which in many cases revealed unexpected gene targets for disease. Here we detail the central components of a fully integrated systems genetics approach to complex disease, starting from assessment of genetic and gene expression variation, linking DNA sequence variation to mRNA (expression QTL mapping), gene regulatory network analysis and mapping the genetic control of regulatory networks. By summarizing a few illustrative (and successful) examples, we highlight how different data-modeling strategies can be effectively integrated in a systems genetics study.

An optimization model for metabolic pathways.

PubMed

Planes, F J; Beasley, J E

2009-10-15

Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner.

A Module Analysis Approach to Investigate Molecular Mechanism of TCM Formula: A Trial on Shu-feng-jie-du Formula

PubMed Central

Zhang, Fangbo; Tang, Shihuan; Liu, Xi; Gao, Yibo; Wang, Yanping

2013-01-01

At the molecular level, it is acknowledged that a TCM formula is often a complex system, which challenges researchers to fully understand its underlying pharmacological action. However, module detection technique developed from complex network provides new insight into systematic investigation of the mode of action of a TCM formula from the molecule perspective. We here proposed a computational approach integrating the module detection technique into a 2-class heterogeneous network (2-HN) which models the complex pharmacological system of a TCM formula. This approach takes three steps: construction of a 2-HN, identification of primary pharmacological units, and pathway analysis. We employed this approach to study Shu-feng-jie-du (SHU) formula, which aimed at discovering its molecular mechanism in defending against influenza infection. Actually, four primary pharmacological units were identified from the 2-HN for SHU formula and further analysis revealed numbers of biological pathways modulated by the four pharmacological units. 24 out of 40 enriched pathways that were ranked in top 10 corresponding to each of the four pharmacological units were found to be involved in the process of influenza infection. Therefore, this approach is capable of uncovering the mode of action underlying a TCM formula via module analysis. PMID:24376467

Investigating low flow process controls, through complex modelling, in a UK chalk catchment

NASA Astrophysics Data System (ADS)

Lubega Musuuza, Jude; Wagener, Thorsten; Coxon, Gemma; Freer, Jim; Woods, Ross; Howden, Nicholas

2017-04-01

The typical streamflow response of Chalk catchments is dominated by groundwater contributions due the high degree of groundwater recharge through preferential flow pathways. The groundwater store attenuates the precipitation signal, which causes a delay between the corresponding high and low extremes in the precipitation and the stream flow signals. Streamflow responses can therefore be quite out of phase with the precipitation input to a Chalk catchment. Therefore characterising such catchment systems, including modelling approaches, clearly need to reproduce these percolation and groundwater dominated pathways to capture these dominant flow pathways. The simulation of low flow conditions for chalk catchments in numerical models is especially difficult due to the complex interactions between various processes that may not be adequately represented or resolved in the models. Periods of low stream flows are particularly important due to competing water uses in the summer, including agriculture and water supply. In this study we apply and evaluate the physically-based Pennstate Integrated Hydrologic Model (PIHM) to the River Kennet, a sub-catchment of the Thames Basin, to demonstrate how the simulations of a chalk catchment are improved by a physically-based system representation. We also use an ensemble of simulations to investigate the sensitivity of various hydrologic signatures (relevant to low flows and droughts) to the different parameters in the model, thereby inferring the levels of control exerted by the processes that the parameters represent.

Holistic systems biology approaches to molecular mechanisms of human helper T cell differentiation to functionally distinct subsets.

PubMed

Chen, Z; Lönnberg, T; Lahesmaa, R

2013-08-01

Current knowledge of helper T cell differentiation largely relies on data generated from mouse studies. To develop therapeutical strategies combating human diseases, understanding the molecular mechanisms how human naïve T cells differentiate to functionally distinct T helper (Th) subsets as well as studies on human differentiated Th cell subsets is particularly valuable. Systems biology approaches provide a holistic view of the processes of T helper differentiation, enable discovery of new factors and pathways involved and generation of new hypotheses to be tested to improve our understanding of human Th cell differentiation and immune-mediated diseases. Here, we summarize studies where high-throughput systems biology approaches have been exploited to human primary T cells. These studies reveal new factors and signalling pathways influencing T cell differentiation towards distinct subsets, important for immune regulation. Such information provides new insights into T cell biology and into targeting immune system for therapeutic interventions. © 2013 John Wiley & Sons Ltd.

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach.

PubMed

Wang, Yin-Yin; Li, Jie; Wu, Zeng-Rui; Zhang, Bo; Yang, Hong-Bin; Wang, Qin; Cai, Ying-Chun; Liu, Gui-Xia; Li, Wei-Hua; Tang, Yun

2017-05-01

An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the potential compound-target interactions (CTIs) were predicted using our substructure-drug-target network-based inference (SDTNBI) method. After screening for hepatotoxicity-related genes by assessing the symptoms of HILI, a compound-target interaction network was constructed. A scoring function, namely, Ascore, was developed to estimate the toxicity of chemicals in the liver. We conducted network analysis to determine the possible mechanisms of the biphasic effects using the analysis tools, including BiNGO, pathway enrichment, organ distribution analysis and predictions of interactions with CYP450 enzymes. Among the chemical components of PmT, 54 components with good intestinal absorption were used for analysis, and 2939 CTIs were obtained. After analyzing the mRNA expression data in the BioGPS database, 1599 CTIs and 125 targets related to liver diseases were identified. In the top 15 compounds, seven with Ascore values >3000 (emodin, quercetin, apigenin, resveratrol, gallic acid, kaempferol and luteolin) were obviously associated with hepatotoxicity. The results from the pathway enrichment analysis suggest that multiple interactions between apoptosis and metabolism may underlie PmT-induced liver injury. Many of the pathways have been verified in specific compounds, such as glutathione metabolism, cytochrome P450 metabolism, and the p53 pathway, among others. Hepatitis symptoms, the perturbation of nine bile acids and yellow or tawny urine also had corresponding pathways, justifying our method. In conclusion, this computational systems toxicology method reveals possible toxic components and could be very helpful for understanding the mechanisms of HILI. In this way, the method might also facilitate the identification of novel hepatotoxic herbs.

Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models.

PubMed

Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J

2018-03-26

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.

Systematic reconstruction of TRANSPATH data into Cell System Markup Language

PubMed Central

Nagasaki, Masao; Saito, Ayumu; Li, Chen; Jeong, Euna; Miyano, Satoru

2008-01-01

Background Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. Results We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. Conclusion By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions. PMID:18570683

Systematic reconstruction of TRANSPATH data into cell system markup language.

PubMed

Nagasaki, Masao; Saito, Ayumu; Li, Chen; Jeong, Euna; Miyano, Satoru

2008-06-23

Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.

A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network

PubMed Central

RUAN, XIYUN; LI, HONGYUN; LIU, BO; CHEN, JIE; ZHANG, SHIBAO; SUN, ZEQIANG; LIU, SHUANGQING; SUN, FAHAI; LIU, QINGYONG

2015-01-01

The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson’s correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson’s correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis. PMID:26058425

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system.

PubMed

Chen, I-Min A; Markowitz, Victor M; Palaniappan, Krishna; Szeto, Ernest; Chu, Ken; Huang, Jinghua; Ratner, Anna; Pillay, Manoj; Hadjithomas, Michalis; Huntemann, Marcel; Mikhailova, Natalia; Ovchinnikova, Galina; Ivanova, Natalia N; Kyrpides, Nikos C

2016-04-26

The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existing IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.

Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis.

PubMed

Yi, Ming; Mudunuri, Uma; Che, Anney; Stephens, Robert M

2009-06-29

One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php.

Towards a New Food System Assessment: AgMIP Coordinated Global and Regional Assessments of Climate Change

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Thorburn, Peter

2017-01-01

Agricultural stakeholders need more credible information on which to base adaptation and mitigation policy decisions. In order to provide this, we must improve the rigor of agricultural modelling. Ensemble approaches can be used to address scale issues and integrated teams can overcome disciplinary silos. The AgMIP Coordinated Global and Regional Assessments of Climate Change and Food Security (CGRA) has the goal to link agricultural systems models using common protocols and scenarios to significantly improve understanding of climate effects on crops, livestock and livelihoods across multiple scales. The AgMIP CGRA assessment brings together experts in climate, crop, livestock, economics, and food security to develop Protocols to guide the process throughout the assessment. Scenarios are designed to consistently combine elements of intertwined storylines of future society including, socioeconomic development, greenhouse gas concentrations, and specific pathways of agricultural sector development. Through these approaches, AgMIP partners around the world are providing an evidence base for their stakeholders as they make decisions and investments.

Systems Biology Model of Interactions between Tissue Growth Factors and DNA Damage Pathways: Low Dose Response and Cross-Talk in TGFβ and ATM Signaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cucinotta, Francis A

The etiology of radiation carcinogenesis has been described in terms of aberrant changes that span several levels of biological organization. Growth factors regulate many important cellular and tissue functions including apoptosis, differentiation and proliferation. A variety of genetic and epigenetic changes of growth factors have been shown to contribute to cancer initiation and progression. It is known that cellular and tissue damage to ionizing radiation is in part initiated by the production of reactive oxygen species, which can activate cytokine signaling, and the DNA damage response pathways, most notably the ATM signaling pathway. Recently, the transforming growth factor β (TGFβ)more » pathway has been shown to regulate or directly interact with the ATM pathway in the response to radiation. The relevance of this interaction with the ATM pathway is not known although p53 becomes phosphorylated and DNA damage responses are involved. However, growth factor interactions with DNA damage responses have not been elucidated particularly at low doses, and further characterization of their relationship to cancer processes is warranted. Our goal will be to use a systems biology approach to mathematically and experimentally describe the low-dose responses and cross-talk between the ATM and TGFβ pathways initiated by low- and high-LET radiation. We will characterize ATM and TGFβ signaling in epithelial and fibroblast cells using 2D models and ultimately extending to 3D organotypic cell culture models to begin to elucidate possible differences that may occur for different cell types and/or inter-cellular communication. We will investigate the roles of the Smad and Activating transcription factor 2 (ATF2) proteins as the potential major contributors to crosstalk between the TGFβ and ATM pathways, and links to cell cycle control and/or the DNA damage response, and potential differences in their responses at low and high doses. We have developed various experimental approaches to apply to these problems using confocal microscopy and flow cytometry to detail changes at low dose/dose-rate in order to understand individual cell responses, and will establish our mathematical models based on the experimental findings resulting from changes in DNA repair, apoptosis and proliferation.« less

Systems Biology Model of Interactions Between Tissue Growth Factors and DNA Damage Pathways: Low Dose Response and Cross-Talk in TGFbeta and ATM Signaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Neill, Peter; Anderson, Jennifer

The etiology of radiation carcinogenesis has been described in terms of aberrant changes that span several levels of biological organization. Growth factors regulate many important cellular and tissue functions including apoptosis, differentiation and proliferation. A variety of genetic and epigenetic changes of growth factors have been shown to contribute to cancer initiation and progression. It is known that cellular and tissue damage to ionizing radiation is in part initiated by the production of reactive oxygen species, which can activate cytokine signaling, and the DNA damage response pathways, most notably the ATM signaling pathway. Recently the transforming growth factor β (TGFβ)more » pathway has been shown to regulate or directly interact with the ATM pathway in the response to radiation. The relevance of this interaction with the ATM pathway is not known although p53 becomes phosphorylated and DNA damage responses are involved. However, growth factor interactions with DNA damage responses have not been elucidated particularly at low doses and further characterization of their relationship to cancer processes is warranted. Our goal will be to use a systems biology approach to mathematically and experimentally describe the low dose responses and cross-talk between the ATM and TGFβ pathways initiated by low and high LET radiation. We will characterize ATM and TGFβ signaling in epithelial and fibroblast cells using 2D models and ultimately extending to 3D organotypic cell culture models to begin to elucidate possible differences that may occur for different cell types and/or inter-cellular communication. We will investigate the roles of the Smad and Activating transcription factor 2 (ATF2) proteins as the potential major contributors to cross- talk between the TGFβ and ATM pathways, and links to cell cycle control and/or the DNA damage response, and potential differences in their responses at low and high doses. We have developed various experimental approaches to apply to these problems using confocal microscopy and flow cytometry to detail changes at low dose/dose-rate in order to understand individual cell responses, and will establish our mathematical models based on the experimental findings resulting from changes in DNA repair, apoptosis and proliferation.« less

Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

PubMed Central

2014-01-01

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. PMID:25374614

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells.

PubMed

Cavill, Rachel; Kamburov, Atanas; Ellis, James K; Athersuch, Toby J; Blagrove, Marcus S C; Herwig, Ralf; Ebbels, Timothy M D; Keun, Hector C

2011-03-01

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

IGF system targeted therapy: Therapeutic opportunities for ovarian cancer.

PubMed

Liefers-Visser, J A L; Meijering, R A M; Reyners, A K L; van der Zee, A G J; de Jong, S

2017-11-01

The insulin-like growth factor (IGF) system comprises multiple growth factor receptors, including insulin-like growth factor 1 receptor (IGF-1R), insulin receptor (IR) -A and -B. These receptors are activated upon binding to their respective growth factor ligands, IGF-I, IGF-II and insulin, and play an important role in development, maintenance, progression, survival and chemotherapeutic response of ovarian cancer. In many pre-clinical studies anti-IGF-1R/IR targeted strategies proved effective in reducing growth of ovarian cancer models. In addition, anti-IGF-1R targeted strategies potentiated the efficacy of platinum based chemotherapy. Despite the vast amount of encouraging and promising pre-clinical data, anti-IGF-1R/IR targeted strategies lacked efficacy in the clinic. The question is whether targeting the IGF-1R/IR signaling pathway still holds therapeutic potential. In this review we address the complexity of the IGF-1R/IR signaling pathway, including receptor heterodimerization within and outside the IGF system and downstream signaling. Further, we discuss the implications of this complexity on current targeted strategies and indicate therapeutic opportunities for successful targeting of the IGF-1R/IR signaling pathway in ovarian cancer. Multiple-targeted approaches circumventing bidirectional receptor tyrosine kinase (RTK) compensation and prevention of system rewiring are expected to have more therapeutic potential. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Targeting disease through novel pathways of apoptosis and autophagy.

PubMed

Maiese, Kenneth; Chong, Zhao Zhong; Shang, Yan Chen; Wang, Shaohui

2012-12-01

Apoptosis and autophagy impact cell death in multiple systems of the body. Development of new therapeutic strategies that target these processes must address their complex role during developmental cell growth as well as during the modulation of toxic cellular environments. Novel signaling pathways involving Wnt1-inducible signaling pathway protein 1 (WISP1), phosphoinositide 3-kinase (PI3K), protein kinase B (Akt), β-catenin and mammalian target of rapamycin (mTOR) govern apoptotic and autophagic pathways during oxidant stress that affect the course of a broad spectrum of disease entities including Alzheimer's disease, Parkinson's disease, myocardial injury, skeletal system trauma, immune system dysfunction and cancer progression. Implications of potential biological and clinical outcome for these signaling pathways are presented. The CCN family member WISP1 and its intimate relationship with canonical and non-canonical wingless signaling pathways of PI3K, Akt1, β-catenin and mTOR offer an exciting approach for governing the pathways of apoptosis and autophagy especially in clinical disorders that are currently without effective treatments. Future studies that can elucidate the intricate role of these cytoprotective pathways during apoptosis and autophagy can further the successful translation and development of these cellular targets into robust and safe clinical therapeutic strategies.

S-aryl-L-cysteine sulphoxides and related organosulphur compounds alter oral biofilm development and AI-2-based cell-cell communication.

PubMed

Kasper, S H; Samarian, D; Jadhav, A P; Rickard, A H; Musah, R A; Cady, N C

2014-11-01

To design and synthesize a library of structurally related, small molecules related to homologues of compounds produced by the plant Petiveria alliacea and determine their ability to interfere with AI-2 cell-cell communication and biofilm formation by oral bacteria. Many human diseases are associated with persistent bacterial biofilms. Oral biofilms (dental plaque) are problematic as they are often associated with tooth decay, periodontal disease and systemic disorders such as heart disease and diabetes. Using a microplate-based approach, a bio-inspired small molecule library was screened for anti-biofilm activity against the oral species Streptococcus mutans UA159, Streptococcus sanguis 10556 and Actinomyces oris MG1. To complement the static screen, a flow-based BioFlux microfluidic system screen was also performed under conditions representative of the human oral cavity. Several compounds were found to display biofilm inhibitory activity in all three of the oral bacteria tested. These compounds were also shown to inhibit bioluminescence by Vibrio harveyi and were thus inferred to be quorum sensing (QS) inhibitors. Due to the structural similarity of these compounds to each other, and to key molecules in AI-2 biosynthetic pathways, we propose that these molecules potentially reduce biofilm formation via antagonism of QS or QS-related pathways. This study highlights the potential for a non-antimicrobial-based strategy, focused on AI-2 cell-cell signalling, to control the development of dental plaque. Considering that many bacterial species use AI-2 cell-cell signalling, as well as the increased concern of the use of antimicrobials in healthcare products, such an anti-biofilm approach could also be used to control biofilms in environments beyond the human oral cavity. © 2014 The Society for Applied Microbiology.

Hybrid stochastic simulation of reaction-diffusion systems with slow and fast dynamics.

PubMed

Strehl, Robert; Ilie, Silvana

2015-12-21

In this paper, we present a novel hybrid method to simulate discrete stochastic reaction-diffusion models arising in biochemical signaling pathways. We study moderately stiff systems, for which we can partition each reaction or diffusion channel into either a slow or fast subset, based on its propensity. Numerical approaches missing this distinction are often limited with respect to computational run time or approximation quality. We design an approximate scheme that remedies these pitfalls by using a new blending strategy of the well-established inhomogeneous stochastic simulation algorithm and the tau-leaping simulation method. The advantages of our hybrid simulation algorithm are demonstrated on three benchmarking systems, with special focus on approximation accuracy and efficiency.

Semirational Approach for Ultrahigh Poly(3-hydroxybutyrate) Accumulation in Escherichia coli by Combining One-Step Library Construction and High-Throughput Screening.

PubMed

Li, Teng; Ye, Jianwen; Shen, Rui; Zong, Yeqing; Zhao, Xuejin; Lou, Chunbo; Chen, Guo-Qiang

2016-11-18

As a product of a multistep enzymatic reaction, accumulation of poly(3-hydroxybutyrate) (PHB) in Escherichia coli (E. coli) can be achieved by overexpression of the PHB synthesis pathway from a native producer involving three genes phbC, phbA, and phbB. Pathway optimization by adjusting expression levels of the three genes can influence properties of the final product. Here, we reported a semirational approach for highly efficient PHB pathway optimization in E. coli based on a phbCAB operon cloned from the native producer Ralstonia entropha (R. entropha). Rationally designed ribosomal binding site (RBS) libraries with defined strengths for each of the three genes were constructed based on high or low copy number plasmids in a one-pot reaction by an oligo-linker mediated assembly (OLMA) method. Strains with desired properties were evaluated and selected by three different methodologies, including visual selection, high-throughput screening, and detailed in-depth analysis. Applying this approach, strains accumulating 0%-92% PHB contents in cell dry weight (CDW) were achieved. PHB with various weight-average molecular weights (M w ) of 2.7-6.8 × 10 6 were also efficiently produced in relatively high contents. These results suggest that the semirational approach combining library design, construction, and proper screening is an efficient way to optimize PHB and other multienzyme pathways.

Yogic exercises and health--a psycho-neuro immunological approach.

PubMed

Kulkarni, D D; Bera, T K

2009-01-01

Relaxation potential of yogic exercises seems to play a vital role in establishing psycho-physical health in reversing the psycho-immunology of emotions under stress based on breath and body awareness. However, mechanism of yogic exercises for restoring health and fitness components operating through psycho-neuro-immunological pathways is unknown. Therefore, a hybrid model of human information processing-psycho-neuroendocrine (HIP-PNE) network has been proposed to reveal the importance of yogic information processing. This study focuses on two major pathways of information processing involving cortical and hypothalamo-pituitary-adrenal axis (HPA) interactions with a deep reach molecular action on cellular, neuro-humoral and immune system in reversing stress mediated diseases. Further, the proposed HIP-PNE model has ample of experimental potential for objective evaluation of yogic view of health and fitness.

Dance between biology, mechanics, and structure: A systems-based approach to developing osteoarthritis prevention strategies.

PubMed

Chu, Constance R; Andriacchi, Thomas P

2015-07-01

Osteoarthritis (OA) is a leading cause of human suffering and disability for which disease-modifying treatments are lacking. OA occurs through complex and dynamic interplays between diverse factors over long periods of time. The traditional research and clinical focus on OA, the end stage disease, obscured understanding pathogenesis prior to reaching a common pathway defined by pain and functional deficits, joint deformity, and radiographic changes. To emphasize disease modification and prevention, we describe a multi-disciplinary systems-based approach encompassing biology, mechanics, and structure to define pre-osteoarthritic disease processes. Central to application of this model is the concept of "pre-osteoarthritis," conditions where clinical OA has not yet developed. Rather, joint homeostasis has been compromised and there are potentially reversible markers for heightened OA risk. Key messages from this perspective are (i) to focus research onto defining pre-OA through identifying and validating biological, mechanical, and imaging markers of OA risk, (ii) to emphasize multi-disciplinary approaches, and (iii) to propose that developing personalized interventions to address reversible markers of OA risk in healthy joints may be the key to prevention. Ultimately, a systems-based analysis of OA pathogenesis shows potential to transform clinical practice by facilitating development and testing of new strategies to prevent or delay the onset of osteoarthritis. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Axonal degeneration in Alzheimer’s disease: When signaling abnormalities meet the axonal transport system

PubMed Central

Kanaan, Nicholas M.; Pigino, Gustavo F.; Brady, Scott T.; Lazarov, Orly; Binder, Lester I.; Morfini, Gerardo A.

2012-01-01

Alzheimer’s disease (AD) is characterized by progressive, age-dependent degeneration of neurons in the central nervous system. A large body of evidence indicates that neurons affected in AD follow a dying-back pattern of degeneration, where abnormalities in synaptic function and axonal connectivity long precede somatic cell death. Mechanisms underlying dying-back degeneration of neurons in AD remain elusive but several have been proposed, including deficits in fast axonal transport (FAT). Accordingly, genetic evidence linked alterations in FAT to dying-back degeneration of neurons, and FAT defects have been widely documented in various AD models. In light of these findings, we discuss experimental evidence linking several AD-related pathogenic polypeptides to aberrant activation of signaling pathways involved in the phosphoregulation of microtubule-based motor proteins. While each pathway appears to affect FAT in a unique manner, in the context of AD, many of these pathways might work synergistically to compromise the delivery of molecular components critical for the maintenance and function of synapses and axons. Therapeutic approaches aimed at preventing FAT deficits by normalizing the activity of specific protein kinases may help prevent degeneration of vulnerable neurons in AD. PMID:22721767

Hydrogeologic characterization of an arid zone Radioactive Waste Management Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ginanni, J.M.; O`Neill, L.J.; Hammermeister, D.P.

1994-06-01

An in-depth subsurface site characterization and monitoring program for the soil water migration pathway has been planned, implemented, and completed to satisfy data requirements for a waiver from groundwater monitoring, for an exemption from liner leachate collections systems, and for different regulatory driven performance assessments. A traditional scientific approach has been taken to focus characterization and monitoring efforts. This involved developing a conceptual model of the hydrogeologic system and defining and testing hypotheses about this model. Specific hypotheses tested included: that the system was hydrologically heterogenous and anisotropic, and that recharge was very low or negligible. Mineralogical, physical, and hydrologicmore » data collected to test hypotheses has shown the hydrologic system to be remarkably homogenous and isotropic rather than heterogenous and anisotropic. Both hydrodynamic and environmental tracer approaches for estimating recharge have led to the conclusion that recharge from the Area 5 RWMS is not occurring in the upper region of the vadose zone, and that recharge at depth is extremely small or negligible. This demonstration of ``no migration of hazardous constituents to the water table satisfies a key requirement for both the groundwater monitoring waiver and the exemption from liner leachate collection systems. Data obtained from testing hypotheses concerning the soil water migration pathway have been used to refine the conceptual model of the hydrogeologic system of the site. These data suggest that the soil gas and atmospheric air pathways may be more important for transporting contaminants to the accessible environment than the soil water pathway. New hypotheses have been developed about these pathways, and characterization and monitoring activities designed to collect data to test these hypotheses.« less

Identification of core pathways based on attractor and crosstalk in ischemic stroke.

PubMed

Diao, Xiufang; Liu, Aijuan

2018-02-01

Ischemic stroke is a leading cause of mortality and disability around the world. It is an important task to identify dysregulated pathways which infer molecular and functional insights existing in high-throughput experimental data. Gene expression profile of E-GEOD-16561 was collected. Pathways were obtained from the database of Kyoto Encyclopedia of Genes and Genomes and Retrieval of Interacting Genes was used to download protein-protein interaction sets. Attractor and crosstalk approaches were applied to screen dysregulated pathways. A total of 20 differentially expressed genes were identified in ischemic stroke. Thirty-nine significant differential pathways were identified according to P<0.01 and 28 pathways were identified with RP<0.01 and 17 pathways were identified with impact factor >250. On the basis of the three criteria, 11 significant dysfunctional pathways were identified. Among them, Epstein-Barr virus infection was the most significant differential pathway. In conclusion, with the method based on attractor and crosstalk, significantly dysfunctional pathways were identified. These pathways are expected to provide molecular mechanism of ischemic stroke and represents a novel potential therapeutic target for ischemic stroke treatment.

Integrative Analysis of Transgenic Alfalfa (Medicago sativa L.) Suggests New Metabolic Control Mechanisms for Monolignol Biosynthesis

PubMed Central

Lee, Yun; Chen, Fang; Gallego-Giraldo, Lina; Dixon, Richard A.; Voit, Eberhard O.

2011-01-01

The entanglement of lignin polymers with cellulose and hemicellulose in plant cell walls is a major biological barrier to the economically viable production of biofuels from woody biomass. Recent efforts of reducing this recalcitrance with transgenic techniques have been showing promise for ameliorating or even obviating the need for costly pretreatments that are otherwise required to remove lignin from cellulose and hemicelluloses. At the same time, genetic manipulations of lignin biosynthetic enzymes have sometimes yielded unforeseen consequences on lignin composition, thus raising the question of whether the current understanding of the pathway is indeed correct. To address this question systemically, we developed and applied a novel modeling approach that, instead of analyzing the pathway within a single target context, permits a comprehensive, simultaneous investigation of different datasets in wild type and transgenic plants. Specifically, the proposed approach combines static flux-based analysis with a Monte Carlo simulation in which very many randomly chosen sets of parameter values are evaluated against kinetic models of lignin biosynthesis in different stem internodes of wild type and lignin-modified alfalfa plants. In addition to four new postulates that address the reversibility of some key reactions, the modeling effort led to two novel postulates regarding the control of the lignin biosynthetic pathway. The first posits functionally independent pathways toward the synthesis of different lignin monomers, while the second postulate proposes a novel feedforward regulatory mechanism. Subsequent laboratory experiments have identified the signaling molecule salicylic acid as a potential mediator of the postulated control mechanism. Overall, the results demonstrate that mathematical modeling can be a valuable complement to conventional transgenic approaches and that it can provide biological insights that are otherwise difficult to obtain. PMID:21625579

A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI

PubMed Central

Stawicki, Piotr; Gembler, Felix; Rezeika, Aya; Volosyak, Ivan

2017-01-01

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques. PMID:28379187

A network pharmacology approach to determine the synergetic mechanisms of herb couple for treating rheumatic arthritis.

PubMed

Xu, Xi-Xi; Bi, Jian-Ping; Ping, Li; Li, Ping; Li, Fei

2018-01-01

The purpose of this study was to investigate the therapeutic mechanism(s) of Clematis chinensis Osbeck/ Notopterygium incisum K.C. Ting ex H.T (CN). A network pharmacology approach integrating prediction of ingredients, target exploration, network construction, module partition and pathway analysis was used. This approach successfully helped to identify 12 active ingredients of CN, interacting with 13 key targets (Akt1, STAT3, TNFsf13, TP53, EPHB2, IL-10, IL-6, TNF, MAPK8, IL-8, RELA, ROS1 and STAT4). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that CN-regulated pathways were mainly classified into signal transduction and immune system. The present work may help to illustrate the mechanism(s) of action of CN, and it may provide a better understanding of antirheumatic effects.

Equitable Access by Design. A Conceptual Framework for Integrated Student Supports within Linked Learning Pathways

ERIC Educational Resources Information Center

de Velasco, Jorge Ruiz; Newman, Elizabeth; Borsato, Graciela

2016-01-01

This report proposes a conceptual framework for defining and implementing a system of integrated student supports that provides equitable access to college and career readiness via Linked Learning pathways in high schools. The framework emphasizes the central commitment of the Linked Learning approach to challenge prevailing norms of…

Single-Molecule Methods for Nucleotide Excision Repair: Building a System to Watch Repair in Real Time.

PubMed

Kong, Muwen; Beckwitt, Emily C; Springall, Luke; Kad, Neil M; Van Houten, Bennett

2017-01-01

Single-molecule approaches to solving biophysical problems are powerful tools that allow static and dynamic real-time observations of specific molecular interactions of interest in the absence of ensemble-averaging effects. Here, we provide detailed protocols for building an experimental system that employs atomic force microscopy and a single-molecule DNA tightrope assay based on oblique angle illumination fluorescence microscopy. Together with approaches for engineering site-specific lesions into DNA substrates, these complementary biophysical techniques are well suited for investigating protein-DNA interactions that involve target-specific DNA-binding proteins, such as those engaged in a variety of DNA repair pathways. In this chapter, we demonstrate the utility of the platform by applying these techniques in the studies of proteins participating in nucleotide excision repair. © 2017 Elsevier Inc. All rights reserved.

Using Multiorder Time-Correlation Functions (TCFs) To Elucidate Biomolecular Reaction Pathways from Microsecond Single-Molecule Fluorescence Experiments.

PubMed

Phelps, Carey; Israels, Brett; Marsh, Morgan C; von Hippel, Peter H; Marcus, Andrew H

2016-12-29

Recent advances in single-molecule fluorescence imaging have made it possible to perform measurements on microsecond time scales. Such experiments have the potential to reveal detailed information about the conformational changes in biological macromolecules, including the reaction pathways and dynamics of the rearrangements involved in processes, such as sequence-specific DNA "breathing" and the assembly of protein-nucleic acid complexes. Because microsecond-resolved single-molecule trajectories often involve "sparse" data, that is, they contain relatively few data points per unit time, they cannot be easily analyzed using the standard protocols that were developed for single-molecule experiments carried out with tens-of-millisecond time resolution and high "data density." Here, we describe a generalized approach, based on time-correlation functions, to obtain kinetic information from microsecond-resolved single-molecule fluorescence measurements. This approach can be used to identify short-lived intermediates that lie on reaction pathways connecting relatively long-lived reactant and product states. As a concrete illustration of the potential of this methodology for analyzing specific macromolecular systems, we accompany the theoretical presentation with the description of a specific biologically relevant example drawn from studies of reaction mechanisms of the assembly of the single-stranded DNA binding protein of the T4 bacteriophage replication complex onto a model DNA replication fork.

Systems pharmacology-based drug discovery for marine resources: an example using sea cucumber (Holothurians).

PubMed

Guo, Yingying; Ding, Yan; Xu, Feifei; Liu, Baoyue; Kou, Zinong; Xiao, Wei; Zhu, Jingbo

2015-05-13

Sea cucumber, a kind of marine animal, have long been utilized as tonic and traditional remedies in the Middle East and Asia because of its effectiveness against hypertension, asthma, rheumatism, cuts and burns, impotence, and constipation. In this study, an overall study performed on sea cucumber was used as an example to show drug discovery from marine resource by using systems pharmacology model. The value of marine natural resources has been extensively considered because these resources can be potentially used to treat and prevent human diseases. However, the discovery of drugs from oceans is difficult, because of complex environments in terms of composition and active mechanisms. Thus, a comprehensive systems approach which could discover active constituents and their targets from marine resource, understand the biological basis for their pharmacological properties is necessary. In this study, a feasible pharmacological model based on systems pharmacology was established to investigate marine medicine by incorporating active compound screening, target identification, and network and pathway analysis. As a result, 106 candidate components of sea cucumber and 26 potential targets were identified. Furthermore, the functions of sea cucumber in health improvement and disease treatment were elucidated in a holistic way based on the established compound-target and target-disease networks, and incorporated pathways. This study established a novel strategy that could be used to explore specific active mechanisms and discover new drugs from marine sources. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The Global Food System as a Transport Pathway for Hazardous Chemicals: The Missing Link between Emissions and Exposure

PubMed Central

Ng, Carla A.; von Goetz, Natalie

2016-01-01

Background: Food is a major pathway for human exposure to hazardous chemicals. The modern food system is becoming increasingly complex and globalized, but models for food-borne exposure typically assume locally derived diets or use concentrations directly measured in foods without accounting for food origin. Such approaches may not reflect actual chemical intakes because concentrations depend on food origin, and representative analysis is seldom available. Processing, packaging, storage, and transportation also impart different chemicals to food and are not yet adequately addressed. Thus, the link between environmental emissions and realistic human exposure is effectively broken. Objectives: We discuss the need for a fully integrated treatment of the modern industrialized food system, and we propose strategies for using existing models and relevant supporting data sources to track chemicals during production, processing, packaging, storage, and transport. Discussion: Fate and bioaccumulation models describe how chemicals distribute in the environment and accumulate through local food webs. Human exposure models can use concentrations in food to determine body burdens based on individual or population characteristics. New models now include the impacts of processing and packaging but are far from comprehensive. We propose to close the gap between emissions and exposure by utilizing a wider variety of models and data sources, including global food trade data, processing, and packaging models. Conclusions: A comprehensive approach that takes into account the complexity of the modern global food system is essential to enable better prediction of human exposure to chemicals in food, sound risk assessments, and more focused risk abatement strategies. Citation: Ng CA, von Goetz N. 2017. The global food system as a transport pathway for hazardous chemicals: the missing link between emissions and exposure. Environ Health Perspect 125:1–7; http://dx.doi.org/10.1289/EHP168 PMID:27384039

Electronic Implementation of Integrated End-of-life Care: A Local Approach

PubMed Central

Schlieper, Daniel; Altreuther, Christiane; Schallenburger, Manuela; Neukirchen, Martin; Schmitz, Andrea

2017-01-01

Introduction: The Liverpool Care Pathway for the Dying Patient is an instrument to deliver integrated care for patients in their last hours of life. Originally a paper-based system, this study investigates the feasibility of an electronic version. Methods: An electronic Liverpool Care Pathway was implemented in a specialized palliative care unit of a German university hospital. Its use is exemplified by means of auditing and analysis of the proportion of recorded items. Results: In the years 2013 and 2014 the electronic Liverpool Care Pathway was used for the care of 159 patients. The uptake of the instrument was high (67%). Most items were recorded. Apart from a high usability, the fast data retrieval allows fast analysis for auditing and research. Conclusions and discussion: The electronic instrument is feasible in a computerized ward and has strong advantages for retrospective analysis. Trial registration: Internal Clinical Trial Register of the Medical Faculty, Heinrich Heine University Düsseldorf, No. 2015124683 (7 December 2015). PMID:28970746

Existence of Inverted Profile in Chemically Responsive Molecular Pathways in the Zebrafish Liver

PubMed Central

Zhang, Xun; Li, Hu; Ma, Jing; Zhang, Louxin; Li, Baowen; Gong, Zhiyuan

2011-01-01

How a living organism maintains its healthy equilibrium in response to endless exposure of potentially harmful chemicals is an important question in current biology. By transcriptomic analysis of zebrafish livers treated by various chemicals, we defined hubs as molecular pathways that are frequently perturbed by chemicals and have high degree of functional connectivity to other pathways. Our network analysis revealed that these hubs were organized into two groups showing inverted functionality with each other. Intriguingly, the inverted activity profiles in these two groups of hubs were observed to associate only with toxicopathological states but not with physiological changes. Furthermore, these inverted profiles were also present in rat, mouse, and human under certain toxicopathological conditions. Thus, toxicopathological-associated anti-correlated profiles in hubs not only indicate their potential use in diagnosis but also development of systems-based therapeutics to modulate gene expression by chemical approach in order to rewire the deregulated activities of hubs back to normal physiology. PMID:22140468

Automatic Concept-Based Query Expansion Using Term Relational Pathways Built from a Collection-Specific Association Thesaurus

ERIC Educational Resources Information Center

Lyall-Wilson, Jennifer Rae

2013-01-01

The dissertation research explores an approach to automatic concept-based query expansion to improve search engine performance. It uses a network-based approach for identifying the concept represented by the user's query and is founded on the idea that a collection-specific association thesaurus can be used to create a reasonable representation of…

Reconstructing biochemical pathways from time course data.

PubMed

Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago

2007-03-01

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers

PubMed Central

2013-01-01

Background Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer. Results We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis. Conclusions In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://compbio.cs.umn.edu/NetPathID/. PMID:23822816

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

PubMed

Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut

2017-11-13

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Attenuating the Systemic Inflammatory Response to Adult Cardiopulmonary Bypass: A Critical Review of the Evidence Base

PubMed Central

Landis, R. Clive; Brown, Jeremiah R.; Fitzgerald, David; Likosky, Donald S.; Shore-Lesserson, Linda; Baker, Robert A.; Hammon, John W.

2014-01-01

Abstract: A wide range of pharmacological, surgical, and mechanical pump approaches have been studied to attenuate the systemic inflammatory response to cardiopulmonary bypass, yet no systematically based review exists to cover the scope of anti-inflammatory interventions deployed. We therefore conducted an evidence-based review to capture “self-identified” anti-inflammatory interventions among adult cardiopulmonary bypass procedures. To be included, trials had to measure at least one inflammatory mediator and one clinical outcome, specified in the “Outcomes 2010” consensus statement. Ninety-eight papers satisfied inclusion criteria and formed the basis of the review. The review identified 33 different interventions and approaches to attenuate the systemic inflammatory response. However, only a minority of papers (35 of 98 [35.7%]) demonstrated any clinical improvement to one or more of the predefined outcome measures (most frequently myocardial protection or length of intensive care unit stay). No single intervention was supported by strong level A evidence (multiple randomized controlled trials [RCTs] or meta-analysis) for clinical benefit. Interventions at level A evidence included off-pump surgery, minimized circuits, biocompatible circuit coatings, leukocyte filtration, complement C5 inhibition, preoperative aspirin, and corticosteroid prophylaxis. Interventions at level B evidence (single RCT) for minimizing inflammation included nitric oxide donors, C1 esterase inhibition, neutrophil elastase inhibition, propofol, propionyl-L-carnitine, and intensive insulin therapy. A secondary analysis revealed that suppression of at least one inflammatory marker was necessary but not sufficient to confer clinical benefit. The most effective interventions were those that targeted multiple inflammatory pathways. These observations are consistent with a “multiple hit” hypothesis, whereby clinically effective suppression of the systemic inflammatory response requires hitting multiple inflammatory targets simultaneously. Further research is warranted to evaluate if combinations of interventions that target multiple inflammatory pathways are capable of synergistically reducing inflammation and improving outcomes after cardiopulmonary bypass. PMID:26357785

The Need for Integrated Approaches in Metabolic Engineering.

PubMed

Lechner, Anna; Brunk, Elizabeth; Keasling, Jay D

2016-11-01

This review highlights state-of-the-art procedures for heterologous small-molecule biosynthesis, the associated bottlenecks, and new strategies that have the potential to accelerate future accomplishments in metabolic engineering. We emphasize that a combination of different approaches over multiple time and size scales must be considered for successful pathway engineering in a heterologous host. We have classified these optimization procedures based on the "system" that is being manipulated: transcriptome, translatome, proteome, or reactome. By bridging multiple disciplines, including molecular biology, biochemistry, biophysics, and computational sciences, we can create an integral framework for the discovery and implementation of novel biosynthetic production routes. Copyright © 2016 Cold Spring Harbor Laboratory Press; all rights reserved.

Tiered Approaches to Incorporate the Adverse Outcome Pathway Framework into Chemical-Specific Risk-Based Decision Making

EPA Science Inventory

The concept of Adverse Outcome Pathways (AOPs) arose as a means of addressing the challenges associated with establishing relationships between high-throughout (HT) in vitro dose response data and in vivo biological outcomes. However, AOP development has also been met with challe...

Proposal for an integrated evaluation model for the study of whole systems health care in cancer.

PubMed

Jonas, Wayne B; Beckner, William; Coulter, Ian

2006-12-01

For more than 200 years, biomedicine has approached the treatment of disease by studying disease processes (patho-genesis), inferring causal connections and developing specific approaches for therapeutically interfering with those processes. This pathogenic approach has been highly successful in acute and traumatic disease but less successful in chronic disease, primarily because of the complex, multi-factorial nature of most chronic disease, which does not allow for simple causal inference or for simple therapeutic interventions. This article suggests that chronic disease is best approached by enhancing healing processes (salutogenesis) as a whole system. Because of the nature of complex systems in chronic disease, an evaluation model based on integrative medicine is felt to be more appropriate than a disease model. The authors propose and describe an integrated model for the evaluation of healing (IMEH) that collects multilevel "thick case" observational data in assessing complex practices for chronic disease. If successful, this approach could become a blueprint for studying healing capacity in whole medical systems, including complementary medicine, traditional medicine, and conventional primary care. In addition, streamlining data collection and applying rapid informatics management might allow for such data to be used in guiding clinical practice. The IMEH involves collection, integration, and potentially feedback of relevant variables in the following areas: (1) sociocultural, (2) psychological and behavioral, (3) clinical (diagnosis based), and (4) biological. Evaluation and integration of these components would involve specialized research teams that feed their data into a single data management and information analysis center. These data can then be subjected to descriptive and pathway analysis providing "bench and bedside" information.

Spreaders and Sponges define metastasis in lung cancer: A Markov chain Monte Carlo Mathematical Model

PubMed Central

Newton, Paul K.; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Norton, Larry; Kuhn, Peter

2013-01-01

The classic view of metastatic cancer progression is that it is a unidirectional process initiated at the primary tumor site, progressing to variably distant metastatic sites in a fairly predictable, though not perfectly understood, fashion. A Markov chain Monte Carlo mathematical approach can determine a pathway diagram that classifies metastatic tumors as ‘spreaders’ or ‘sponges’ and orders the timescales of progression from site to site. In light of recent experimental evidence highlighting the potential significance of self-seeding of primary tumors, we use a Markov chain Monte Carlo (MCMC) approach, based on large autopsy data sets, to quantify the stochastic, systemic, and often multi-directional aspects of cancer progression. We quantify three types of multi-directional mechanisms of progression: (i) self-seeding of the primary tumor; (ii) re-seeding of the primary tumor from a metastatic site (primary re-seeding); and (iii) re-seeding of metastatic tumors (metastasis re-seeding). The model shows that the combined characteristics of the primary and the first metastatic site to which it spreads largely determine the future pathways and timescales of systemic disease. For lung cancer, the main ‘spreaders’ of systemic disease are the adrenal gland and kidney, whereas the main ‘sponges’ are regional lymph nodes, liver, and bone. Lung is a significant self-seeder, although it is a ‘sponge’ site with respect to progression characteristics. PMID:23447576

Multi-membership gene regulation in pathway based microarray analysis

PubMed Central

2011-01-01

Background Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes. PMID:21939531

Multi-membership gene regulation in pathway based microarray analysis.

PubMed

Pavlidis, Stelios P; Payne, Annette M; Swift, Stephen M

2011-09-22

Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

An integrative system biology approach to unravel potential drug candidates for multiple age related disorders.

PubMed

Srivastava, Isha; Khurana, Pooja; Yadav, Mohini; Hasija, Yasha

2017-12-01

Aging, though an inevitable part of life, is becoming a worldwide social and economic problem. Healthy aging is usually marked by low probability of age related disorders. Good therapeutic approaches are still in need to cure age related disorders. Occurrence of more than one ARD in an individual, expresses the need of discovery of such target proteins, which can affect multiple ARDs. Advanced scientific and medical research technologies throughout last three decades have arrived to the point where lots of key molecular determinants affect human disorders can be examined thoroughly. In this study, we designed and executed an approach to prioritize drugs that may target multiple age related disorders. Our methodology, focused on the analysis of biological pathways and protein protein interaction networks that may contribute to the pharmacology of age related disorders, included various steps such as retrieval and analysis of data, protein-protein interaction network analysis, and statistical and comparative analysis of topological coefficients, pathway, and functional enrichment analysis, and identification of drug-target proteins. We assume that the identified molecular determinants may be prioritized for further screening as novel drug targets to cure multiple ARDs. Based on the analysis, an online tool named as 'ARDnet' has been developed to construct and demonstrate ARD interactions at the level of PPI, ARDs and ARDs protein interaction, ARDs pathway interaction and drug-target interaction. The tool is freely made available at http://genomeinformatics.dtu.ac.in/ARDNet/Index.html. Copyright © 2017 Elsevier B.V. All rights reserved.

2-Keto acids based biosynthesis pathways for renewable fuels and chemicals.

PubMed

Tashiro, Yohei; Rodriguez, Gabriel M; Atsumi, Shota

2015-03-01

Global energy and environmental concerns have driven the development of biological chemical production from renewable sources. Biological processes using microorganisms are efficient and have been traditionally utilized to convert biomass (i.e., glucose) to useful chemicals such as amino acids. To produce desired fuels and chemicals with high yield and rate, metabolic pathways have been enhanced and expanded with metabolic engineering and synthetic biology approaches. 2-Keto acids, which are key intermediates in amino acid biosynthesis, can be converted to a wide range of chemicals. 2-Keto acid pathways were engineered in previous research efforts and these studies demonstrated that 2-keto acid pathways have high potential for novel metabolic routes with high productivity. In this review, we discuss recently developed 2-keto acid-based pathways.

Developing a one-stop tinnitus service: outcomes of a joined up management strategy: a retrospective observational cohort study.

PubMed

Farr, Matthew R B; Moraleda Deleito, Javier; Xu, Yanmin; Ray, Jaydip

2016-02-01

The pressure to deliver quality care with finite resources means that dealing with single-symptom conditions like tinnitus in an efficient and individualized manner has never been more important. Both primary and secondary care practitioners have an obligation to explore efficient delivery of simple management pathways. Commissioners of health care are in a unique position to affect evidence-based strategic change in the management of uncomplicated tinnitus. This study is an attempt to explore one such option. We present the outcomes of a tinnitus patient pathway designed for one-stop management, thereby minimizing unnecessary additional appointments. A retrospective observational cohort study of 452 patients referred to a NHS one-stop tinnitus clinic from 2008 to 2012. Clinical care guided was through the use of a structured approach to history taking, neurotological examination and management. 294 out of 452 (65%) of patients referred had unilateral tinnitus. The most common associated complaints were hearing loss (387/452, 86%) and hyperacusis (329/452, 73%). 210 (46%) of patients had their presenting complaint dealt with in a single clinic visit. A structured system for referral and management of tinnitus within the health system ensures patients have timely access to evidence-based investigation and treatment. A consistent approach to imaging aimed at identifying retrocochlear pathology can benefit patients through early diagnosis of central pathology and the reassurance provided by a negative scan. © 2015 John Wiley & Sons, Ltd.

Cells of Origin of Epithelial Ovarian Cancers

DTIC Science & Technology

2015-09-01

cells in oral squamous cell carcinomas by a novel pathway-based lineage tracing approach in a murine model. ! 13! Specific aims: 1. Determine...SUNDARESAN Lineage tracing and clonal analysis of oral cancer initiating cells The goal of this project is to study cancer stem cells /cancer initiating...whether oral cancer cells genetically marked based on their activities for stem cell -related pathways exhibit cancer stem cell properties in vivo by

Establishment of an Arabidopsis callus system to study the interrelations of biosynthesis, degradation and accumulation of carotenoids

PubMed Central

Schaub, Patrick; Rodriguez-Franco, Marta; Cazzonelli, Christopher Ian; Álvarez, Daniel; Wüst, Florian

2018-01-01

The net amounts of carotenoids accumulating in plant tissues are determined by the rates of biosynthesis and degradation. While biosynthesis is rate-limited by the activity of PHYTOENE SYNTHASE (PSY), carotenoid losses are caused by catabolic enzymatic and non-enzymatic degradation. We established a system based on non-green Arabidopsis callus which allowed investigating major determinants for high steady-state levels of β-carotene. Wild-type callus development was characterized by strong carotenoid degradation which was only marginally caused by the activity of carotenoid cleavage oxygenases. In contrast, carotenoid degradation occurred mostly non-enzymatically and selectively affected carotenoids in a molecule-dependent manner. Using carotenogenic pathway mutants, we found that linear carotenes such as phytoene, phytofluene and pro-lycopene resisted degradation and accumulated while β-carotene was highly susceptible towards degradation. Moderately increased pathway activity through PSY overexpression was compensated by degradation revealing no net increase in β-carotene. However, higher pathway activities outcompeted carotenoid degradation and efficiently increased steady-state β-carotene amounts to up to 500 μg g-1 dry mass. Furthermore, we identified oxidative β-carotene degradation products which correlated with pathway activities, yielding β-apocarotenals of different chain length and various apocarotene-dialdehydes. The latter included methylglyoxal and glyoxal as putative oxidative end products suggesting a potential recovery of carotenoid-derived carbon for primary metabolic pathways. Moreover, we investigated the site of β-carotene sequestration by co-localization experiments which revealed that β-carotene accumulated as intra-plastid crystals which was confirmed by electron microscopy with carotenoid-accumulating roots. The results are discussed in the context of using the non-green calli carotenoid assay system for approaches targeting high steady-state β-carotene levels prior to their application in crops. PMID:29394270

The mevalonate pathway regulates primitive streak formation via protein farnesylation

PubMed Central

Okamoto-Uchida, Yoshimi; Yu, Ruoxing; Miyamura, Norio; Arima, Norie; Ishigami-Yuasa, Mari; Kagechika, Hiroyuki; Yoshida, Suguru; Hosoya, Takamitsu; Nawa, Makiko; Kasama, Takeshi; Asaoka, Yoichi; Alois, Reiner Wimmer; Elling, Ulrich; Penninger, Josef M.; Nishina, Sachiko; Azuma, Noriyuki; Nishina, Hiroshi

2016-01-01

The primitive streak in peri-implantation embryos forms the mesoderm and endoderm and controls cell differentiation. The metabolic cues regulating primitive streak formation remain largely unknown. Here we utilised a mouse embryonic stem (ES) cell differentiation system and a library of well-characterised drugs to identify these metabolic factors. We found that statins, which inhibit the mevalonate metabolic pathway, suppressed primitive streak formation in vitro and in vivo. Using metabolomics and pharmacologic approaches we identified the downstream signalling pathway of mevalonate and revealed that primitive streak formation requires protein farnesylation but not cholesterol synthesis. A tagging-via-substrate approach revealed that nuclear lamin B1 and small G proteins were farnesylated in embryoid bodies and important for primitive streak gene expression. In conclusion, protein farnesylation driven by the mevalonate pathway is a metabolic cue essential for primitive streak formation. PMID:27883036

7. Survivable low frequency communication system pathway, looking east ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

7. Survivable low frequency communication system pathway, looking east - Ellsworth Air Force Base, Delta Flight, Launch Control Facility, County Road CS23A, North of Exit 127, Interior, Jackson County, SD

Functional phosphoproteomic mass spectrometry-based approaches

PubMed Central

2012-01-01

Mass Spectrometry (MS)-based phosphoproteomics tools are crucial for understanding the structure and dynamics of signaling networks. Approaches such as affinity purification followed by MS have also been used to elucidate relevant biological questions in health and disease. The study of proteomes and phosphoproteomes as linked systems, rather than research studies of individual proteins, are necessary to understand the functions of phosphorylated and un-phosphorylated proteins under spatial and temporal conditions. Phosphoproteome studies also facilitate drug target protein identification which may be clinically useful in the near future. Here, we provide an overview of general principles of signaling pathways versus phosphorylation. Likewise, we detail chemical phosphoproteomic tools, including pros and cons with examples where these methods have been applied. In addition, basic clues of electrospray ionization and collision induced dissociation fragmentation are detailed in a simple manner for successful phosphoproteomic clinical studies. PMID:23369623

Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search

PubMed Central

Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier; Knapp, Merrill; Talcott, Carolyn

2017-01-01

In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing, which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation. PMID:28191459

Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search.

PubMed

Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier; Knapp, Merrill; Santos-García, Gustavo; Talcott, Carolyn

2017-01-01

In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing , which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation.

Adaptive Control Model Reveals Systematic Feedback and Key Molecules in Metabolic Pathway Regulation

PubMed Central

Moffitt, Richard A.; Merrill, Alfred H.; Wang, May D.

2011-01-01

Abstract Robust behavior in metabolic pathways resembles stabilized performance in systems under autonomous control. This suggests we can apply control theory to study existing regulation in these cellular networks. Here, we use model-reference adaptive control (MRAC) to investigate the dynamics of de novo sphingolipid synthesis regulation in a combined theoretical and experimental case study. The effects of serine palmitoyltransferase over-expression on this pathway are studied in vitro using human embryonic kidney cells. We report two key results from comparing numerical simulations with observed data. First, MRAC simulations of pathway dynamics are comparable to simulations from a standard model using mass action kinetics. The root-sum-square (RSS) between data and simulations in both cases differ by less than 5%. Second, MRAC simulations suggest systematic pathway regulation in terms of adaptive feedback from individual molecules. In response to increased metabolite levels available for de novo sphingolipid synthesis, feedback from molecules along the main artery of the pathway is regulated more frequently and with greater amplitude than from other molecules along the branches. These biological insights are consistent with current knowledge while being new that they may guide future research in sphingolipid biology. In summary, we report a novel approach to study regulation in cellular networks by applying control theory in the context of robust metabolic pathways. We do this to uncover potential insight into the dynamics of regulation and the reverse engineering of cellular networks for systems biology. This new modeling approach and the implementation routines designed for this case study may be extended to other systems. Supplementary Material is available at www.liebertonline.com/cmb. PMID:21314456

A Model of an Integrated Immune System Pathway in Homo sapiens and Its Interaction with Superantigen Producing Expression Regulatory Pathway in Staphylococcus aureus: Comparing Behavior of Pathogen Perturbed and Unperturbed Pathway

PubMed Central

Tomar, Namrata; De, Rajat K.

2013-01-01

Response of an immune system to a pathogen attack depends on the balance between the host immune defense and the virulence of the pathogen. Investigation of molecular interactions between the proteins of a host and a pathogen helps in identifying the pathogenic proteins. It is necessary to understand the dynamics of a normally behaved host system to evaluate the capacity of its immune system upon pathogen attack. In this study, we have compared the behavior of an unperturbed and pathogen perturbed host system. Moreover, we have developed a formalism under Flux Balance Analysis (FBA) for the optimization of conflicting objective functions. We have constructed an integrated pathway system, which includes Staphylococcal Superantigen (SAg) expression regulatory pathway and TCR signaling pathway of Homo sapiens. We have implemented the method on this pathway system and observed the behavior of host signaling molecules upon pathogen attack. The entire study has been divided into six different cases, based on the perturbed/unperturbed conditions. In other words, we have investigated unperturbed and pathogen perturbed human TCR signaling pathway, with different combinations of optimization of concentrations of regulatory and signaling molecules. One of these cases has aimed at finding out whether minimization of the toxin production in a pathogen leads to the change in the concentration levels of the proteins coded by TCR signaling pathway genes in the infected host. Based on the computed results, we have hypothesized that the balance between TCR signaling inhibitory and stimulatory molecules can keep TCR signaling system into resting/stimulating state, depending upon the perturbation. The proposed integrated host-pathogen interaction pathway model has accurately reflected the experimental evidences, which we have used for validation purpose. The significance of this kind of investigation lies in revealing the susceptible interaction points that can take back the Staphylococcal Enterotoxin (SE)-challenged system within the range of normal behavior. PMID:24324645

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining.

PubMed

Xu, Xiao; Jin, Tao; Wei, Zhijie; Wang, Jianmin

2017-01-01

Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model.

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

PubMed Central

Xu, Xiao; Wei, Zhijie

2017-01-01

Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model. PMID:29065617

Position-specific isotope modeling of organic micropollutants transformations through different reaction pathways

NASA Astrophysics Data System (ADS)

Jin, Biao; Rolle, Massimo

2016-04-01

Organic compounds are produced in vast quantities for industrial and agricultural use, as well as for human and animal healthcare [1]. These chemicals and their metabolites are frequently detected at trace levels in fresh water environments where they undergo degradation via different reaction pathways. Compound specific stable isotope analysis (CSIA) is a valuable tool to identify such degradation pathways in different environmental systems. Recent advances in analytical techniques have promoted the fast development and implementation of multi-element CSIA. However, quantitative frameworks to evaluate multi-element stable isotope data and incorporating mechanistic information on the degradation processes [2,3] are still lacking. In this study we propose a mechanism-based modeling approach to simultaneously evaluate concentration as well as bulk and position-specific multi-element isotope evolution during the transformation of organic micropollutants. The model explicitly simulates position-specific isotopologues for those atoms that experience isotope effects and, thereby, provides a mechanistic description of isotope fractionation occurring at different molecular positions. We validate the proposed approach with the concentration and multi-element isotope data of three selected organic micropollutants: dichlorobenzamide (BAM), isoproturon (IPU) and diclofenac (DCF). The model precisely captures the dual element isotope trends characteristic of different reaction pathways and their range of variation consistent with observed multi-element (C, N) bulk isotope fractionation. The proposed approach can also be used as a tool to explore transformation pathways in scenarios for which position-specific isotope data are not yet available. [1] Schwarzenbach, R.P., Egli, T., Hofstetter, T.B., von Gunten, U., Wehrli, B., 2010. Global Water Pollution and Human Health. Annu. Rev. Environ. Resour. doi:10.1146/annurev-environ-100809-125342. [2] Jin, B., Haderlein, S.B., Rolle, M., 2013. Integrated carbon and chlorine isotope modeling: Applications to chlorinated aliphatic hydrocarbons dechlorination. Environ. Sci. Technol. 47, 1443-1451. doi:10.1021/es304053h. [3] Jin, B., Rolle, M., 2014. Mechanistic approach to multi-element isotope modeling of organic contaminant degradation. Chemosphere 95, 131-139. doi:10.1016/j.chemosphere.2013.08.050.

Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma.

PubMed

Li, Chaoxing; Liu, Li; Dinu, Valentin

2018-01-01

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher's exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov-Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.

Pathway analysis of genome-wide association study data highlights pancreatic development genes as susceptibility factors for pancreatic cancer

PubMed Central

Duell, Eric J.; Yu, Kai; Risch, Harvey A.; Olson, Sara H.; Kooperberg, Charles; Wolpin, Brian M.; Jiao, Li; Dong, Xiaoqun; Wheeler, Bill; Arslan, Alan A.; Bueno-de-Mesquita, H. Bas; Fuchs, Charles S.; Gallinger, Steven; Gross, Myron; Hartge, Patricia; Hoover, Robert N.; Holly, Elizabeth A.; Jacobs, Eric J.; Klein, Alison P.; LaCroix, Andrea; Mandelson, Margaret T.; Petersen, Gloria; Zheng, Wei; Agalliu, Ilir; Albanes, Demetrius; Boutron-Ruault, Marie-Christine; Bracci, Paige M.; Buring, Julie E.; Canzian, Federico; Chang, Kenneth; Chanock, Stephen J.; Cotterchio, Michelle; Gaziano, J.Michael; Giovannucci, Edward L.; Goggins, Michael; Hallmans, Göran; Hankinson, Susan E.; Hoffman Bolton, Judith A.; Hunter, David J.; Hutchinson, Amy; Jacobs, Kevin B.; Jenab, Mazda; Khaw, Kay-Tee; Kraft, Peter; Krogh, Vittorio; Kurtz, Robert C.; McWilliams, Robert R.; Mendelsohn, Julie B.; Patel, Alpa V.; Rabe, Kari G.; Riboli, Elio; Shu, Xiao-Ou; Tjønneland, Anne; Tobias, Geoffrey S.; Trichopoulos, Dimitrios; Virtamo, Jarmo; Visvanathan, Kala; Watters, Joanne; Yu, Herbert; Zeleniuch-Jacquotte, Anne; Stolzenberg-Solomon, Rachael Z.

2012-01-01

Four loci have been associated with pancreatic cancer through genome-wide association studies (GWAS). Pathway-based analysis of GWAS data is a complementary approach to identify groups of genes or biological pathways enriched with disease-associated single-nucleotide polymorphisms (SNPs) whose individual effect sizes may be too small to be detected by standard single-locus methods. We used the adaptive rank truncated product method in a pathway-based analysis of GWAS data from 3851 pancreatic cancer cases and 3934 control participants pooled from 12 cohort studies and 8 case–control studies (PanScan). We compiled 23 biological pathways hypothesized to be relevant to pancreatic cancer and observed a nominal association between pancreatic cancer and five pathways (P < 0.05), i.e. pancreatic development, Helicobacter pylori lacto/neolacto, hedgehog, Th1/Th2 immune response and apoptosis (P = 2.0 × 10−6, 1.6 × 10−5, 0.0019, 0.019 and 0.023, respectively). After excluding previously identified genes from the original GWAS in three pathways (NR5A2, ABO and SHH), the pancreatic development pathway remained significant (P = 8.3 × 10−5), whereas the others did not. The most significant genes (P < 0.01) in the five pathways were NR5A2, HNF1A, HNF4G and PDX1 for pancreatic development; ABO for H. pylori lacto/neolacto; SHH for hedgehog; TGFBR2 and CCL18 for Th1/Th2 immune response and MAPK8 and BCL2L11 for apoptosis. Our results provide a link between inherited variation in genes important for pancreatic development and cancer and show that pathway-based approaches to analysis of GWAS data can yield important insights into the collective role of genetic risk variants in cancer. PMID:22523087

DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments.

PubMed

Vrahatis, Aristidis G; Balomenos, Panos; Tsakalidis, Athanasios K; Bezerianos, Anastasios

2016-12-15

DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customized framework with a broad range of operation modes at all stages of the subpathway analysis, enabling so a case-specific approach. The operation modes include pathway network construction and processing, subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render DEsubs a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level drug targets and biomarkers for complex diseases. DEsubs is implemented as an R package following Bioconductor guidelines: http://bioconductor.org/packages/DEsubs/ CONTACT: tassos.bezerianos@nus.edu.sgSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A novel approach of dynamic cross correlation analysis on molecular dynamics simulations and its application to Ets1 dimer-DNA complex.

PubMed

Kasahara, Kota; Fukuda, Ikuo; Nakamura, Haruki

2014-01-01

The dynamic cross correlation (DCC) analysis is a popular method for analyzing the trajectories of molecular dynamics (MD) simulations. However, it is difficult to detect correlative motions that appear transiently in only a part of the trajectory, such as atomic contacts between the side-chains of amino acids, which may rapidly flip. In order to capture these multi-modal behaviors of atoms, which often play essential roles, particularly at the interfaces of macromolecules, we have developed the "multi-modal DCC (mDCC)" analysis. The mDCC is an extension of the DCC and it takes advantage of a Bayesian-based pattern recognition technique. We performed MD simulations for molecular systems modeled from the (Ets1)2-DNA complex and analyzed their results with the mDCC method. Ets1 is an essential transcription factor for a variety of physiological processes, such as immunity and cancer development. Although many structural and biochemical studies have so far been performed, its DNA binding properties are still not well characterized. In particular, it is not straightforward to understand the molecular mechanisms how the cooperative binding of two Ets1 molecules facilitates their recognition of Stromelysin-1 gene regulatory elements. A correlation network was constructed among the essential atomic contacts, and the two major pathways by which the two Ets1 molecules communicate were identified. One is a pathway via direct protein-protein interactions and the other is that via the bound DNA intervening two recognition helices. These two pathways intersected at the particular cytosine bases (C110/C11), interacting with the H1, H2, and H3 helices. Furthermore, the mDCC analysis showed that both pathways included the transient interactions at their intermolecular interfaces of Tyr396-C11 and Ala327-Asn380 in multi-modal motions of the amino acid side chains and the nucleotide backbone. Thus, the current mDCC approach is a powerful tool to reveal these complicated behaviors and scrutinize intermolecular communications in a molecular system.

An Adaptive Genetic Association Test Using Double Kernel Machines.

PubMed

Zhan, Xiang; Epstein, Michael P; Ghosh, Debashis

2015-10-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.

Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension.

PubMed

Li, Chen; Nagasaki, Masao; Ueno, Kazuko; Miyano, Satoru

2009-04-27

Model checking approaches were applied to biological pathway validations around 2003. Recently, Fisher et al. have proved the importance of model checking approach by inferring new regulation of signaling crosstalk in C. elegans and confirming the regulation with biological experiments. They took a discrete and state-based approach to explore all possible states of the system underlying vulval precursor cell (VPC) fate specification for desired properties. However, since both discrete and continuous features appear to be an indispensable part of biological processes, it is more appropriate to use quantitative models to capture the dynamics of biological systems. Our key motivation of this paper is to establish a quantitative methodology to model and analyze in silico models incorporating the use of model checking approach. A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe) as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules - Rule I and Rule II - to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from in silico and in vivo experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in 1986. Our simulation results suggest that: Rule I that cannot be applied with qualitative based model checking, is more reasonable than Rule II owing to the high coverage of predicted fate patterns (except for the genotype of lin-15ko; lin-12ko double mutants). More insights are also suggested. The quantitative simulation-based model checking approach is a useful means to provide us valuable biological insights and better understandings of biological systems and observation data that may be hard to capture with the qualitative one.

Systems biology-based approaches toward understanding drought tolerance in food crops.

PubMed

Jogaiah, Sudisha; Govind, Sharathchandra Ramsandra; Tran, Lam-Son Phan

2013-03-01

Economically important crops, such as maize, wheat, rice, barley, and other food crops are affected by even small changes in water potential at important growth stages. Developing a comprehensive understanding of host response to drought requires a global view of the complex mechanisms involved. Research on drought tolerance has generally been conducted using discipline-specific approaches. However, plant stress response is complex and interlinked to a point where discipline-specific approaches do not give a complete global analysis of all the interlinked mechanisms. Systems biology perspective is needed to understand genome-scale networks required for building long-lasting drought resistance. Network maps have been constructed by integrating multiple functional genomics data with both model plants, such as Arabidopsis thaliana, Lotus japonicus, and Medicago truncatula, and various food crops, such as rice and soybean. Useful functional genomics data have been obtained from genome-wide comparative transcriptome and proteome analyses of drought responses from different crops. This integrative approach used by many groups has led to identification of commonly regulated signaling pathways and genes following exposure to drought. Combination of functional genomics and systems biology is very useful for comparative analysis of other food crops and has the ability to develop stable food systems worldwide. In addition, studying desiccation tolerance in resurrection plants will unravel how combination of molecular genetic and metabolic processes interacts to produce a resurrection phenotype. Systems biology-based approaches have helped in understanding how these individual factors and mechanisms (biochemical, molecular, and metabolic) "interact" spatially and temporally. Signaling network maps of such interactions are needed that can be used to design better engineering strategies for improving drought tolerance of important crop species.

DRUGS System Improving the Effects of Clinical Pathways: A Systematic Study.

PubMed

Wang, Shan; Zhu, Xiaohe; Zhao, Xian; Lu, Yang; Yang, Zhifu; Qian, Xiaoliang; Li, Weiwei; Ma, Lixiazi; Guo, Huning; Wang, Jingwen; Wen, Aidong

2016-03-01

The aim of the study is to assess the feasibility of Drugs Rational Usage Guideline System (DRUGS)-supported clinical pathway (CP) for breast carcinoma, cataract, inguinal hernia and 2-diabetes mellitus whether the application of such a system could improve work efficiency, medical safety, and decrease hospital cost. Four kinds of diseases which included 1773 cases (where 901 cases using paper-based clinical pathways and 872 cases using DRUGS-supported clinical pathways) were selected and their demographic and clinical data were collected. The evaluation criteria were length of stay, preoperative length of stay, hospital cost, antibiotics prescribed during hospitalization, unscheduled surgery, complications and prognosis. The median total LOS was 1 to 3 days shorter in the DRUGS-supported CP group as compared to the Paper-based CP group for all types (p < 0.05). Totel hospital cost decreased significantly in the DRUGS-supported CP group than that in Paper-based CP group. About antibiotics prescribed during hospitalization, there were no statistically differences in the time of initial dose of antibiotic and the duration of administration except the choice of antibiotic categories. The proportion of DRUGS-supported clinical pathway conditions where a broad-spectrum antibiotic was prescribed decreased from 63.6 to 34.5 % (p < 0.01) in the Paper-based group. While after the intervention, the differences were statistically not significant in unscheduled surgery, complications and prognosis. In this study, DRUGS-supported clinical pathway for breast carcinoma, cataract, inguinal hernia, 2-diabetes mellitus was smoothly shifted from a paper-based to an electronic system, and confer benefits at the hospital level.

The impact of clinical nurse specialists on clinical pathways in the application of evidence-based practice.

PubMed

Gurzick, Martha; Kesten, Karen S

2010-01-01

The purpose of this article was to address the call for evidence-based practice through the development of clinical pathways and to assert the role of the clinical nurse specialist (CNS) as a champion in clinical pathway implementation. In the current health care system, providing quality of care while maintaining cost-effectiveness is an ever-growing battle that institutions face. The CNS's role is central to meeting these demands. An extensive literature review has been conducted to validate the use of clinical pathways as a means of improving patient outcomes. This literature also suggests that clinical pathways must be developed, implemented, and evaluated utilizing validated methods including the use of best practice standards. Execution of clinical pathways should include a clinical expert, who has the ability to look at the system as a whole and can facilitate learning and change by employing a multitude of competencies while maintaining a sphere of influence over patient and families, nurses, and the system. The CNS plays a pivotal role in influencing effective clinical pathway development, implementation, utilization, and ongoing evaluation to ensure improved patient outcomes and reduced costs. This article expands upon the call for evidence-based practice through the utilization of clinical pathways to improve patient outcomes and reduce costs and stresses the importance of the CNS as a primary figure for ensuring proper pathway development, implementation, and ongoing evaluation. Copyright 2010 Elsevier Inc. All rights reserved.

Molecular stratification and precision medicine in systemic sclerosis from genomic and proteomic data.

PubMed

Martyanov, Viktor; Whitfield, Michael L

2016-01-01

The goal of this review is to summarize recent advances into the pathogenesis and treatment of systemic sclerosis (SSc) from genomic and proteomic studies. Intrinsic gene expression-driven molecular subtypes of SSc are reproducible across three independent datasets. These subsets are a consistent feature of SSc and are found in multiple end-target tissues, such as skin and esophagus. Intrinsic subsets as well as baseline levels of molecular target pathways are potentially predictive of clinical response to specific therapeutics, based on three recent clinical trials. A gene expression-based biomarker of modified Rodnan skin score, a measure of SSc skin severity, can be used as a surrogate outcome metric and has been validated in a recent trial. Proteome analyses have identified novel biomarkers of SSc that correlate with SSc clinical phenotypes. Integrating intrinsic gene expression subset data, baseline molecular pathway information, and serum biomarkers along with surrogate measures of modified Rodnan skin score provides molecular context in SSc clinical trials. With validation, these approaches could be used to match patients with the therapies from which they are most likely to benefit and thus increase the likelihood of clinical improvement.

Systems biology: An emerging strategy for discovering novel pathogenetic mechanisms that promote cardiovascular disease.

PubMed

Maron, Bradley A; Leopold, Jane A

2016-09-30

Reductionist theory proposes that analyzing complex systems according to their most fundamental components is required for problem resolution, and has served as the cornerstone of scientific methodology for more than four centuries. However, technological gains in the current scientific era now allow for the generation of large datasets that profile the proteomic, genomic, and metabolomic signatures of biological systems across a range of conditions. The accessibility of data on such a vast scale has, in turn, highlighted the limitations of reductionism, which is not conducive to analyses that consider multiple and contemporaneous interactions between intermediates within a pathway or across constructs. Systems biology has emerged as an alternative approach to analyze complex biological systems. This methodology is based on the generation of scale-free networks and, thus, provides a quantitative assessment of relationships between multiple intermediates, such as protein-protein interactions, within and between pathways of interest. In this way, systems biology is well positioned to identify novel targets implicated in the pathogenesis or treatment of diseases. In this review, the historical root and fundamental basis of systems biology, as well as the potential applications of this methodology are discussed with particular emphasis on integration of these concepts to further understanding of cardiovascular disorders such as coronary artery disease and pulmonary hypertension.

Synthetic and systems biology for microbial production of commodity chemicals.

PubMed

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J; Keasling, Jay D; Martín, Héctor García

2016-01-01

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.

Synthetic and systems biology for microbial production of commodity chemicals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges startmore » at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.« less

Synthetic and systems biology for microbial production of commodity chemicals

DOE PAGES

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.; ...

2016-04-07

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges startmore » at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.« less

A pathway-based network analysis of hypertension-related genes

NASA Astrophysics Data System (ADS)

Wang, Huan; Hu, Jing-Bo; Xu, Chuan-Yun; Zhang, De-Hai; Yan, Qian; Xu, Ming; Cao, Ke-Fei; Zhang, Xu-Sheng

2016-02-01

Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.

Reactive pathways of hydrogen and carbon removal from organosilicate glass low- κ films by F atoms

NASA Astrophysics Data System (ADS)

Voronina, Ekaterina N.; Mankelevich, Yuri A.; Rakhimova, Tatyana V.

2017-07-01

Direct molecular dynamic simulation on the base of the density functional theory (DFT) method is used to study some critical reactions of F atoms with organosilicate glass (OSG) low-κ films. Here static and dynamic DFT-based approaches are applied for a variety of reactive pathways of hydrogen and carbon removal in the form of volatile products (HF, CF2 and CF3 molecules) from initial SiCH3 surface groups. These reactions constitute an important part of the proposed multi-step mechanism of OSG films damage and etching by thermal F atoms. Two models (POSS and TMCTS macromolecules and their modifications) are used to illustrate the peculiarities and dynamics of the successive reactions of F atoms with the initial SiCH3 and appeared SiCHxFy (x + y ≤ 3) surface groups. Contribution to the Topical Issue "Dynamics of Molecular Systems (MOLEC 2016)", edited by Alberto Garcia-Vela, Luis Banares and Maria Luisa Senent.

Connectivity of surface flow and sediments in a small upland catchment

NASA Astrophysics Data System (ADS)

Lexartza-Artza, I.; Wainwright, J.

2009-04-01

The study of connectivity can help understand complex systems in which different factors interact to influence water-transfer pathways across the landscape. Changes in the catchment can affect connectivity, which in turn can have significant effects on catchment processes and network structure. Furthermore, the potential negative effects of the transfer of nutrients, pollutants and sediments by water from land to water bodies make it necessary to improve our understanding of connectivity. This need is reinforced by increasing demands of legislation such as the Water Framework Directive for effective Integrated Catchment Management in which whole systems are considered rather than their individual parts separately. Thus, connectivity can potentially be a useful concept to assess more effectively the effects that changes can have in complex systems, and could provide useful knowledge for decision makers. Field-based approaches to connectivity, needed to gain a useful understanding of real systems, need to include both the structural and functional aspects of connectivity, as the interaction between function and structure has to be understood to examine the complexity of the relationships between factors influencing pathways and transfer processes. This has to be taken into consideration, therefore, when designing and carrying studies to assess connectivity of flow networks that can provide context-specific data necessary to inform modelling approaches. The Ingbirchworth Catchment, in the uplands of the River Don, England, is used to assess the feedbacks between the different factors influencing transfer networks and the spatial and temporal variability in dynamic and non-linear process responses across the landscape. An especial focus has been given to land-use change, as one of the variables that might have a considerable influence on runoff generation and pathways. This 8.5 km2 catchment shares many characteristics with many others in the River Don uplands, including the presence of small reservoirs that regulate the flow, a number of which have experienced pollution problems. A range of agricultural uses create a patchwork landscape in this area that is part of the Catchment Sensitive Farming programme. Using a nested approach, a baseline structure on which to develop a context-specific field approach and to acquire the data necessary to assess connectivity in the system has been followed. An initial and then iterative description of the catchment structure and characteristics has been carried, together with a study of the catchment history and sedimentation record. These allow the definition of the relevant landscape units, identification of elements that might influence connectivity and inference of potential past changes of flow pathways. Through event monitoring at different landscape settings and scales, both structural and functional aspects are considered together and the variability and changes in the flow network are shown. The knowledge obtained is being used to assess the roles of the identified elements in relation to connectivity and to recognize the interactions and feedbacks between different system components.

Cavallin et al 2016

EPA Pesticide Factsheets

Adult fathead minnows were exposed to dilutions of a historically estrogenic wastewater treatment plant effluent in a 21-d reproduction study. This dataset is comprised of a variety of endpoints representing key events along adverse outcome pathways linking estrogen receptor activation and other molecular initiating events to reproductive impairment. This study demonstrates the value of using an integrative approach that encompasses analytical chemistry, in vitro bioassays, and in vivo apical and pathway-based approaches with endpoints spanning from molecular- (e.g., gene expression) to organismal- (e.g., reproduction) levels of biological organization to help infer causal relationships between chemistry and potential effects on reproduction.This dataset is associated with the following publication:Cavallin , J., K. Jensen , M. Kahl , D. Villeneuve , K. Lee, A. Schroeder , J. Mayasich, E. Eid, K. Nelson, R. Milsk, B. Blackwell, J. Berninger , C. LaLone, C. Blanksma, T. Jicha , C. Elonen , R. Johnson , and G. Ankley. Pathway-based approaches for assessment of real-time exposure to an estrogenic wastewater treatment plant effluent on fathead minnow reproduction. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 35(3): 702-716, (2016).

Recovery in the 21st Century: From Shame to Strength.

PubMed

Gumbley, Stephen J

2016-01-01

Through the "war on drugs," the just-say-no campaign, and into the early years of this century, the overarching approach to substance use disorders (SUDs) called for a single outcome (abstinence) and a single methodology (spiritual connection with a higher power) as the remedy for SUDs. Those who did not become permanently abstinent or rejected the spiritual approach were seen as "not ready" or "in denial."A seismic shift in thinking about "addiction" and "recovery" began in earnest in the 1990s. In 2005, the Substance Abuse and Mental Health Services Administration brought together leaders of the treatment and recovery field for the historic National Summit on Recovery to develop broad-based consensus on guiding principles for recovery and elements of recovery-oriented systems of care.Major changes associated with the recovery-oriented approach include viewing SUDs as chronic, rather than acute, problems that require long-term support and focusing on recovery management rather than disease management. Complete abstinence is not an absolute requirement for wellness for all persons with SUDs. There are "many pathways to recovery," not only the 12-Step approach (White & Kurtz, 2006). Sustained recovery is self-directed and requires personal choices, the support of peers and allies, and community reinforcement as well as a strength-based approach and the use of research-based interventions. This Perspectives column addresses the historical context for the transformation toward a recovery-oriented system of care, highlights federal efforts to promote recovery-oriented approaches, and describes recovery-oriented terminology to reduce misconceptions, labeling, and stigmatization and promote recovery for individuals, families, and communities.

A Multipronged Approach to Improving Transitions and Outcomes for Nontraditional-Aged Students in Select Pathways at Oakton Community College. Pathways to Results: Implementation Partnerships Strategy Brief

ERIC Educational Resources Information Center

McCambly, Heather

2016-01-01

Oakton Community College (Oakton) launched a Pathways to Results (PTR) project for the first time in 2014, a decision that converged with the launch of an all-college student success team with the goal of making evidence-based decisions to significantly improve student success. Oakton chose to work initially on its manufacturing program.…

Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma

PubMed Central

Liu, Li; Dinu, Valentin

2018-01-01

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway’s topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher’s exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level. PMID:29666752

Constructing phylogenetic trees using interacting pathways.

PubMed

Wan, Peng; Che, Dongsheng

2013-01-01

Phylogenetic trees are used to represent evolutionary relationships among biological species or organisms. The construction of phylogenetic trees is based on the similarities or differences of their physical or genetic features. Traditional approaches of constructing phylogenetic trees mainly focus on physical features. The recent advancement of high-throughput technologies has led to accumulation of huge amounts of biological data, which in turn changed the way of biological studies in various aspects. In this paper, we report our approach of building phylogenetic trees using the information of interacting pathways. We have applied hierarchical clustering on two domains of organisms-eukaryotes and prokaryotes. Our preliminary results have shown the effectiveness of using the interacting pathways in revealing evolutionary relationships.

Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation.

PubMed

Zimmer, Christoph

2016-01-01

Computational modeling is a key technique for analyzing models in systems biology. There are well established methods for the estimation of the kinetic parameters in models of ordinary differential equations (ODE). Experimental design techniques aim at devising experiments that maximize the information encoded in the data. For ODE models there are well established approaches for experimental design and even software tools. However, data from single cell experiments on signaling pathways in systems biology often shows intrinsic stochastic effects prompting the development of specialized methods. While simulation methods have been developed for decades and parameter estimation has been targeted for the last years, only very few articles focus on experimental design for stochastic models. The Fisher information matrix is the central measure for experimental design as it evaluates the information an experiment provides for parameter estimation. This article suggest an approach to calculate a Fisher information matrix for models containing intrinsic stochasticity and high nonlinearity. The approach makes use of a recently suggested multiple shooting for stochastic systems (MSS) objective function. The Fisher information matrix is calculated by evaluating pseudo data with the MSS technique. The performance of the approach is evaluated with simulation studies on an Immigration-Death, a Lotka-Volterra, and a Calcium oscillation model. The Calcium oscillation model is a particularly appropriate case study as it contains the challenges inherent to signaling pathways: high nonlinearity, intrinsic stochasticity, a qualitatively different behavior from an ODE solution, and partial observability. The computational speed of the MSS approach for the Fisher information matrix allows for an application in realistic size models.

Pricing and reimbursement experiences and insights in the European Union and the United States: Lessons learned to approach adaptive payer pathways.

PubMed

Faulkner, S D; Lee, M; Qin, D; Morrell, L; Xoxi, E; Sammarco, A; Cammarata, S; Russo, P; Pani, L; Barker, R

2016-12-01

Earlier patient access to beneficial therapeutics that addresses unmet need is one of the main requirements of innovation in global healthcare systems already burdened by unsustainable budgets. "Adaptive pathways" encompass earlier cross-stakeholder engagement, regulatory tools, and iterative evidence generation through the life cycle of the medicinal product. A key enabler of earlier patient access is through more flexible and adaptive payer approaches to pricing and reimbursement that reflect the emerging evidence generated. © 2016 American Society for Clinical Pharmacology and Therapeutics.

Inter-species pathway perturbation prediction via data-driven detection of functional homology.

PubMed

Hafemeister, Christoph; Romero, Roberto; Bilal, Erhan; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Bonneau, Richard; Tarca, Adi L

2015-02-15

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli. We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli. Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h. christoph.hafemeister@nyu.edu or atarca@med.wayne.edu. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Differentiating high priority pathway-based toxicity from non-specific effects in high throughput toxicity data: A foundation for prioritizing AOP development.

EPA Science Inventory

The ToxCast chemical screening approach enables the rapid assessment of large numbers of chemicals for biological effects, primarily at the molecular level. Adverse outcome pathways (AOPs) offer a means to link biomolecular effects with potential adverse outcomes at the level of...

Building Blocks: Laying the Groundwork for Guided Pathways Reform in Ohio

ERIC Educational Resources Information Center

Jenkins, Davis; Lahr, Hana; Fink, John

2017-01-01

This report describes how Ohio's two-year colleges are approaching guided pathways reforms, based on on-site interviews with faculty, administrators, staff, and students at six selected community colleges and telephone interviews with representatives from all 23 Ohio community colleges. In these interviews participants were asked to describe their…

Artificial intelligence based decision support for trumpeter swan management

USGS Publications Warehouse

Sojda, Richard S.

2002-01-01

The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-State area where Montana, Idaho, and Wyoming come together has declined to just a few hundred pairs. However, these birds are part of the Rocky Mountain Population which additionally has over 3,500 birds breeding in Alberta, British Columbia, Northwest Territories, and Yukon Territory. To a large degree, these birds seem to have abandoned traditional migratory pathways in the flyway. Waterfowl managers have been interested in decision support tools that would help them explore simulated management scenarios in their quest towards reaching population recovery and the reestablishment of traditional migratory pathways. I have developed a decision support system to assist biologists with such management, especially related to wetland ecology. Decision support systems use a combination of models, analytical techniques, and information retrieval to help develop and evaluate appropriate alternatives. Swan management is a domain that is ecologically complex, and this complexity is compounded by spatial and temporal issues. As such, swan management is an inherently distributed problem. Therefore, the ecological context for modeling swan movements in response to management actions was built as a multiagent system of interacting intelligent agents that implements a queuing model representing swan migration. These agents accessed ecological knowledge about swans, their habitats, and flyway management principles from three independent expert systems. The agents were autonomous, had some sensory capability, and could respond to changing conditions. A key problem when developing ecological decision support systems is empirically determining that the recommendations provided are valid. Because Rocky Mountain trumpeter swans have been surveyed for a long period of time, I was able to compare simulated distributions provided by the system with actual field observations across 20 areas for the period 1988-2000. Applying the Matched Pairs Multivariate Permutation Test as a statistical tool was a new approach for comparing flyway distributions of waterfowl over time that seemed to work well. Based on this approach, the empirical evidence that I gathered led me to conclude that the base queuing model does accurately simulate swan distributions in the flyway. The system was insensitive to almost all model parameters tested. That remains perplexing, but might result from the base queuing model, itself, being particularly effective at representing the actual ecological diversity in the world of Rocky Mountain trumpeter swans, both spatial and temporally.

When avoidance leads to approach: how ear preference interacts with neuroticism to predict disinhibited approach.

PubMed

Jackson, Chris J

2008-07-01

A series of eight studies focuses on how the avoidance system represented by neuroticism can lead to disinhibited approach tendencies. Based on research which argues that hemispheric preferences predispose the left hemisphere to fast action goal formation, and contralateral pathways between ear and brain, it is proposed that (a) people with a right ear preference will engage in fast action goal formation and (b) disinhibited approach results from neurotic people who reduce anxiety by means of fast action goal formation. Study 1 provides evidence from telesales operators of a link between self-rated ear preference and objective ear preference and provides evidence that disinhibited approach is predicted by a neuroticismxear preference interaction. Studies 2, 3, and 4 provide evidence that ear preference is related to other measures of objective aural preference and action goal formation. Studies 5, 6, 7, and 8 provide evidence that the neuroticismxear preference interaction predicts a variety of different disinhibited approach tendencies.

Integrative systems control approach for reactivating Kaposi's sarcoma-associated herpesvirus (KSHV) with combinatory drugs

PubMed Central

Sun, Chien-Pin; Usui, Takane; Yu, Fuqu; Al-Shyoukh, Ibrahim; Shamma, Jeff; Sun, Ren; Ho, Chih-Ming

2009-01-01

Cells serve as basic units of life and represent intricate biological molecular systems. The vast number of cellular molecules with their signaling and regulatory circuitries forms an intertwined network. In this network, each pathway interacts non-linearly with others through different intermediates. Thus, the challenge of manipulating cellular functions for desired outcomes, such as cancer eradication and controlling viral infection lies within the integrative system of regulatory circuitries. By using a closed-loop system control scheme, we can efficiently analyze biological signaling networks and manipulate their behavior through multiple stimulations on a collection of pathways. Specifically, we aimed to maximize the reactivation of Kaposi's Sarcoma-associated Herpesvirus (KSHV) in a Primary Effusion Lymphoma cell line. The advantage of this approach is that it is well-suited to study complex integrated systems; it circumvents the need for detailed information of individual signaling components; and it investigates the network as a whole by utilizing key systemic outputs as indicators. PMID:19851479

Integrative systems control approach for reactivating Kaposi's sarcoma-associated herpesvirus (KSHV) with combinatory drugs.

PubMed

Sun, Chien-Pin; Usui, Takane; Yu, Fuqu; Al-Shyoukh, Ibrahim; Shamma, Jeff; Sun, Ren; Ho, Chih-Ming

2009-01-01

Cells serve as basic units of life and represent intricate biological molecular systems. The vast number of cellular molecules with their signaling and regulatory circuitries forms an intertwined network. In this network, each pathway interacts non-linearly with others through different intermediates. Thus, the challenge of manipulating cellular functions for desired outcomes, such as cancer eradication and controlling viral infection lies within the integrative system of regulatory circuitries. By using a closed-loop system control scheme, we can efficiently analyze biological signaling networks and manipulate their behavior through multiple stimulations on a collection of pathways. Specifically, we aimed to maximize the reactivation of Kaposi's Sarcoma-associated Herpesvirus (KSHV) in a Primary Effusion Lymphoma cell line. The advantage of this approach is that it is well-suited to study complex integrated systems; it circumvents the need for detailed information of individual signaling components; and it investigates the network as a whole by utilizing key systemic outputs as indicators.

Unravelling the neurophysiological basis of aggression in a fish model

PubMed Central

2010-01-01

Background Aggression is a near-universal behaviour with substantial influence on and implications for human and animal social systems. The neurophysiological basis of aggression is, however, poorly understood in all species and approaches adopted to study this complex behaviour have often been oversimplified. We applied targeted expression profiling on 40 genes, spanning eight neurological pathways and in four distinct regions of the brain, in combination with behavioural observations and pharmacological manipulations, to screen for regulatory pathways of aggression in the zebrafish (Danio rerio), an animal model in which social rank and aggressiveness tightly correlate. Results Substantial differences occurred in gene expression profiles between dominant and subordinate males associated with phenotypic differences in aggressiveness and, for the chosen gene set, they occurred mainly in the hypothalamus and telencephalon. The patterns of differentially-expressed genes implied multifactorial control of aggression in zebrafish, including the hypothalamo-neurohypophysial-system, serotonin, somatostatin, dopamine, hypothalamo-pituitary-interrenal, hypothalamo-pituitary-gonadal and histamine pathways, and the latter is a novel finding outside mammals. Pharmacological manipulations of various nodes within the hypothalamo-neurohypophysial-system and serotonin pathways supported their functional involvement. We also observed differences in expression profiles in the brains of dominant versus subordinate females that suggested sex-conserved control of aggression. For example, in the HNS pathway, the gene encoding arginine vasotocin (AVT), previously believed specific to male behaviours, was amongst those genes most associated with aggression, and AVT inhibited dominant female aggression, as in males. However, sex-specific differences in the expression profiles also occurred, including differences in aggression-associated tryptophan hydroxylases and estrogen receptors. Conclusions Thus, through an integrated approach, combining gene expression profiling, behavioural analyses, and pharmacological manipulations, we identified candidate genes and pathways that appear to play significant roles in regulating aggression in fish. Many of these are novel for non-mammalian systems. We further present a validated system for advancing our understanding of the mechanistic underpinnings of complex behaviours using a fish model. PMID:20846403

Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

PubMed

Maes, Michael; Nowak, Gabriel; Caso, Javier R; Leza, Juan Carlos; Song, Cai; Kubera, Marta; Klein, Hans; Galecki, Piotr; Noto, Cristiano; Glaab, Enrico; Balling, Rudi; Berk, Michael

2016-07-01

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular networks or pathways. Drug discovery processes should delineate new drugs targeting the intracellular networks and immune-related pathways.

Systems and synthetic biology approaches to alter plant cell walls and reduce biomass recalcitrance

DOE PAGES

Kalluri, Udaya C.; Yin, Hengfu; Yang, Xiaohan; ...

2014-11-03

Fine-tuning plant cell wall properties to render plant biomass more amenable to biofuel conversion is a colossal challenge. A deep knowledge of the biosynthesis and regulation of plant cell wall and a high-precision genome engineering toolset are the two essential pillars of efforts to alter plant cell walls and reduce biomass recalcitrance. The past decade has seen a meteoric rise in use of transcriptomics and high-resolution imaging methods resulting in fresh insights into composition, structure, formation and deconstruction of plant cell walls. Subsequent gene manipulation approaches, however, commonly include ubiquitous mis-expression of a single candidate gene in a host thatmore » carries an intact copy of the native gene. The challenges posed by pleiotropic and unintended changes resulting from such an approach are moving the field towards synthetic biology approaches. Finally, synthetic biology builds on a systems biology knowledge base and leverages high-precision tools for high-throughput assembly of multigene constructs and pathways, precision genome editing and site-specific gene stacking, silencing and/or removal. Here, we summarize the recent breakthroughs in biosynthesis and remodelling of major secondary cell wall components, assess the impediments in obtaining a systems-level understanding and explore the potential opportunities in leveraging synthetic biology approaches to reduce biomass recalcitrance.« less

Systems and synthetic biology approaches to alter plant cell walls and reduce biomass recalcitrance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kalluri, Udaya C.; Yin, Hengfu; Yang, Xiaohan

Fine-tuning plant cell wall properties to render plant biomass more amenable to biofuel conversion is a colossal challenge. A deep knowledge of the biosynthesis and regulation of plant cell wall and a high-precision genome engineering toolset are the two essential pillars of efforts to alter plant cell walls and reduce biomass recalcitrance. The past decade has seen a meteoric rise in use of transcriptomics and high-resolution imaging methods resulting in fresh insights into composition, structure, formation and deconstruction of plant cell walls. Subsequent gene manipulation approaches, however, commonly include ubiquitous mis-expression of a single candidate gene in a host thatmore » carries an intact copy of the native gene. The challenges posed by pleiotropic and unintended changes resulting from such an approach are moving the field towards synthetic biology approaches. Finally, synthetic biology builds on a systems biology knowledge base and leverages high-precision tools for high-throughput assembly of multigene constructs and pathways, precision genome editing and site-specific gene stacking, silencing and/or removal. Here, we summarize the recent breakthroughs in biosynthesis and remodelling of major secondary cell wall components, assess the impediments in obtaining a systems-level understanding and explore the potential opportunities in leveraging synthetic biology approaches to reduce biomass recalcitrance.« less

A Palladium Iodide-Catalyzed Cyclocarbonylation Approach to Thiadiazafluorenones.

PubMed

Veltri, Lucia; Paladino, Veronica; Plastina, Pierluigi; Gabriele, Bartolo

2016-07-15

The first example of an additive cyclocarbonylation process leading to 1-thia-4a,9-diazafluoren-4-ones is reported. This process is based on the reaction of readily available 2-(propynylthio)benzimidazoles with carbon monoxide carried out in EtOH at 100 °C under a 5/2 mixture of CO-CO2 at 70 atm in the presence of the PdI2/KI catalytic system. Experimental evidence suggests a mechanistic pathway involving N-palladation of the substrate followed by CO insertion, triple bond insertion, protonolysis, and isomerization.

Engineering dynamic pathway regulation using stress-response promoters.

PubMed

Dahl, Robert H; Zhang, Fuzhong; Alonso-Gutierrez, Jorge; Baidoo, Edward; Batth, Tanveer S; Redding-Johanson, Alyssa M; Petzold, Christopher J; Mukhopadhyay, Aindrila; Lee, Taek Soon; Adams, Paul D; Keasling, Jay D

2013-11-01

Heterologous pathways used in metabolic engineering may produce intermediates toxic to the cell. Dynamic control of pathway enzymes could prevent the accumulation of these metabolites, but such a strategy requires sensors, which are largely unknown, that can detect and respond to the metabolite. Here we applied whole-genome transcript arrays to identify promoters that respond to the accumulation of toxic intermediates, and then used these promoters to control accumulation of the intermediate and improve the final titers of a desired product. We apply this approach to regulate farnesyl pyrophosphate (FPP) production in the isoprenoid biosynthetic pathway in Escherichia coli. This strategy improved production of amorphadiene, the final product, by twofold over that from inducible or constitutive promoters, eliminated the need for expensive inducers, reduced acetate accumulation and improved growth. We extended this approach to another toxic intermediate to demonstrate the broad utility of identifying novel sensor-regulator systems for dynamic regulation.

An integrated approach to demonstrating the ANR pathway of proanthocyanidin biosynthesis in plants.

PubMed

Peng, Qing-Zhong; Zhu, Yue; Liu, Zhong; Du, Ci; Li, Ke-Gang; Xie, De-Yu

2012-09-01

Proanthocyanidins (PAs) are oligomers or polymers of plant flavan-3-ols and are important to plant adaptation in extreme environmental conditions. The characterization of anthocyanidin reductase (ANR) and leucoanthocyanidin reductase (LAR) has demonstrated the different biogenesis of four stereo-configurations of flavan-3-ols. It is important to understand whether ANR and the ANR pathway widely occur in the plant kingdom. Here, we report an integrated approach to demonstrate the ANR pathway in plants. This includes different methods to extract native ANR from different tissues of eight angiosperm plants (Lotus corniculatus, Desmodium uncinatum, Medicago sativa, Hordeum vulgare, Vitis vinifera, Vitis bellula, Parthenocissus heterophylla, and Cerasus serrulata) and one fern plant (Dryopteris pycnopteroides), a general enzymatic analysis approach to demonstrate the ANR activity, high-performance liquid chromatography-based fingerprinting to demonstrate (-)-epicatechin and other flavan-3-ol molecules, and phytochemical analysis of PAs. Results demonstrate that in addition to leaves of M. sativa, tissues of other eight plants contain an active ANR pathway. Particularly, the leaves, flowers and pods of D. uncinatum, which is a model plant to study LAR and the LAR pathways, are demonstrated to express an active ANR pathway. This finding suggests that the ANR pathway involves PA biosynthesis in D. uncinatum. In addition, a sequence BLAST analysis reveals that ANR homologs have been sequenced in plants from both gymnosperms and angiosperms. These data show that the ANR pathway to PA biosynthesis occurs in both seed and seedless vascular plants.

Food sovereignty, food security and health equity: a meta-narrative mapping exercise.

PubMed

Weiler, Anelyse M; Hergesheimer, Chris; Brisbois, Ben; Wittman, Hannah; Yassi, Annalee; Spiegel, Jerry M

2015-10-01

There has been growing policy interest in social justice issues related to both health and food. We sought to understand the state of knowledge on relationships between health equity--i.e. health inequalities that are socially produced--and food systems, where the concepts of 'food security' and 'food sovereignty' are prominent. We undertook exploratory scoping and mapping stages of a 'meta-narrative synthesis' on pathways from global food systems to health equity outcomes. The review was oriented by a conceptual framework delineating eight pathways to health (in)equity through the food system: 1--Multi-Scalar Environmental, Social Context; 2--Occupational Exposures; 3--Environmental Change; 4--Traditional Livelihoods, Cultural Continuity; 5--Intake of Contaminants; 6--Nutrition; 7--Social Determinants of Health and 8--Political, Economic and Regulatory context. The terms 'food security' and 'food sovereignty' were, respectively, paired with a series of health equity-related terms. Combinations of health equity and food security (1414 citations) greatly outnumbered pairings with food sovereignty (18 citations). Prominent crosscutting themes that were observed included climate change, biotechnology, gender, racialization, indigeneity, poverty, citizenship and HIV as well as institutional barriers to reducing health inequities in the food system. The literature indicates that food sovereignty-based approaches to health in specific contexts, such as advancing healthy school food systems, promoting soil fertility, gender equity and nutrition, and addressing structural racism, can complement the longer-term socio-political restructuring processes that health equity requires. Our conceptual model offers a useful starting point for identifying interventions with strong potential to promote health equity. A research agenda to explore project-based interventions in the food system along these pathways can support the identification of ways to strengthen both food sovereignty and health equity. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2014; all rights reserved.

Hybrid stochastic simulation of reaction-diffusion systems with slow and fast dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Strehl, Robert; Ilie, Silvana, E-mail: silvana@ryerson.ca

2015-12-21

In this paper, we present a novel hybrid method to simulate discrete stochastic reaction-diffusion models arising in biochemical signaling pathways. We study moderately stiff systems, for which we can partition each reaction or diffusion channel into either a slow or fast subset, based on its propensity. Numerical approaches missing this distinction are often limited with respect to computational run time or approximation quality. We design an approximate scheme that remedies these pitfalls by using a new blending strategy of the well-established inhomogeneous stochastic simulation algorithm and the tau-leaping simulation method. The advantages of our hybrid simulation algorithm are demonstrated onmore » three benchmarking systems, with special focus on approximation accuracy and efficiency.« less

Challenges for academic accreditation: the UK experience

NASA Astrophysics Data System (ADS)

Shearman, Richard; Seddon, Deborah

2010-08-01

Several factors (government policy, demographic trends, employer pressure) are leading to new forms of degree programmes in UK universities. The government is strongly encouraging engagement between universities and employers. Work-based learning is increasingly found in first and second cycle programmes, along with modules designed by employers and increasing use of distance learning. Engineering faculties are playing a leading part in these developments, and the Engineering Council, the engineering professional bodies and some universities are collaborating to develop work-based learning programmes as a pathway to professional qualification. While potentially beneficial to the engineering profession, these developments pose a challenge to traditional approaches to programme accreditation. This paper explores how this system deals with these challenges and highlights the issues that will have to be addressed to ensure that the system can cope effectively with change, especially the development of individually tailored, work-based second cycle programmes, while maintaining appropriate standards and international confidence.

Redundancy control in pathway databases (ReCiPa): an application for improving gene-set enrichment analysis in Omics studies and "Big data" biology.

PubMed

Vivar, Juan C; Pemu, Priscilla; McPherson, Ruth; Ghosh, Sujoy

2013-08-01

Abstract Unparalleled technological advances have fueled an explosive growth in the scope and scale of biological data and have propelled life sciences into the realm of "Big Data" that cannot be managed or analyzed by conventional approaches. Big Data in the life sciences are driven primarily via a diverse collection of 'omics'-based technologies, including genomics, proteomics, metabolomics, transcriptomics, metagenomics, and lipidomics. Gene-set enrichment analysis is a powerful approach for interrogating large 'omics' datasets, leading to the identification of biological mechanisms associated with observed outcomes. While several factors influence the results from such analysis, the impact from the contents of pathway databases is often under-appreciated. Pathway databases often contain variously named pathways that overlap with one another to varying degrees. Ignoring such redundancies during pathway analysis can lead to the designation of several pathways as being significant due to high content-similarity, rather than truly independent biological mechanisms. Statistically, such dependencies also result in correlated p values and overdispersion, leading to biased results. We investigated the level of redundancies in multiple pathway databases and observed large discrepancies in the nature and extent of pathway overlap. This prompted us to develop the application, ReCiPa (Redundancy Control in Pathway Databases), to control redundancies in pathway databases based on user-defined thresholds. Analysis of genomic and genetic datasets, using ReCiPa-generated overlap-controlled versions of KEGG and Reactome pathways, led to a reduction in redundancy among the top-scoring gene-sets and allowed for the inclusion of additional gene-sets representing possibly novel biological mechanisms. Using obesity as an example, bioinformatic analysis further demonstrated that gene-sets identified from overlap-controlled pathway databases show stronger evidence of prior association to obesity compared to pathways identified from the original databases.

Optimization of the THP-1 activation assay to detect pharmaceuticals with potential to cause immune mediated drug reactions.

PubMed

Corti, Daniele; Galbiati, Valentina; Gatti, Nicolò; Marinovich, Marina; Galli, Corrado L; Corsini, Emanuela

2015-10-01

Despite important impacts of systemic hypersensitivity induced by pharmaceuticals, for such endpoint no reliable preclinical approaches are available. We previously established an in vitro test to identify contact and respiratory allergens based on interleukin-8 (IL-8) production in THP-1 cells. Here, we challenged it for identification of pharmaceuticals associated with systemic hypersensitivity reactions, with the idea that drug sensitizers share common mechanisms of cell activation. Cells were exposed to drugs associated with systemic hypersensitivity reactions (streptozotocin, sulfamethoxazole, neomycin, probenecid, clonidine, procainamide, ofloxacin, methyl salicylate), while metformin was used as negative drug. Differently to chemicals, drugs tested were well tolerated, except clonidine and probenecid, with no signs of cytotoxicity up to 1-2mg/ml. THP-1 activation assay was adjusted, and conditions, that allow identification of all sensitizing drugs tested, were established. Next, using streptozotocin and selective inhibitors of PKC-β and p38 MAPK, two pathways involved in chemical allergen-induced cell activation, we tested the hypothesis that similar pathways were also involved in drug-induced IL-8 production and CD86 upregulation. Results indicated that drugs and chemical allergens share similar activation pathways. Finally, we made a structure-activity hypothesis related to hypersensitivity reactions, trying to individuate structural requisite that can be involved in immune mediated adverse reactions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Metabolomics and proteomics technologies to explore the herbal preparation affecting metabolic disorders using high resolution mass spectrometry.

PubMed

Zhang, Aihua; Zhou, Xiaohang; Zhao, Hongwei; Zou, Shiyu; Ma, Chung Wah; Liu, Qi; Sun, Hui; Liu, Liang; Wang, Xijun

2017-01-31

An integrative metabolomics and proteomics approach can provide novel insights in the understanding of biological systems. We have integrated proteome and metabolome data sets for a holistic view of the molecular mechanisms in disease. Using quantitative iTRAQ-LC-MS/MS proteomics coupled with UPLC-Q-TOF-HDMS based metabolomics, we determined the protein and metabolite expression changes in the kidney-yang deficiency syndrome (KYDS) rat model and further investigated the intervention effects of the Jinkui Shenqi Pill (JSP). The VIP-plot of the orthogonal PLS-DA (OPLS-DA) was used for discovering the potential biomarkers to clarify the therapeutic mechanisms of JSP in treating KYDS. The results showed that JSP can alleviate the kidney impairment induced by KYDS. Sixty potential biomarkers, including 5-l-glutamyl-taurine, phenylacetaldehyde, 4,6-dihydroxyquinoline, and xanthurenic acid etc., were definitely up- or down-regulated. The regulatory effect of JSP on the disturbed metabolic pathways was proved by the established metabonomic method. Using pathway analyses, we identified the disturbed metabolic pathways such as taurine and hypotaurine metabolism, pyrimidine metabolism, tyrosine metabolism, tryptophan metabolism, histidine metabolism, steroid hormone biosynthesis, etc. Furthermore, using iTRAQ-based quantitative proteomics analysis, seventeen differential proteins were identified and significantly altered by the JSP treatment. These proteins appear to be involved in Wnt, chemokine, PPAR, and MAPK signaling pathways, etc. Functional pathway analysis revealed that most of the proteins were found to play a key role in the regulation of metabolism pathways. Bioinformatics analysis with the IPA software found that these differentially-expressed moleculars had a strong correlation with the α-adrenergic signaling, FGF signaling, etc. Our data indicate that high-throughput metabolomics and proteomics can provide an insight on the herbal preparations affecting the metabolic disorders using high resolution mass spectrometry.

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE PAGES

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna; ...

2016-04-26

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

A review of the efficacy and safety of nanoparticle-based oral insulin delivery systems.

PubMed

Card, Jeffrey W; Magnuson, Bernadene A

2011-12-01

Nanotechnology is providing new and innovative means to detect, diagnose, and treat disease. In this regard, numerous nanoparticle-based approaches have been taken in an effort to develop an effective oral insulin therapy for the treatment of diabetes. This review summarizes efficacy data from studies that have evaluated oral insulin therapies in experimental models. Also provided here is an overview of the limited safety data that have been reported in these studies. To date, the most promising approaches for nanoparticle-based oral insulin therapy appear to involve the incorporation of insulin into complex multilayered nanoparticles that are mucoadhesive, biodegradable, biocompatible, and acid protected and into nanoparticles that are designed to take advantage of the vitamin B(12) uptake pathway. It is anticipated that the continued investigation and optimization of nanoparticle-based formulations for oral delivery of insulin will lead to a much sought-after noninvasive treatment for diabetes. Such investigations also may provide insight into the use of nanoparticle-based formulations for peptide- and protein-based oral treatment of other diseases and for various food-related purposes.

In search of new lead compounds for trypanosomiasis drug design: A protein structure-based linked-fragment approach

NASA Astrophysics Data System (ADS)

Verlinde, Christophe L. M. J.; Rudenko, Gabrielle; Hol, Wim G. J.

1992-04-01

A modular method for pursuing structure-based inhibitor design in the framework of a design cycle is presented. The approach entails four stages: (1) a design pathway is defined in the three-dimensional structure of a target protein; (2) this pathway is divided into subregions; (3) complementary building blocks, also called fragments, are designed in each subregion; complementarity is defined in terms of shape, hydrophobicity, hydrogen bond properties and electrostatics; and (4) fragments from different subregions are linked into potential lead compounds. Stages (3) and (4) are qualitatively guided by force-field calculations. In addition, the designed fragments serve as entries for retrieving existing compounds from chemical databases. This linked-fragment approach has been applied in the design of potentially selective inhibitors of triosephosphate isomerase from Trypanosoma brucei, the causative agent of sleeping sickness.

Metabolic and miRNA Profiling of TMV Infected Plants Reveals Biphasic Temporal Changes

PubMed Central

Bazzini, Ariel A.; Manacorda, Carlos A.; Tohge, Takayuki; Conti, Gabriela; Rodriguez, Maria C.; Nunes-Nesi, Adriano; Villanueva, Sofía; Fernie, Alisdair R.; Carrari, Fernando; Asurmendi, Sebastian

2011-01-01

Plant viral infections induce changes including gene expression and metabolic components. Identification of metabolites and microRNAs (miRNAs) differing in abundance along infection may provide a broad view of the pathways involved in signaling and defense that orchestrate and execute the response in plant-pathogen interactions. We used a systemic approach by applying both liquid and gas chromatography coupled to mass spectrometry to determine the relative level of metabolites across the viral infection, together with a miRs profiling using a micro-array based procedure. Systemic changes in metabolites were characterized by a biphasic response after infection. The first phase, detected at one dpi, evidenced the action of a systemic signal since no virus was detected systemically. Several of the metabolites increased at this stage were hormone-related. miRs profiling after infection also revealed a biphasic alteration, showing miRs alteration at 5 dpi where no virus was detected systemically and a late phase correlating with virus accumulation. Correlation analyses revealed a massive increase in the density of correlation networks after infection indicating a complex reprogramming of the regulatory pathways, either in response to the plant defense mechanism or to the virus infection itself. Our data propose the involvement of a systemic signaling on early miRs alteration. PMID:22174812

Metabolic engineering of microorganisms for the production of L-arginine and its derivatives.

PubMed

Shin, Jae Ho; Lee, Sang Yup

2014-12-03

L-arginine (ARG) is an important amino acid for both medicinal and industrial applications. For almost six decades, the research has been going on for its improved industrial level production using different microorganisms. While the initial approaches involved random mutagenesis for increased tolerance to ARG and consequently higher ARG titer, it is laborious and often leads to unwanted phenotypes, such as retarded growth. Discovery of L-glutamate (GLU) overproducing strains and using them as base strains for ARG production led to improved ARG production titer. Continued effort to unveil molecular mechanisms led to the accumulation of detailed knowledge on amino acid metabolism, which has contributed to better understanding of ARG biosynthesis and its regulation. Moreover, systems metabolic engineering now enables scientists and engineers to efficiently construct genetically defined microorganisms for ARG overproduction in a more rational and system-wide manner. Despite such effort, ARG biosynthesis is still not fully understood and many of the genes in the pathway are mislabeled. Here, we review the major metabolic pathways and its regulation involved in ARG biosynthesis in different prokaryotes including recent discoveries. Also, various strategies for metabolic engineering of bacteria for the overproduction of ARG are described. Furthermore, metabolic engineering approaches for producing ARG derivatives such as L-ornithine (ORN), putrescine and cyanophycin are described. ORN is used in medical applications, while putrescine can be used as a bio-based precursor for the synthesis of nylon-4,6 and nylon-4,10. Cyanophycin is also an important compound for the production of polyaspartate, another important bio-based polymer. Strategies outlined here will serve as a general guideline for rationally designing of cell-factories for overproduction of ARG and related compounds that are industrially valuable.

Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013

PubMed Central

2015-01-01

Background Since their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies. Results Six groups participated in the CG task and two groups in the PC task, together applying a wide range of extraction approaches including both established state-of-the-art systems and newly introduced extraction methods. The best-performing systems achieved F-scores of 55% on the CG task and 53% on the PC task, demonstrating a level of performance comparable to the best results achieved in similar previously proposed tasks. Conclusions The results indicate that existing event extraction technology can generalize to meet the novel challenges represented by the CG and PC task settings, suggesting that extraction methods are capable of supporting the construction of knowledge bases on the molecular mechanisms of cancer and the curation of biomolecular pathway models. The CG and PC tasks continue as open challenges for all interested parties, with data, tools and resources available from the shared task homepage. PMID:26202570

Assessment of Land and Water Resource Implications of the UK 2050 Carbon Plan

NASA Astrophysics Data System (ADS)

Konadu, D. D.; Sobral Mourao, Z.; Skelton, S.; Lupton, R.

2015-12-01

The UK Carbon Plan presents four low-carbon energy system pathways that achieves 80% GHG emission targets by 2050, stipulated in the UK Climate Change Act (2008). However, some of the energy technologies prescribed under these pathways are land and water intensive; but would the increase demand for land and water under these pathways lead to increased competition and stress on agricultural land, and water resources in the UK? To answer the above question, this study uses an integrated modelling approach, ForeseerTM, which characterises the interdependencies and evaluates the land and water requirement for the pathways, based on scenarios of power plant location, and the energy crop yield projections. The outcome is compared with sustainable limits of resource appropriation to assess potential stresses and competition for water and land by other sectors of the economy. The results show the Carbon Plan pathways have low overall impacts on UK water resources, but agricultural land use and food production could be significantly impacted. The impact on agricultural land use is shown to be mainly driven by projections for transport decarbonisation via indigenously sourced biofuels. On the other hand, the impact on water resources is mainly associated with increased inland thermal electricity generation capacity, which would compete with other industrial and public water demands. The results highlight the need for a critical appraisal of UK's long term low-carbon energy system planning, in particular bioenergy sourcing strategy, and the siting of thermal power generation in order to avert potential resource stress and competition.

Structure-oriented versus process-oriented approach to enhance efficiency for emergency room operations: what lessons can we learn?

PubMed

Hwang, Taik Gun; Lee, Younsuk; Shin, Hojung

2011-01-01

The efficiency and quality of a healthcare system can be defined as interactions among the system structure, processes, and outcome. This article examines the effect of structural adjustment (change in floor plan or layout) and process improvement (critical pathway implementation) on performance of emergency room (ER) operations for acute cerebral infarction patients. Two large teaching hospitals participated in this study: Korea University (KU) Guro Hospital and KU Anam Hospital. The administration of Guro adopted a structure-oriented approach in improving its ER operations while the administration of Anam employed a process-oriented approach, facilitating critical pathways and protocols. To calibrate improvements, the data for time interval, length of stay, and hospital charges were collected, before and after the planned changes were implemented at each hospital. In particular, time interval is the most essential measure for handling acute stroke patients because patients' survival and recovery are affected by the promptness of diagnosis and treatment. Statistical analyses indicated that both redesign of layout at Guro and implementation of critical pathways at Anam had a positive influence on most of the performance measures. However, reduction in time interval was not consistent at Guro, demonstrating delays in processing time for a few processes. The adoption of critical pathways at Anam appeared more effective in reducing time intervals than the structural rearrangement at Guro, mainly as a result of the extensive employee training required for a critical pathway implementation. Thus, hospital managers should combine structure-oriented and process-oriented strategies to maximize effectiveness of improvement efforts.

Neural circuitry and immunity

PubMed Central

Pavlov, Valentin A.; Tracey, Kevin J.

2015-01-01

Research during the last decade has significantly advanced our understanding of the molecular mechanisms at the interface between the nervous system and the immune system. Insight into bidirectional neuroimmune communication has characterized the nervous system as an important partner of the immune system in the regulation of inflammation. Neuronal pathways, including the vagus nerve-based inflammatory reflex are physiological regulators of immune function and inflammation. In parallel, neuronal function is altered in conditions characterized by immune dysregulation and inflammation. Here, we review these regulatory mechanisms and describe the neural circuitry modulating immunity. Understanding these mechanisms reveals possibilities to use targeted neuromodulation as a therapeutic approach for inflammatory and autoimmune disorders. These findings and current clinical exploration of neuromodulation in the treatment of inflammatory diseases defines the emerging field of Bioelectronic Medicine. PMID:26512000

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

DOE PAGES

Mudunuru, Maruti Kumar; Karra, Satish; Makedonska, Nataliia; ...

2017-09-05

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics formore » the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.« less

Identifying niche-mediated regulatory factors of stem cell phenotypic state: a systems biology approach.

PubMed

Ravichandran, Srikanth; Del Sol, Antonio

2017-02-01

Understanding how the cellular niche controls the stem cell phenotype is often hampered due to the complexity of variegated niche composition, its dynamics, and nonlinear stem cell-niche interactions. Here, we propose a systems biology view that considers stem cell-niche interactions as a many-body problem amenable to simplification by the concept of mean field approximation. This enables approximation of the niche effect on stem cells as a constant field that induces sustained activation/inhibition of specific stem cell signaling pathways in all stem cells within heterogeneous populations exhibiting the same phenotype (niche determinants). This view offers a new basis for the development of single cell-based computational approaches for identifying niche determinants, which has potential applications in regenerative medicine and tissue engineering. © 2017 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

Advancing patient safety: a framework for accountability and practical action.

PubMed

Wilson, N J; Hatlie, M J

2001-01-01

This article traces the development of the patient safety movement in healthcare from 1997 to the present. It reviews the findings and recommendations in the Institute of Medicine report on medical errors, which issued a call to action. Moving beyond the call to action requires aligning incentives, in both public and private sectors, consistent with complexity theory and the tenets of a systems approach to the reliable delivery of service in dynamic environments in which failure produces severe consequences. Because safety is a fundamental value of healthcare and has money-saving potential, it can be a powerful pathway forcultural change. Thisarticle explains a simple framework that requires alignment among stakeholder groups and communities. It recommends a practical problem-solving approach and explores the roles and responsibilities of each segment within the framework. Finally, it describes a VHA Inc. leadership initiative, based on the framework, to promote change within healthcare systems.

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mudunuru, Maruti Kumar; Karra, Satish; Makedonska, Nataliia

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics formore » the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.« less

A pathway-based view of human diseases and disease relationships.

PubMed

Li, Yong; Agarwal, Pankaj

2009-01-01

It is increasingly evident that human diseases are not isolated from each other. Understanding how different diseases are related to each other based on the underlying biology could provide new insights into disease etiology, classification, and shared biological mechanisms. We have taken a computational approach to studying disease relationships through 1) systematic identification of disease associated genes by literature mining, 2) associating diseases to biological pathways where disease genes are enriched, and 3) linking diseases together based on shared pathways. We identified 4,195 candidate disease associated genes for 1028 diseases. On average, about 50% of disease associated genes of a disease are statistically mapped to pathways. We generated a disease network which consists of 591 diseases and 6,931 disease relationships. We examined properties of this network and provided examples of novel disease relationships which cannot be readily captured through simple literature search or gene overlap analysis. Our results could potentially provide insights into the design of novel, pathway-guided therapeutic interventions for diseases.

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Systems approach to the pharmacological actions of HDAC inhibitors reveals EP300 activities and convergent mechanisms of regulation in diabetes.

PubMed

Rafehi, Haloom; Kaspi, Antony; Ziemann, Mark; Okabe, Jun; Karagiannis, Tom C; El-Osta, Assam

2017-01-01

Given the skyrocketing costs to develop new drugs, repositioning of approved drugs, such as histone deacetylase (HDAC) inhibitors, may be a promising strategy to develop novel therapies. However, a gap exists in the understanding and advancement of these agents to meaningful translation for which new indications may emerge. To address this, we performed systems-level analyses of 33 independent HDAC inhibitor microarray studies. Based on network analysis, we identified enrichment for pathways implicated in metabolic syndrome and diabetes (insulin receptor signaling, lipid metabolism, immunity and trafficking). Integration with ENCODE ChIP-seq datasets identified suppression of EP300 target genes implicated in diabetes. Experimental validation indicates reversal of diabetes-associated EP300 target genes in primary vascular endothelial cells derived from a diabetic individual following inhibition of HDACs (by SAHA), EP300, or EP300 knockdown. Our computational systems biology approach provides an adaptable framework for the prediction of novel therapeutics for existing disease.

Computationally efficient characterization of potential energy surfaces based on fingerprint distances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schaefer, Bastian; Goedecker, Stefan, E-mail: stefan.goedecker@unibas.ch

2016-07-21

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows us to understand important characteristics like thermodynamic, dynamic, and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This methodmore » allows us to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows us to generate an approximate network of the minima, their connectivity, and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling.« less

Microfluidics-Based Approaches to the Isolation of African Trypanosomes

PubMed Central

Barrett, Michael P.; Regnault, Clément; Tegenfeldt, Jonas O.; Hochstetter, Axel

2017-01-01

African trypanosomes are responsible for significant levels of disease in both humans and animals. The protozoan parasites are free-living flagellates, usually transmitted by arthropod vectors, including the tsetse fly. In the mammalian host they live in the bloodstream and, in the case of human-infectious species, later invade the central nervous system. Diagnosis of the disease requires the positive identification of parasites in the bloodstream. This can be particularly challenging where parasite numbers are low, as is often the case in peripheral blood. Enriching parasites from body fluids is an important part of the diagnostic pathway. As more is learned about the physicochemical properties of trypanosomes, this information can be exploited through use of different microfluidic-based approaches to isolate the parasites from blood or other fluids. Here, we discuss recent advances in the use of microfluidics to separate trypanosomes from blood and to isolate single trypanosomes for analyses including drug screening. PMID:28981471

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

PubMed

Singh, Nitesh Kumar; Ernst, Mathias; Liebscher, Volkmar; Fuellen, Georg; Taher, Leila

2016-10-20

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

Quantitative assessment of key parameters in qualitative vulnerability methods applied in karst systems based on an integrated numerical modelling approach

NASA Astrophysics Data System (ADS)

Doummar, Joanna; Kassem, Assaad

2017-04-01

In the framework of a three-year PEER (USAID/NSF) funded project, flow in a Karst system in Lebanon (Assal) dominated by snow and semi arid conditions was simulated and successfully calibrated using an integrated numerical model (MIKE-She 2016) based on high resolution input data and detailed catchment characterization. Point source infiltration and fast flow pathways were simulated by a bypass function and a high conductive lens respectively. The approach consisted of identifying all the factors used in qualitative vulnerability methods (COP, EPIK, PI, DRASTIC, GOD) applied in karst systems and to assess their influence on recharge signals in the different hydrological karst compartments (Atmosphere, Unsaturated zone and Saturated zone) based on the integrated numerical model. These parameters are usually attributed different weights according to their estimated impact on Groundwater vulnerability. The aim of this work is to quantify the importance of each of these parameters and outline parameters that are not accounted for in standard methods, but that might play a role in the vulnerability of a system. The spatial distribution of the detailed evapotranspiration, infiltration, and recharge signals from atmosphere to unsaturated zone to saturated zone was compared and contrasted among different surface settings and under varying flow conditions (e.g., in varying slopes, land cover, precipitation intensity, and soil properties as well point source infiltration). Furthermore a sensitivity analysis of individual or coupled major parameters allows quantifying their impact on recharge and indirectly on vulnerability. The preliminary analysis yields a new methodology that accounts for most of the factors influencing vulnerability while refining the weights attributed to each one of them, based on a quantitative approach.

A new synthetic biology approach allows transfer of an entire metabolic pathway from a medicinal plant to a biomass crop.

PubMed

Fuentes, Paulina; Zhou, Fei; Erban, Alexander; Karcher, Daniel; Kopka, Joachim; Bock, Ralph

2016-06-14

Artemisinin-based therapies are the only effective treatment for malaria, the most devastating disease in human history. To meet the growing demand for artemisinin and make it accessible to the poorest, an inexpensive and rapidly scalable production platform is urgently needed. Here we have developed a new synthetic biology approach, combinatorial supertransformation of transplastomic recipient lines (COSTREL), and applied it to introduce the complete pathway for artemisinic acid, the precursor of artemisinin, into the high-biomass crop tobacco. We first introduced the core pathway of artemisinic acid biosynthesis into the chloroplast genome. The transplastomic plants were then combinatorially supertransformed with cassettes for all additional enzymes known to affect flux through the artemisinin pathway. By screening large populations of COSTREL lines, we isolated plants that produce more than 120 milligram artemisinic acid per kilogram biomass. Our work provides an efficient strategy for engineering complex biochemical pathways into plants and optimizing the metabolic output.

Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach

PubMed Central

Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R.

2015-01-01

Motivation: Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. Results: In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: julio@iim.csic.es or saezrodriguez@ebi.ac.uk PMID:26002881

Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach.

PubMed

Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R

2015-09-15

Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary data are available at Bioinformatics online. julio@iim.csic.es or saezrodriguez@ebi.ac.uk. © The Author 2015. Published by Oxford University Press.

Graphite Web: web tool for gene set analysis exploiting pathway topology

PubMed Central

Sales, Gabriele; Calura, Enrica; Martini, Paolo; Romualdi, Chiara

2013-01-01

Graphite web is a novel web tool for pathway analyses and network visualization for gene expression data of both microarray and RNA-seq experiments. Several pathway analyses have been proposed either in the univariate or in the global and multivariate context to tackle the complexity and the interpretation of expression results. These methods can be further divided into ‘topological’ and ‘non-topological’ methods according to their ability to gain power from pathway topology. Biological pathways are, in fact, not only gene lists but can be represented through a network where genes and connections are, respectively, nodes and edges. To this day, the most used approaches are non-topological and univariate although they miss the relationship among genes. On the contrary, topological and multivariate approaches are more powerful, but difficult to be used by researchers without bioinformatic skills. Here we present Graphite web, the first public web server for pathway analysis on gene expression data that combines topological and multivariate pathway analyses with an efficient system of interactive network visualizations for easy results interpretation. Specifically, Graphite web implements five different gene set analyses on three model organisms and two pathway databases. Graphite Web is freely available at http://graphiteweb.bio.unipd.it/. PMID:23666626

Mechanisms of Severe Acute Respiratory Syndrome Coronavirus-Induced Acute Lung Injury

PubMed Central

Gralinski, Lisa E.; Bankhead, Armand; Jeng, Sophia; Menachery, Vineet D.; Proll, Sean; Belisle, Sarah E.; Matzke, Melissa; Webb-Robertson, Bobbie-Jo M.; Luna, Maria L.; Shukla, Anil K.; Ferris, Martin T.; Bolles, Meagan; Chang, Jean; Aicher, Lauri; Waters, Katrina M.; Smith, Richard D.; Metz, Thomas O.; Law, G. Lynn; Katze, Michael G.; McWeeney, Shannon; Baric, Ralph S.

2013-01-01

ABSTRACT Systems biology offers considerable promise in uncovering novel pathways by which viruses and other microbial pathogens interact with host signaling and expression networks to mediate disease severity. In this study, we have developed an unbiased modeling approach to identify new pathways and network connections mediating acute lung injury, using severe acute respiratory syndrome coronavirus (SARS-CoV) as a model pathogen. We utilized a time course of matched virologic, pathological, and transcriptomic data within a novel methodological framework that can detect pathway enrichment among key highly connected network genes. This unbiased approach produced a high-priority list of 4 genes in one pathway out of over 3,500 genes that were differentially expressed following SARS-CoV infection. With these data, we predicted that the urokinase and other wound repair pathways would regulate lethal versus sublethal disease following SARS-CoV infection in mice. We validated the importance of the urokinase pathway for SARS-CoV disease severity using genetically defined knockout mice, proteomic correlates of pathway activation, and pathological disease severity. The results of these studies demonstrate that a fine balance exists between host coagulation and fibrinolysin pathways regulating pathological disease outcomes, including diffuse alveolar damage and acute lung injury, following infection with highly pathogenic respiratory viruses, such as SARS-CoV. PMID:23919993

Principles of Pharmacology and Toxicology Also Govern Effects of Chemicals on the Endocrine System.

PubMed

Autrup, Herman; Barile, Frank A; Blaauboer, Bas J; Degen, Gisela H; Dekant, Wolfgang; Dietrich, Daniel; Domingo, Jose L; Gori, Gio Batta; Greim, Helmuth; Hengstler, Jan G; Kacew, Sam; Marquardt, Hans; Pelkonen, Olavi; Savolainen, Kai; Vermeulen, Nico P

2015-07-01

The present debate on chemicals with Hormonal activity, often termed 'endocrine disruptors', is highly controversial and includes challenges of the present paradigms used in toxicology and in hazard identification and risk characterization. In our opinion, chemicals with hormonal activity can be subjected to the well-evaluated health risk characterization approach used for many years including adverse outcome pathways. Many of the points arguing for a specific approach for risk characterization of chemicals with hormonal activity are based on highly speculative conclusions. These conclusions are not well supported when evaluating the available information. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mizrachi, Eshchar; Verbeke, Lieven; Christie, Nanette

As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We havemore » applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. Furthermore, a more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.« less

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing

DOE PAGES

Mizrachi, Eshchar; Verbeke, Lieven; Christie, Nanette; ...

2017-01-17

As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We havemore » applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. Furthermore, a more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.« less

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing.

PubMed

Mizrachi, Eshchar; Verbeke, Lieven; Christie, Nanette; Fierro, Ana C; Mansfield, Shawn D; Davis, Mark F; Gjersing, Erica; Tuskan, Gerald A; Van Montagu, Marc; Van de Peer, Yves; Marchal, Kathleen; Myburg, Alexander A

2017-01-31

As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We have applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. A more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.

A critique of the molecular target-based drug discovery paradigm based on principles of metabolic control: advantages of pathway-based discovery.

PubMed

Hellerstein, Marc K

2008-01-01

Contemporary drug discovery and development (DDD) is dominated by a molecular target-based paradigm. Molecular targets that are potentially important in disease are physically characterized; chemical entities that interact with these targets are identified by ex vivo high-throughput screening assays, and optimized lead compounds enter testing as drugs. Contrary to highly publicized claims, the ascendance of this approach has in fact resulted in the lowest rate of new drug approvals in a generation. The primary explanation for low rates of new drugs is attrition, or the failure of candidates identified by molecular target-based methods to advance successfully through the DDD process. In this essay, I advance the thesis that this failure was predictable, based on modern principles of metabolic control that have emerged and been applied most forcefully in the field of metabolic engineering. These principles, such as the robustness of flux distributions, address connectivity relationships in complex metabolic networks and make it unlikely a priori that modulating most molecular targets will have predictable, beneficial functional outcomes. These same principles also suggest, however, that unexpected therapeutic actions will be common for agents that have any effect (i.e., that complexity can be exploited therapeutically). A potential operational solution (pathway-based DDD), based on observability rather than predictability, is described, focusing on emergent properties of key metabolic pathways in vivo. Recent examples of pathway-based DDD are described. In summary, the molecular target-based DDD paradigm is built on a naïve and misleading model of biologic control and is not heuristically adequate for advancing the mission of modern therapeutics. New approaches that take account of and are built on principles described by metabolic engineers are needed for the next generation of DDD.

A Trans-omics Mathematical Analysis Reveals Novel Functions of the Ornithine Metabolic Pathway in Cancer Stem Cells

NASA Astrophysics Data System (ADS)

Koseki, Jun; Matsui, Hidetoshi; Konno, Masamitsu; Nishida, Naohiro; Kawamoto, Koichi; Kano, Yoshihiro; Mori, Masaki; Doki, Yuichiro; Ishii, Hideshi

2016-02-01

Bioinformatics and computational modelling are expected to offer innovative approaches in human medical science. In the present study, we performed computational analyses and made predictions using transcriptome and metabolome datasets obtained from fluorescence-based visualisations of chemotherapy-resistant cancer stem cells (CSCs) in the human oesophagus. This approach revealed an uncharacterized role for the ornithine metabolic pathway in the survival of chemotherapy-resistant CSCs. The present study fastens this rationale for further characterisation that may lead to the discovery of innovative drugs against robust CSCs.

A procurement-based pathway for promoting public health: innovative purchasing approaches for state and local government agencies.

PubMed

Noonan, Kathleen; Miller, Dorothy; Sell, Katherine; Rubin, David

2013-11-01

Through their purchasing powers, government agencies can play a critical role in leveraging markets to create healthier foods. In the United States, state and local governments are implementing creative approaches to procuring healthier foods, moving beyond the traditional regulatory relationship between government and vendors. They are forging new partnerships between government, non-profits, and researchers to increase healthier purchasing. On the basis of case examples, this article proposes a pathway in which state and local government agencies can use the procurement cycle to improve healthy eating.

Novel Neuroprotective Multicomponent Therapy for Amyotrophic Lateral Sclerosis Designed by Networked Systems

PubMed Central

Herrando-Grabulosa, Mireia; Mulet, Roger; Pujol, Albert; Mas, José Manuel; Navarro, Xavier; Aloy, Patrick; Coma, Mireia; Casas, Caty

2016-01-01

Amyotrophic Lateral Sclerosis is a fatal, progressive neurodegenerative disease characterized by loss of motor neuron function for which there is no effective treatment. One of the main difficulties in developing new therapies lies on the multiple events that contribute to motor neuron death in amyotrophic lateral sclerosis. Several pathological mechanisms have been identified as underlying events of the disease process, including excitotoxicity, mitochondrial dysfunction, oxidative stress, altered axonal transport, proteasome dysfunction, synaptic deficits, glial cell contribution, and disrupted clearance of misfolded proteins. Our approach in this study was based on a holistic vision of these mechanisms and the use of computational tools to identify polypharmacology for targeting multiple etiopathogenic pathways. By using a repositioning analysis based on systems biology approach (TPMS technology), we identified and validated the neuroprotective potential of two new drug combinations: Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine. In addition, we estimated their molecular mechanisms of action in silico and validated some of these results in a well-established in vitro model of amyotrophic lateral sclerosis based on cultured spinal cord slices. The results verified that Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine promote neuroprotection of motor neurons and reduce microgliosis. PMID:26807587

The process of justifying assisted reproductive technologies in Iran.

PubMed

Gooshki, Ehsan Shamsi; Allahbedashti, Neda

2015-01-01

Infertility is medically defined as one year of unprotected intercourse that does not result in pregnancy. Infertility is a noticeable medical problem in Iran, and about a quarter of Iranian couples experience primary infertility at some point in their lives. Since having children is a basic social value in Iran, infertility has an adverse effect on the health of the couple and affects their well-being. The various methods of assisting infertile couples raise several ethical questions and touch upon certain sensitive points. Although the present Iranian legislative system, which is based on the Shi'a school of Islam, has legalised some aspects of assisted reproductive technologies (ARTs), given the absence of a general officially ratified act (official pathway), such medical interventions are usually justified through a fatwa system (non-official pathway). Officially registered married couples can access almost all ART methods, including third-party gamete donation, if they use such pathways. The process of justifying ART interventions generally began when in vitro fertilisation was given the nod and later, Ayatollah Khamenei (the political-religious leader of the country) issued a fatwa which permitted gamete donation by third parties. This open juristic approach paved the way for the ratification of the Embryo Donation to Infertile Spouses Act in 2003.

Identifying viable regulatory and innovation pathways for regenerative medicine: a case study of cultured red blood cells.

PubMed

Mittra, J; Tait, J; Mastroeni, M; Turner, M L; Mountford, J C; Bruce, K

2015-01-25

The creation of red blood cells for the blood transfusion markets represents a highly innovative application of regenerative medicine with a medium term (5-10 year) prospect for first clinical studies. This article describes a case study analysis of a project to derive red blood cells from human embryonic stem cells, including the systemic challenges arising from (i) the selection of appropriate and viable regulatory protocols and (ii) technological constraints related to stem cell manufacture and scale up to clinical Good Manufacturing Practice (GMP) standard. The method used for case study analysis (Analysis of Life Science Innovation Systems (ALSIS)) is also innovative, demonstrating a new approach to social and natural science collaboration to foresight product development pathways. Issues arising along the development pathway include cell manufacture and scale-up challenges, affected by regulatory demands emerging from the innovation ecosystem (preclinical testing and clinical trials). Our discussion reflects on the efforts being made by regulators to adapt the current pharmaceuticals-based regulatory model to an allogeneic regenerative medicine product and the broader lessons from this case study for successful innovation and translation of regenerative medicine therapies, including the role of methodological and regulatory innovation in future development in the field. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

A detailed view on sulphur metabolism at the cellular and whole-plant level illustrates challenges in metabolite flux analyses.

PubMed

Rennenberg, Heinz; Herschbach, Cornelia

2014-11-01

Understanding the dynamics of physiological process in the systems biology era requires approaches at the genome, transcriptome, proteome, and metabolome levels. In this context, metabolite flux experiments have been used in mapping metabolite pathways and analysing metabolic control. In the present review, sulphur metabolism was taken to illustrate current challenges of metabolic flux analyses. At the cellular level, restrictions in metabolite flux analyses originate from incomplete knowledge of the compartmentation network of metabolic pathways. Transport of metabolites through membranes is usually not considered in flux experiments but may be involved in controlling the whole pathway. Hence, steady-state and snapshot readings need to be expanded to time-course studies in combination with compartment-specific metabolite analyses. Because of species-specific differences, differences between tissues, and stress-related responses, the quantitative significance of different sulphur sinks has to be elucidated; this requires the development of methods for whole-sulphur metabolome approaches. Different cell types can contribute to metabolite fluxes to different extents at the tissue and organ level. Cell type-specific analyses are needed to characterize these contributions. Based on such approaches, metabolite flux analyses can be expanded to the whole-plant level by considering long-distance transport and, thus, the interaction of roots and the shoot in metabolite fluxes. However, whole-plant studies need detailed empirical and mathematical modelling that have to be validated by experimental analyses. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

In vitro reconstitution of mevalonate pathway and targeted engineering of farnesene overproduction in Escherichia coli.

PubMed

Zhu, Fayin; Zhong, Xiaofang; Hu, Mengzhu; Lu, Lei; Deng, Zixin; Liu, Tiangang

2014-07-01

Approaches using metabolic engineering and synthetic biology to overproduce terpenoids, such as the precursors of taxol and artemisinin, in microbial systems have achieved initial success. However, due to the lack of steady-state kinetic information and incomplete understanding of the terpenoid biosynthetic pathway, it has been difficult to build a highly efficient, universal system. Here, we reconstituted the mevalonate pathway to produce farnesene (a precursor of new jet fuel) in vitro using purified protein components. The information from this in vitro reconstituted system guided us to rationally optimize farnesene production in E. coli by quantitatively overexpressing each component. Targeted proteomic assays and intermediate assays were used to determine the metabolic status of each mutant. Through targeted engineering, farnesene production could be increased predictably step by step, up to 1.1 g/L (∼ 2,000 fold) 96 h after induction at the shake-flask scale. The strategy developed to release the potential of the mevalonate pathway for terpenoid overproduction should also work in other multistep synthetic pathways. © 2014 Wiley Periodicals, Inc.

R-Based Software for the Integration of Pathway Data into Bioinformatic Algorithms

PubMed Central

Kramer, Frank; Bayerlová, Michaela; Beißbarth, Tim

2014-01-01

Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools. PMID:24833336

Junction detection and pathway selection

NASA Astrophysics Data System (ADS)

Peck, Alex N.; Lim, Willie Y.; Breul, Harry T.

1992-02-01

The ability to detect junctions and make choices among the possible pathways is important for autonomous navigation. In our script-based navigation approach where a journey is specified as a script of high-level instructions, actions are frequently referenced to junctions, e.g., `turn left at the intersection.' In order for the robot to carry out these kind of instructions, it must be able (1) to detect an intersection (i.e., an intersection of pathways), (2) know that there are several possible pathways it can take, and (3) pick the pathway consistent with the high level instruction. In this paper we describe our implementation of the ability to detect junctions in an indoor environment, such as corners, T-junctions and intersections, using sonar. Our approach uses a combination of partial scan of the local environment and recognition of sonar signatures of certain features of the junctions. In the case where the environment is known, we use additional sensor information (such as compass bearings) to help recognize the specific junction. In general, once a junction is detected and its type known, the number of possible pathways can be deduced and the correct pathway selected. Then the appropriate behavior for negotiating the junction is activated.

An Adaptive Genetic Association Test Using Double Kernel Machines

PubMed Central

Zhan, Xiang; Epstein, Michael P.; Ghosh, Debashis

2014-01-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study. PMID:26640602

Big data to smart data in Alzheimer's disease: Real-world examples of advanced modeling and simulation.

PubMed

Haas, Magali; Stephenson, Diane; Romero, Klaus; Gordon, Mark Forrest; Zach, Neta; Geerts, Hugo

2016-09-01

Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.