Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology

PubMed Central

Sudhir, Putty-Reddy; Chen, Chung-Hsuan

2016-01-01

A protein complex consists of two or more proteins that are linked together through protein–protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples. PMID:27011181

Unlocking Proteomic Heterogeneity in Complex Diseases through Visual Analytics

PubMed Central

Bhavnani, Suresh K.; Dang, Bryant; Bellala, Gowtham; Divekar, Rohit; Visweswaran, Shyam; Brasier, Allan; Kurosky, Alex

2015-01-01

Despite years of preclinical development, biological interventions designed to treat complex diseases like asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks have been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine. PMID:25684269

Technical Development and Application of Soft Computing in Agricultural and Biological Engineering

USDA-ARS?s Scientific Manuscript database

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and...

Development of Soft Computing and Applications in Agricultural and Biological Engineering

USDA-ARS?s Scientific Manuscript database

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and...

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.

Comparative exploration of multidimensional flow cytometry software: a model approach evaluating T cell polyfunctional behavior.

PubMed

Spear, Timothy T; Nishimura, Michael I; Simms, Patricia E

2017-08-01

Advancement in flow cytometry reagents and instrumentation has allowed for simultaneous analysis of large numbers of lineage/functional immune cell markers. Highly complex datasets generated by polychromatic flow cytometry require proper analytical software to answer investigators' questions. A problem among many investigators and flow cytometry Shared Resource Laboratories (SRLs), including our own, is a lack of access to a flow cytometry-knowledgeable bioinformatics team, making it difficult to learn and choose appropriate analysis tool(s). Here, we comparatively assess various multidimensional flow cytometry software packages for their ability to answer a specific biologic question and provide graphical representation output suitable for publication, as well as their ease of use and cost. We assessed polyfunctional potential of TCR-transduced T cells, serving as a model evaluation, using multidimensional flow cytometry to analyze 6 intracellular cytokines and degranulation on a per-cell basis. Analysis of 7 parameters resulted in 128 possible combinations of positivity/negativity, far too complex for basic flow cytometry software to analyze fully. Various software packages were used, analysis methods used in each described, and representative output displayed. Of the tools investigated, automated classification of cellular expression by nonlinear stochastic embedding (ACCENSE) and coupled analysis in Pestle/simplified presentation of incredibly complex evaluations (SPICE) provided the most user-friendly manipulations and readable output, evaluating effects of altered antigen-specific stimulation on T cell polyfunctionality. This detailed approach may serve as a model for other investigators/SRLs in selecting the most appropriate software to analyze complex flow cytometry datasets. Further development and awareness of available tools will help guide proper data analysis to answer difficult biologic questions arising from incredibly complex datasets. © Society for Leukocyte Biology.

From Synthesis to Biological Impact of Pd (II) Complexes: Synthesis, Characterization, and Antimicrobial and Scavenging Activity

PubMed Central

Sharma, Nitin Kumar; Ameta, Rakesh Kumar; Singh, Man

2016-01-01

The Pd (II) complexes with a series of halosubstituted benzylamine ligands (BLs) have been synthesized and characterized with different spectroscopic technique such as FTIR, UV/Vis, LCMS, 1H, and 13C NMR. Their molecular sustainability in different solvents such as DMSO, DMSO : H2O, and DMSO : PBS at physiological condition (pH 7.2) was determined by UV/Vis spectrophotometer. The in vitro antibacterial and antifungal activities of the complexes were investigated against Gram-positive and Gram-negative microbes and two different fungi indicated their significant biological potential. Additionally, their antioxidant activity has been analyzed with DPPH• free radical through spectrophotometric method and the result inferred them as an antioxidant. The stronger antibacterial and antioxidant activities of the synthesized complexes suggested them as a stronger antimicrobial agent. Our study advances the biological importance of palladium (II) amine complexes in the field of antimicrobial and antioxidant activities. PMID:27119023

On the interplay between mathematics and biology. Hallmarks toward a new systems biology

NASA Astrophysics Data System (ADS)

Bellomo, Nicola; Elaiw, Ahmed; Althiabi, Abdullah M.; Alghamdi, Mohammed Ali

2015-03-01

This paper proposes a critical analysis of the existing literature on mathematical tools developed toward systems biology approaches and, out of this overview, develops a new approach whose main features can be briefly summarized as follows: derivation of mathematical structures suitable to capture the complexity of biological, hence living, systems, modeling, by appropriate mathematical tools, Darwinian type dynamics, namely mutations followed by selection and evolution. Moreover, multiscale methods to move from genes to cells, and from cells to tissue are analyzed in view of a new systems biology approach.

Bioinformatics and Cancer

Cancer.gov

Researchers take on challenges and opportunities to mine "Big Data" for answers to complex biological questions. Learn how bioinformatics uses advanced computing, mathematics, and technological platforms to store, manage, analyze, and understand data.

Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.

PubMed

Popescu, George V; Noutsos, Christos; Popescu, Sorina C

2016-01-01

In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

On the interplay between mathematics and biology: hallmarks toward a new systems biology.

PubMed

Bellomo, Nicola; Elaiw, Ahmed; Althiabi, Abdullah M; Alghamdi, Mohammed Ali

2015-03-01

This paper proposes a critical analysis of the existing literature on mathematical tools developed toward systems biology approaches and, out of this overview, develops a new approach whose main features can be briefly summarized as follows: derivation of mathematical structures suitable to capture the complexity of biological, hence living, systems, modeling, by appropriate mathematical tools, Darwinian type dynamics, namely mutations followed by selection and evolution. Moreover, multiscale methods to move from genes to cells, and from cells to tissue are analyzed in view of a new systems biology approach. Copyright © 2014 Elsevier B.V. All rights reserved.

Interactome Networks and Human Disease

PubMed Central

Vidal, Marc; Cusick, Michael E.; Barabási, Albert-László

2011-01-01

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease. PMID:21414488

A toolbox for discrete modelling of cell signalling dynamics.

PubMed

Paterson, Yasmin Z; Shorthouse, David; Pleijzier, Markus W; Piterman, Nir; Bendtsen, Claus; Hall, Benjamin A; Fisher, Jasmin

2018-06-18

In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.

Fitness and Individuality in Complex Life Cycles.

PubMed

Herron, Matthew D

2016-12-01

Complex life cycles are common in the eukaryotic world, and they complicate the question of how to define individuality. Using a bottom-up, gene-centric approach, I consider the concept of fitness in the context of complex life cycles. I analyze the fitness effects of an allele (or a trait) on different biological units within a complex life history and how these effects drive evolutionary change within populations. Based on these effects, I attempt to construct a concept of fitness that accurately predicts evolutionary change in the context of complex life cycles.

A meta-analysis to evaluate the cellular processes regulated by the interactome of endogenous and over-expressed estrogen receptor alpha.

PubMed

Simões, Joana; Amado, Francisco M; Vitorino, Rui; Helguero, Luisa A

2015-01-01

The nature of the proteins complexes that regulate ERα subcellular localization and activity is still an open question in breast cancer biology. Identification of such complexes will help understand development of endocrine resistance in ER+ breast cancer. Mass spectrometry (MS) has allowed comprehensive analysis of the ERα interactome. We have compared six published works analyzing the ERα interactome of MCF-7 and HeLa cells in order to identify a shared or different pathway-related fingerprint. Overall, 806 ERα interacting proteins were identified. The cellular processes were differentially represented according to the ERα purification methodology, indicating that the methodologies used are complementary. While in MCF-7 cells, the interactome of endogenous and over-expressed ERα essentially represents the same biological processes and cellular components, the proteins identified were not over-lapping; thus, suggesting that the biological response may differ as the regulatory/participating proteins in these complexes are different. Interestingly, biological processes uniquely associated to ERα over-expressed in HeLa cell line included L-serine biosynthetic process, cellular amino acid biosynthetic process and cell redox homeostasis. In summary, all the approaches analyzed in this meta-analysis are valid and complementary; in particular, for those cases where the processes occur at low frequency with normal ERα levels, and can be identified when the receptor is over-expressed. However special effort should be put into validating these findings in cells expressing physiological ERα levels.

Compressed learning and its applications to subcellular localization.

PubMed

Zheng, Zhong-Long; Guo, Li; Jia, Jiong; Xie, Chen-Mao; Zeng, Wen-Cai; Yang, Jie

2011-09-01

One of the main challenges faced by biological applications is to predict protein subcellular localization in automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological systems into account. Traditionally, such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressed Sensing) theory, we propose the methodology which performs compressed learning with a sparseness criterion such that feature selection and dimension reduction are merged into one analysis. The proposed methodology decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse methods are quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.

Connecting qualitative observation and quantitative measurement for enhancing quantitative literacy in plant anatomy course

NASA Astrophysics Data System (ADS)

Nuraeni, E.; Rahmat, A.

2018-05-01

Forming of cognitive schemes of plant anatomy concepts is performed by processing of qualitative and quantitative data obtained from microscopic observations. To enhancing student’s quantitative literacy, strategy of plant anatomy course was modified by adding the task to analyze quantitative data produced by quantitative measurement of plant anatomy guided by material course. Participant in this study was 24 biology students and 35 biology education students. Quantitative Literacy test, complex thinking in plant anatomy test and questioner used to evaluate the course. Quantitative literacy capability data was collected by quantitative literacy test with the rubric from the Association of American Colleges and Universities, Complex thinking in plant anatomy by test according to Marzano and questioner. Quantitative literacy data are categorized according to modified Rhodes and Finley categories. The results showed that quantitative literacy of biology education students is better than biology students.

Interactive and coordinated visualization approaches for biological data analysis.

PubMed

Cruz, António; Arrais, Joel P; Machado, Penousal

2018-03-26

The field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein-protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.

Rapid classification of biological components

DOEpatents

Thompson, Vicki S.; Barrett, Karen B.; Key, Diane E.

2013-10-15

A method is disclosed for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an illustrative embodiment of the invention, the analyte is a drug, such as marijuana, cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method involves attaching antigens to a surface of a solid support in a preselected pattern to form an array wherein the locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to antigens in the array, thereby forming immune complexes; washing away antibodies that do not form immune complexes; and detecting the immune complexes, thereby forming an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to a subject's identity.

Rapid classification of biological components

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

Thompson, Vicki S.; Barrett, Karen B.; Key, Diane E.

A method is disclosed for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an illustrative embodiment of the invention, the analyte is a drug, such as marijuana, cocaine, methamphetamine, methyltestosterone, or mesterolone. The method involves attaching antigens to the surface of a solid support in a preselected pattern to form an array wherein the locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to antigens in the array, thereby forming immunemore » complexes; washing away antibodies that do form immune complexes; and detecting the immune complexes, thereby forming an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to the subject's identity.« less

Inference, simulation, modeling, and analysis of complex networks, with special emphasis on complex networks in systems biology

NASA Astrophysics Data System (ADS)

Christensen, Claire Petra

Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author's own publications have contributed network inference, simulation, modeling, and analysis methods to the much larger body of work in systems biology, and indeed, in network science. The aim of this thesis is therefore twofold: to present this original work in the historical context of network science, but also to provide sufficient review and reference regarding complex systems (with an emphasis on complex networks in systems biology) and tools and techniques for their inference, simulation, analysis, and modeling, such that the reader will be comfortable in seeking out further information on the subject. The review-like Chapters 1, 2, and 4 are intended to convey the co-evolution of network science and the slow but noticeable breakdown of boundaries between disciplines in academia as research and comparison of diverse systems has brought to light the shared properties of these systems. It is the author's hope that theses chapters impart some sense of the remarkable and rapid progress in complex systems research that has led to this unprecedented academic synergy. Chapters 3 and 5 detail the author's original work in the context of complex systems research. Chapter 3 presents the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. These networks are then analyzed from a graph theoretical perspective, and their biological viability is critiqued by comparing the networks' graph theoretical properties to those of other biological systems. The results of topological perturbation analyses revealing commonalities in behavior at multiple levels of complexity are also presented, and are shown to be an invaluable means by which to ascertain the level of complexity to which the network inference process is robust to noise. Chapter 5 outlines a learning algorithm for the development of a realistic, evolving social network (a city) into which a disease is introduced. The results of simulations in populations spanning two orders of magnitude are compared to prevaccine era measles data for England and Wales and demonstrate that the simulations are able to capture the quantitative and qualitative features of epidemics in populations as small as 10,000 people. The work presented in Chapter 5 validates the utility of network simulation in concurrently probing contact network dynamics and disease dynamics.

Plant Systems Biology at the Single-Cell Level.

PubMed

Libault, Marc; Pingault, Lise; Zogli, Prince; Schiefelbein, John

2017-11-01

Our understanding of plant biology is increasingly being built upon studies using 'omics and system biology approaches performed at the level of the entire plant, organ, or tissue. Although these approaches open new avenues to better understand plant biology, they suffer from the cellular complexity of the analyzed sample. Recent methodological advances now allow plant scientists to overcome this limitation and enable biological analyses of single-cells or single-cell-types. Coupled with the development of bioinformatics and functional genomics resources, these studies provide opportunities for high-resolution systems analyses of plant phenomena. In this review, we describe the recent advances, current challenges, and future directions in exploring the biology of single-cells and single-cell-types to enhance our understanding of plant biology as a system. Copyright © 2017 Elsevier Ltd. All rights reserved.

Literature Mining and Knowledge Discovery Tools for Virtual Tissues

EPA Science Inventory

Virtual Tissues (VTs) are in silico models that simulate the cellular fabric of tissues to analyze complex relationships and predict multicellular behaviors in specific biological systems such as the mature liver (v-Liver™) or developing embryo (v-Embryo™). VT models require inpu...

Native Mass Spectrometry: What is in the Name?

NASA Astrophysics Data System (ADS)

Leney, Aneika C.; Heck, Albert J. R.

2017-01-01

Electrospray ionization mass spectrometry (ESI-MS) is nowadays one of the cornerstones of biomolecular mass spectrometry and proteomics. Advances in sample preparation and mass analyzers have enabled researchers to extract much more information from biological samples than just the molecular weight. In particular, relevant for structural biology, noncovalent protein-protein and protein-ligand complexes can now also be analyzed by MS. For these types of analyses, assemblies need to be retained in their native quaternary state in the gas phase. This initial small niche of biomolecular mass spectrometry, nowadays often referred to as "native MS," has come to maturation over the last two decades, with dozens of laboratories using it to study mostly protein assemblies, but also DNA and RNA-protein assemblies, with the goal to define structure-function relationships. In this perspective, we describe the origins of and (re)define the term native MS, portraying in detail what we meant by "native MS," when the term was coined and also describing what it does (according to us) not entail. Additionally, we describe a few examples highlighting what native MS is, showing its successes to date while illustrating the wide scope this technology has in solving complex biological questions.

Advantages of tandem LC-MS for the rapid assessment of tissue-specific metabolic complexity using a pentafluorophenylpropyl stationary phase

PubMed Central

Lv, Haitao; Palacios, Gustavo; Hartil, Kirsten; Kurland, Irwin J.

2014-01-01

In this study a UPLC-tandem (Waters Xevo TQ) MRM based MS method was developed for rapid, broad profiling of hydrophilic metabolites from biological samples, in either positive or negative ion modes without the need for an ion pairing reagent, using a reversed-phase pentafluorophenylpropyl (PFPP) column. The developed method was successfully applied to analyze various biological samples from C57BL/6 mice; including urine, duodenum, liver, plasma, kidney, heart, and skeletal muscle. As result, a total 112 of hydrophilic metabolites were detected within 8 min of running time to obtain a metabolite profile of the biological samples. The analysis of this number of hydrophilic metabolites is significantly faster than previous studies. Classification separation for metabolites from different tissues was globally analyzed by PCA, PLS-DA and HCA biostatistical methods. Overall, most of the hydrophilic metabolites were found to have a “fingerprint” characteristic of tissue dependency. In general, a higher level of most metabolites was found in urine, duodenum and kidney. Altogether, these results suggest that this method has potential application for targeted metabolomic analyzes of hydrophilic metabolites in a wide ranges of biological samples. PMID:21322650

Advantages of tandem LC-MS for the rapid assessment of tissue-specific metabolic complexity using a pentafluorophenylpropyl stationary phase.

PubMed

Lv, Haitao; Palacios, Gustavo; Hartil, Kirsten; Kurland, Irwin J

2011-04-01

In this study, a tandem LC-MS (Waters Xevo TQ) MRM-based MS method was developed for rapid, broad profiling of hydrophilic metabolites from biological samples, in either positive or negative ion modes without the need for an ion pairing reagent, using a reversed-phase pentafluorophenylpropyl (PFPP) column. The developed method was successfully applied to analyze various biological samples from C57BL/6 mice, including urine, duodenum, liver, plasma, kidney, heart, and skeletal muscle. As result, a total 112 of hydrophilic metabolites were detected within 8 min of running time to obtain a metabolite profile of the biological samples. The analysis of this number of hydrophilic metabolites is significantly faster than previous studies. Classification separation for metabolites from different tissues was globally analyzed by PCA, PLS-DA and HCA biostatistical methods. Overall, most of the hydrophilic metabolites were found to have a "fingerprint" characteristic of tissue dependency. In general, a higher level of most metabolites was found in urine, duodenum, and kidney. Altogether, these results suggest that this method has potential application for targeted metabolomic analyzes of hydrophilic metabolites in a wide ranges of biological samples.

The Dichotomy in Degree Correlation of Biological Networks

PubMed Central

Hao, Dapeng; Li, Chuanxing

2011-01-01

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled. PMID:22164269

Evolutionary cell biology: functional insight from "endless forms most beautiful".

PubMed

Richardson, Elisabeth; Zerr, Kelly; Tsaousis, Anastasios; Dorrell, Richard G; Dacks, Joel B

2015-12-15

In animal and fungal model organisms, the complexities of cell biology have been analyzed in exquisite detail and much is known about how these organisms function at the cellular level. However, the model organisms cell biologists generally use include only a tiny fraction of the true diversity of eukaryotic cellular forms. The divergent cellular processes observed in these more distant lineages are still largely unknown in the general scientific community. Despite the relative obscurity of these organisms, comparative studies of them across eukaryotic diversity have had profound implications for our understanding of fundamental cell biology in all species and have revealed the evolution and origins of previously observed cellular processes. In this Perspective, we will discuss the complexity of cell biology found across the eukaryotic tree, and three specific examples of where studies of divergent cell biology have altered our understanding of key functional aspects of mitochondria, plastids, and membrane trafficking. © 2015 Richardson et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

A Novel Framework for the Comparative Analysis of Biological Networks

PubMed Central

Pache, Roland A.; Aloy, Patrick

2012-01-01

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes. PMID:22363585

Structural interaction fingerprints: a new approach to organizing, mining, analyzing, and designing protein-small molecule complexes.

PubMed

Singh, Juswinder; Deng, Zhan; Narale, Gaurav; Chuaqui, Claudio

2006-01-01

The combination of advances in structure-based drug design efforts in the pharmaceutical industry in parallel with structural genomics initiatives in the public domain has led to an explosion in the number of structures of protein-small molecule complexes structures. This information has critical importance to both the understanding of the structural basis for molecular recognition in biological systems and the design of better drugs. A significant challenge exists in managing this vast amount of data and fully leveraging it. Here, we review our work to develop a simple, fast way to store, organize, mine, and analyze large numbers of protein-small molecule complexes. We illustrate the utility of the approach to the management of inhibitor complexes from the protein kinase family. Finally, we describe our recent efforts in applying this method to the design of target-focused chemical libraries.

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

Hartmann, Anja, E-mail: hartmann@ipk-gatersleben.de; Schreiber, Falk; Martin-Luther-University Halle-Wittenberg, Halle

The characterization of biological systems with respect to their behavior and functionality based on versatile biochemical interactions is a major challenge. To understand these complex mechanisms at systems level modeling approaches are investigated. Different modeling formalisms allow metabolic models to be analyzed depending on the question to be solved, the biochemical knowledge and the availability of experimental data. Here, we describe a method for an integrative analysis of the structure and dynamics represented by qualitative and quantitative metabolic models. Using various formalisms, the metabolic model is analyzed from different perspectives. Determined structural and dynamic properties are visualized in the contextmore » of the metabolic model. Interaction techniques allow the exploration and visual analysis thereby leading to a broader understanding of the behavior and functionality of the underlying biological system. The System Biology Metabolic Model Framework (SBM{sup 2} – Framework) implements the developed method and, as an example, is applied for the integrative analysis of the crop plant potato.« less

Metabolic Compartmentation – A System Level Property of Muscle Cells

PubMed Central

Saks, Valdur; Beraud, Nathalie; Wallimann, Theo

2008-01-01

Problems of quantitative investigation of intracellular diffusion and compartmentation of metabolites are analyzed. Principal controversies in recently published analyses of these problems for the living cells are discussed. It is shown that the formal theoretical analysis of diffusion of metabolites based on Fick's equation and using fixed diffusion coefficients for diluted homogenous aqueous solutions, but applied for biological systems in vivo without any comparison with experimental results, may lead to misleading conclusions, which are contradictory to most biological observations. However, if the same theoretical methods are used for analysis of actual experimental data, the apparent diffusion constants obtained are orders of magnitude lower than those in diluted aqueous solutions. Thus, it can be concluded that local restrictions of diffusion of metabolites in a cell are a system-level properties caused by complex structural organization of the cells, macromolecular crowding, cytoskeletal networks and organization of metabolic pathways into multienzyme complexes and metabolons. This results in microcompartmentation of metabolites, their channeling between enzymes and in modular organization of cellular metabolic networks. The perspectives of further studies of these complex intracellular interactions in the framework of Systems Biology are discussed. PMID:19325782

Rapid classification of biological components

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

Thompson, Vicki S.; Barrett, Karen B.; Key, Diane E.

A method is disclosed for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an illustrative embodiment of the invention, the analyte is a drug, such as marijuana, Cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method involves attaching antigens of the surface of a solid support in a preselected pattern to form an array wherein the locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to antigens in the array,more » thereby forming immune complexes; washing away antibodies that do not form immune complexes; and detecting the immune complexes, thereby forming an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to a subject's identity.« less

Rapid classification of biological components

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

Thompson, Vicki S.; Barrett, Karen B.; Key, Diane E.

A method is disclosed for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an illustrative embodiment of the invention, the analyte is a drug, such as marijuana, cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method involves attaching antigens to a surface of a solid support in a preselected pattern to form an array wherein the locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to antigens in the array,more » thereby forming immune complexes; washing away antibodies that do not form immune complexes; and detecting the immune complexes, thereby forming an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to a subject's identity.« less

Precision phenotyping, panomics, and system-level bioinformatics to delineate complex biologies of atherosclerosis: rationale and design of the "Genetic Loci and the Burden of Atherosclerotic Lesions" study.

PubMed

Voros, Szilard; Maurovich-Horvat, Pal; Marvasty, Idean B; Bansal, Aruna T; Barnes, Michael R; Vazquez, Gustavo; Murray, Sarah S; Voros, Viktor; Merkely, Bela; Brown, Bradley O; Warnick, G Russell

2014-01-01

Complex biological networks of atherosclerosis are largely unknown. The main objective of the Genetic Loci and the Burden of Atherosclerotic Lesions study is to assemble comprehensive biological networks of atherosclerosis using advanced cardiovascular imaging for phenotyping, a panomic approach to identify underlying genomic, proteomic, metabolomic, and lipidomic underpinnings, analyzed by systems biology-driven bioinformatics. By design, this is a hypothesis-free unbiased discovery study collecting a large number of biologically related factors to examine biological associations between genomic, proteomic, metabolomic, lipidomic, and phenotypic factors of atherosclerosis. The Genetic Loci and the Burden of Atherosclerotic Lesions study (NCT01738828) is a prospective, multicenter, international observational study of atherosclerotic coronary artery disease. Approximately 7500 patients are enrolled and undergo non-contrast-enhanced coronary calcium scanning by CT for the detection and quantification of coronary artery calcium, as well as coronary artery CT angiography for the detection and quantification of plaque, stenosis, and overall coronary artery disease burden. In addition, patients undergo whole genome sequencing, DNA methylation, whole blood-based transcriptome sequencing, unbiased proteomics based on mass spectrometry, as well as metabolomics and lipidomics on a mass spectrometry platform. The study is analyzed in 3 subsequent phases, and each phase consists of a discovery cohort and an independent validation cohort. For the primary analysis, the primary phenotype will be the presence of any atherosclerotic plaque, as detected by cardiac CT. Additional phenotypic analyses will include per patient maximal luminal stenosis defined as 50% and 70% diameter stenosis. Single-omic and multi-omic associations will be examined for each phenotype; putative biomarkers will be assessed for association, calibration, discrimination, and reclassification. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

USGS Publications Warehouse

Schroeder, Anthony L.; Martinovic-Weigelt, Dalma; Ankley, Gerald T.; Lee, Kathy E.; Garcia-Reyero, Natalia; Perkins, Edward J.; Schoenfuss, Heiko L.; Villeneuve, Daniel L.

2017-01-01

Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.

Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA.

PubMed

Schroeder, Anthony L; Martinović-Weigelt, Dalma; Ankley, Gerald T; Lee, Kathy E; Garcia-Reyero, Natalia; Perkins, Edward J; Schoenfuss, Heiko L; Villeneuve, Daniel L

2017-02-01

Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation. Published by Elsevier Ltd.

Hemojuvelin-hepcidin axis modeled and analyzed using Petri nets.

PubMed

Formanowicz, Dorota; Kozak, Adam; Głowacki, Tomasz; Radom, Marcin; Formanowicz, Piotr

2013-12-01

Systems biology approach to investigate biological phenomena seems to be very promising because it is capable to capture one of the fundamental properties of living organisms, i.e. their inherent complexity. It allows for analysis biological entities as complex systems of interacting objects. The first and necessary step of such an analysis is building a precise model of the studied biological system. This model is expressed in the language of some branch of mathematics, as for example, differential equations. During the last two decades the theory of Petri nets has appeared to be very well suited for building models of biological systems. The structure of these nets reflects the structure of interacting biological molecules and processes. Moreover, on one hand, Petri nets have intuitive graphical representation being very helpful in understanding the structure of the system and on the other hand, there is a lot of mathematical methods and software tools supporting an analysis of the properties of the nets. In this paper a Petri net based model of the hemojuvelin-hepcidin axis involved in the maintenance of the human body iron homeostasis is presented. The analysis based mainly on T-invariants of the model properties has been made and some biological conclusions have been drawn. Copyright © 2013 Elsevier Inc. All rights reserved.

Modeling of the U1 snRNP assembly pathway in alternative splicing in human cells using Petri nets.

PubMed

Kielbassa, J; Bortfeldt, R; Schuster, S; Koch, I

2009-02-01

The investigation of spliceosomal processes is currently a topic of intense research in molecular biology. In the molecular mechanism of alternative splicing, a multi-protein-RNA complex - the spliceosome - plays a crucial role. To understand the biological processes of alternative splicing, it is essential to comprehend the biogenesis of the spliceosome. In this paper, we propose the first abstract model of the regulatory assembly pathway of the human spliceosomal subunit U1. Using Petri nets, we describe its highly ordered assembly that takes place in a stepwise manner. Petri net theory represents a mathematical formalism to model and analyze systems with concurrent processes at different abstraction levels with the possibility to combine them into a uniform description language. There exist many approaches to determine static and dynamic properties of Petri nets, which can be applied to analyze biochemical systems. In addition, Petri net tools usually provide intuitively understandable graphical network representations, which facilitate the dialog between experimentalists and theoreticians. Our Petri net model covers binding, transport, signaling, and covalent modification processes. Through the computation of structural and behavioral Petri net properties and their interpretation in biological terms, we validate our model and use it to get a better understanding of the complex processes of the assembly pathway. We can explain the basic network behavior, using minimal T-invariants which represent special pathways through the network. We find linear as well as cyclic pathways. We determine the P-invariants that represent conserved moieties in a network. The simulation of the net demonstrates the importance of the stability of complexes during the maturation pathway. We can show that complexes that dissociate too fast, hinder the formation of the complete U1 snRNP.

Charge Inversion by Electrostatic Complexation: Molecular Dynamics Simulations

NASA Astrophysics Data System (ADS)

Faraudo, Jordi; Travesset, Alex

2007-03-01

Ions near interfaces play an important role in many biological and physico-chemical processes and exhibit a fascinating diverse range of phenomena. A relevant example is charge inversion, where interfacial charges attract counterions in excess of their own nominal charge, thus leading to an inversion of the sign of the interfacial charge. In this work, we argue that in the case of amphiphilic interfaces, charge inversion can be generated by complexation, that is, electrostatic complexes containing several counterions bound to amphiphilic molecules. The formation of these complexes require the presence at the interface of groups with conformational degrees of freedom with many electronegative atoms. We illustrate this mechanism by analyzing all atomic molecular dynamics simulations of a DMPA (Dimirystoil-Phosphatidic acid) phospholipid monolayer in contact with divalent counterions. The results are found to be in agreement with recent experimental results on Langmuir monolayers. We also discuss the implications for biological systems, as Phosphatidic acid is emerging as a key signaling phospholipid.

Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers.

PubMed

Chadeau-Hyam, Marc; Campanella, Gianluca; Jombart, Thibaut; Bottolo, Leonardo; Portengen, Lutzen; Vineis, Paolo; Liquet, Benoit; Vermeulen, Roel C H

2013-08-01

Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets. Copyright © 2013 Wiley Periodicals, Inc.

A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

PubMed Central

Koorehdavoudi, Hana; Bogdan, Paul

2016-01-01

Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity. PMID:27297496

A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

NASA Astrophysics Data System (ADS)

Koorehdavoudi, Hana; Bogdan, Paul

2016-06-01

Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity.

Logic-Based Models for the Analysis of Cell Signaling Networks†

PubMed Central

2010-01-01

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks. PMID:20225868

Mass Spectrometry: A Technique of Many Faces

PubMed Central

Olshina, Maya A.; Sharon, Michal

2016-01-01

Protein complexes form the critical foundation for a wide range of biological process, however understanding the intricate details of their activities is often challenging. In this review we describe how mass spectrometry plays a key role in the analysis of protein assemblies and the cellular pathways which they are involved in. Specifically, we discuss how the versatility of mass spectrometric approaches provides unprecedented information on multiple levels. We demonstrate this on the ubiquitin-proteasome proteolytic pathway, a process that is responsible for protein turnover. We follow the various steps of this degradation route and illustrate the different mass spectrometry workflows that were applied for elucidating molecular information. Overall, this review aims to stimulate the integrated use of multiple mass spectrometry approaches for analyzing complex biological systems. PMID:28100928

Tilting at Quixotic Trait Loci (QTL): An Evolutionary Perspective on Genetic Causation

PubMed Central

Weiss, Kenneth M.

2008-01-01

Recent years have seen great advances in generating and analyzing data to identify the genetic architecture of biological traits. Human disease has understandably received intense research focus, and the genes responsible for most Mendelian diseases have successfully been identified. However, the same advances have shown a consistent if less satisfying pattern, in which complex traits are affected by variation in large numbers of genes, most of which have individually minor or statistically elusive effects, leaving the bulk of genetic etiology unaccounted for. This pattern applies to diverse and unrelated traits, not just disease, in basically all species, and is consistent with evolutionary expectations, raising challenging questions about the best way to approach and understand biological complexity. PMID:18711218

cellPACK: A Virtual Mesoscope to Model and Visualize Structural Systems Biology

PubMed Central

Johnson, Graham T.; Autin, Ludovic; Al-Alusi, Mostafa; Goodsell, David S.; Sanner, Michel F.; Olson, Arthur J.

2014-01-01

cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10−7–10−8m) between molecular and cellular biology. cellPACK’s modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive 3D models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is currently available as open source code, with tools for validation of models and with recipes and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators, and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org. PMID:25437435

Systems Biology-Driven Hypotheses Tested In Vivo: The Need to Advancing Molecular Imaging Tools.

PubMed

Verma, Garima; Palombo, Alessandro; Grigioni, Mauro; La Monaca, Morena; D'Avenio, Giuseppe

2018-01-01

Processing and interpretation of biological images may provide invaluable insights on complex, living systems because images capture the overall dynamics as a "whole." Therefore, "extraction" of key, quantitative morphological parameters could be, at least in principle, helpful in building a reliable systems biology approach in understanding living objects. Molecular imaging tools for system biology models have attained widespread usage in modern experimental laboratories. Here, we provide an overview on advances in the computational technology and different instrumentations focused on molecular image processing and analysis. Quantitative data analysis through various open source software and algorithmic protocols will provide a novel approach for modeling the experimental research program. Besides this, we also highlight the predictable future trends regarding methods for automatically analyzing biological data. Such tools will be very useful to understand the detailed biological and mathematical expressions under in-silico system biology processes with modeling properties.

Separating intrinsic from extrinsic fluctuations in dynamic biological systems

PubMed Central

Paulsson, Johan

2011-01-01

From molecules in cells to organisms in ecosystems, biological populations fluctuate due to the intrinsic randomness of individual events and the extrinsic influence of changing environments. The combined effect is often too complex for effective analysis, and many studies therefore make simplifying assumptions, for example ignoring either intrinsic or extrinsic effects to reduce the number of model assumptions. Here we mathematically demonstrate how two identical and independent reporters embedded in a shared fluctuating environment can be used to identify intrinsic and extrinsic noise terms, but also how these contributions are qualitatively and quantitatively different from what has been previously reported. Furthermore, we show for which classes of biological systems the noise contributions identified by dual-reporter methods correspond to the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mechanisms. We find that for broad classes of systems, the extrinsic noise from the dual-reporter method can be rigorously analyzed using models that ignore intrinsic stochasticity. In contrast, the intrinsic noise can be rigorously analyzed using models that ignore extrinsic stochasticity only under very special conditions that rarely hold in biology. Testing whether the conditions are met is rarely possible and the dual-reporter method may thus produce flawed conclusions about the properties of the system, particularly about the intrinsic noise. Our results contribute toward establishing a rigorous framework to analyze dynamically fluctuating biological systems. PMID:21730172

Separating intrinsic from extrinsic fluctuations in dynamic biological systems.

PubMed

Hilfinger, Andreas; Paulsson, Johan

2011-07-19

From molecules in cells to organisms in ecosystems, biological populations fluctuate due to the intrinsic randomness of individual events and the extrinsic influence of changing environments. The combined effect is often too complex for effective analysis, and many studies therefore make simplifying assumptions, for example ignoring either intrinsic or extrinsic effects to reduce the number of model assumptions. Here we mathematically demonstrate how two identical and independent reporters embedded in a shared fluctuating environment can be used to identify intrinsic and extrinsic noise terms, but also how these contributions are qualitatively and quantitatively different from what has been previously reported. Furthermore, we show for which classes of biological systems the noise contributions identified by dual-reporter methods correspond to the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mechanisms. We find that for broad classes of systems, the extrinsic noise from the dual-reporter method can be rigorously analyzed using models that ignore intrinsic stochasticity. In contrast, the intrinsic noise can be rigorously analyzed using models that ignore extrinsic stochasticity only under very special conditions that rarely hold in biology. Testing whether the conditions are met is rarely possible and the dual-reporter method may thus produce flawed conclusions about the properties of the system, particularly about the intrinsic noise. Our results contribute toward establishing a rigorous framework to analyze dynamically fluctuating biological systems.

Evaluation of the nephrotoxicity of complex mixtures containing organics and metals: advantages and disadvantages of the use of real-world complex mixtures.

PubMed

Simmons, J E; Yang, R S; Berman, E

1995-02-01

As part of a multidisciplinary health effects study, the nephrotoxicity of complex industrial waste mixtures was assessed. Adult, male Fischer 344 rats were gavaged with samples of complex industrial waste and nephrotoxicity evaluated 24 hr later. Of the 10 tested samples, 4 produced increased absolute or relative kidney weight, or both, coupled with a statistically significant alteration in at least one of the measured serum parameters (urea nitrogen (BUN), creatinine (CREAT), and BUN/CREAT ratio). Although the waste samples had been analyzed for a number of organic chemicals and 7 of the 10 samples were analyzed also for 12 elemental metals and metalloids, their nephrotoxicity was not readily predicted from the partial chemical characterization data. Because the chemical form or speciation of the metals was unknown, it was not possible to estimate their contribution to the observed biological response. Various experimental approaches, including use of real-world complex mixtures, chemically defined synthetic mixtures, and simple mixtures, will be necessary to adequately determine the potential human health risk from exposure to complex chemical mixtures.

An integrative strategy for quantitative analysis of the N-glycoproteome in complex biological samples.

PubMed

Wang, Ji; Zhou, Chuang; Zhang, Wei; Yao, Jun; Lu, Haojie; Dong, Qiongzhu; Zhou, Haijun; Qin, Lunxiu

2014-01-15

The complexity of protein glycosylation makes it difficult to characterize glycosylation patterns on a proteomic scale. In this study, we developed an integrated strategy for comparatively analyzing N-glycosylation/glycoproteins quantitatively from complex biological samples in a high-throughput manner. This strategy entailed separating and enriching glycopeptides/glycoproteins using lectin affinity chromatography, and then tandem labeling them with 18O/16O to generate a mass shift of 6 Da between the paired glycopeptides, and finally analyzing them with liquid chromatography-mass spectrometry (LC-MS) and the automatic quantitative method we developed based on Mascot Distiller. The accuracy and repeatability of this strategy were first verified using standard glycoproteins; linearity was maintained within a range of 1:10-10:1. The peptide concentration ratios obtained by the self-build quantitative method were similar to both the manually calculated and theoretical values, with a standard deviation (SD) of 0.023-0.186 for glycopeptides. The feasibility of the strategy was further confirmed with serum from hepatocellular carcinoma (HCC) patients and healthy individuals; the expression of 44 glycopeptides and 30 glycoproteins were significantly different between HCC patient and control serum. This strategy is accurate, repeatable, and efficient, and may be a useful tool for identification of disease-related N-glycosylation/glycoprotein changes.

Problem-Solving Test: Targeted Gene Disruption

ERIC Educational Resources Information Center

Szeberenyi, Jozsef

2008-01-01

Mutational inactivation of a specific gene is the most powerful technique to analyze the biological function of the gene. This approach has been used for a long time in viruses, bacteria, yeast, and fruit fly, but looked quite hopeless in more complex organisms. Targeted inactivation of specific genes (also known as knock-out mutation) in mice is…

Optimization Techniques for Analysis of Biological and Social Networks

DTIC Science & Technology

2012-03-28

analyzing a new metaheuristic technique, variable objective search. 3. Experimentation and application: Implement the proposed algorithms , test and fine...alternative mathematical programming formulations, their theoretical analysis, the development of exact algorithms , and heuristics. Originally, clusters...systematic fashion under a unifying theoretical and algorithmic framework. Optimization, Complex Networks, Social Network Analysis, Computational

BiNA: A Visual Analytics Tool for Biological Network Data

PubMed Central

Gerasch, Andreas; Faber, Daniel; Küntzer, Jan; Niermann, Peter; Kohlbacher, Oliver; Lenhof, Hans-Peter; Kaufmann, Michael

2014-01-01

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/. PMID:24551056

General method to find the attractors of discrete dynamic models of biological systems.

PubMed

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

General method to find the attractors of discrete dynamic models of biological systems

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

Shape Mode Analysis Exposes Movement Patterns in Biology: Flagella and Flatworms as Case Studies

PubMed Central

Werner, Steffen; Rink, Jochen C.; Riedel-Kruse, Ingmar H.; Friedrich, Benjamin M.

2014-01-01

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way. PMID:25426857

Tomographic reconstruction of melanin structures of optical coherence tomography via the finite-difference time-domain simulation

NASA Astrophysics Data System (ADS)

Huang, Shi-Hao; Wang, Shiang-Jiu; Tseng, Snow H.

2015-03-01

Optical coherence tomography (OCT) provides high resolution, cross-sectional image of internal microstructure of biological tissue. We use the Finite-Difference Time-Domain method (FDTD) to analyze the data acquired by OCT, which can help us reconstruct the refractive index of the biological tissue. We calculate the refractive index tomography and try to match the simulation with the data acquired by OCT. Specifically, we try to reconstruct the structure of melanin, which has complex refractive indices and is the key component of human pigment system. The results indicate that better reconstruction can be achieved for homogenous sample, whereas the reconstruction is degraded for samples with fine structure or with complex interface. Simulation reconstruction shows structures of the Melanin that may be useful for biomedical optics applications.

Addressing the unmet need for visualizing conditional random fields in biological data

PubMed Central

2014-01-01

Background The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors. Unfortunately, while GPMs ideally fit many questions in biology, they are not an easy solution to apply. Building a GPM is not a simple task for an end user. Moreover, applying GPMs is also impeded by the insidious fact that the “complex web of interacting factors” inherent to a problem might be easy to define and also intractable to compute upon. Discussion We propose that the visualization sciences can contribute to many domains of the bio-sciences, by developing tools to address archetypal representation and user interaction issues in GPMs, and in particular a variety of GPM called a Conditional Random Field(CRF). CRFs bring additional power, and additional complexity, because the CRF dependency network can be conditioned on the query data. Conclusions In this manuscript we examine the shared features of several biological problems that are amenable to modeling with CRFs, highlight the challenges that existing visualization and visual analytics paradigms induce for these data, and document an experimental solution called StickWRLD which, while leaving room for improvement, has been successfully applied in several biological research projects. Software and tutorials are available at http://www.stickwrld.org/ PMID:25000815

Application of machine learning methods in bioinformatics

NASA Astrophysics Data System (ADS)

Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

2018-05-01

Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

Using the Tools and Resources of the RCSB Protein Data Bank.

PubMed

Costanzo, Luigi Di; Ghosh, Sutapa; Zardecki, Christine; Burley, Stephen K

2016-09-07

The Protein Data Bank (PDB) archive is the worldwide repository of experimentally determined three-dimensional structures of large biological molecules found in all three kingdoms of life. Atomic-level structures of these proteins, nucleic acids, and complex assemblies thereof are central to research and education in molecular, cellular, and organismal biology, biochemistry, biophysics, materials science, bioengineering, ecology, and medicine. Several types of information are associated with each PDB archival entry, including atomic coordinates, primary experimental data, polymer sequence(s), and summary metadata. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) serves as the U.S. data center for the PDB, distributing archival data and supporting both simple and complex queries that return results. These data can be freely downloaded, analyzed, and visualized using RCSB PDB tools and resources to gain a deeper understanding of fundamental biological processes, molecular evolution, human health and disease, and drug discovery. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Systems Biology Approaches for Discovering Biomarkers for Traumatic Brain Injury

PubMed Central

Feala, Jacob D.; AbdulHameed, Mohamed Diwan M.; Yu, Chenggang; Dutta, Bhaskar; Yu, Xueping; Schmid, Kara; Dave, Jitendra; Tortella, Frank

2013-01-01

Abstract The rate of traumatic brain injury (TBI) in service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse data sets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI data sets to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates. PMID:23510232

Synthesis, Characterization, Crystal Structure, and Biological Studies of a Cadmium(II) Complex with a Tridentate Ligand 4′-Chloro-2,2′:6′,2′′-Terpyridine

PubMed Central

Saghatforoush, L. A.; Valencia, L.; Chalabian, F.; Ghammamy, Sh.

2011-01-01

A new Cd(II) complex with the ligand 4′-chloro-2,2′6′,2′′-terpyridine (Cltpy), [Cd(Cltpy)(I)2], has been synthesized and characterized by CHN elemental analysis, 1H-NMR, 13C-NMR, and IR spectroscopy and structurally analyzed by X-ray single-crystal diffraction. The single-crystal X-ray analyses show that the coordination number in complex is five with three terpyridine (Cltpy) N-donor atoms and two iodine atoms. The antibacterial activities of Cltpy and its Cd(II) complex are tested against different bacteria. PMID:21738495

Network Approach to Disease Diagnosis

NASA Astrophysics Data System (ADS)

Sharma, Amitabh; Bashan, Amir; Barabasi, Alber-Laszlo

2014-03-01

Human diseases could be viewed as perturbations of the underlying biological system. A thorough understanding of the topological and dynamical properties of the biological system is crucial to explain the mechanisms of many complex diseases. Recently network-based approaches have provided a framework for integrating multi-dimensional biological data that results in a better understanding of the pathophysiological state of complex diseases. Here we provide a network-based framework to improve the diagnosis of complex diseases. This framework is based on the integration of transcriptomics and the interactome. We analyze the overlap between the differentially expressed (DE) genes and disease genes (DGs) based on their locations in the molecular interaction network (''interactome''). Disease genes and their protein products tend to be much more highly connected than random, hence defining a disease sub-graph (called disease module) in the interactome. DE genes, even though different from the known set of DGs, may be significantly associated with the disease when considering their closeness to the disease module in the interactome. This new network approach holds the promise to improve the diagnosis of patients who cannot be diagnosed using conventional tools. Support was provided by HL066289 and HL105339 grants from the U.S. National Institutes of Health.

Animal models and conserved processes

PubMed Central

2012-01-01

Background The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation? Methods We surveyed the literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the value of nonhuman animal models when studying conserved processes. Results Evolution through natural selection has employed components and processes both to produce the same outcomes among species but also to generate different functions and traits. Many genes and processes are conserved, but new combinations of these processes or different regulation of the genes involved in these processes have resulted in unique organisms. Further, there is a hierarchy of organization in complex living systems. At some levels, the components are simple systems that can be analyzed by mathematics or the physical sciences, while at other levels the system cannot be fully analyzed by reducing it to a physical system. The study of complex living systems must alternate between focusing on the parts and examining the intact whole organism while taking into account the connections between the two. Systems biology aims for this holism. We examined the actions of inhalational anesthetic agents and anti-neoplastic agents in order to address what the characteristics of complex living systems imply for inter-species extrapolation of traits and responses related to conserved processes. Conclusion We conclude that even the presence of conserved processes is insufficient for inter-species extrapolation when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible. PMID:22963674

Meaning Making through Multiple Modalities in a Biology Classroom: A Multimodal Semiotics Discourse Analysis

ERIC Educational Resources Information Center

Jaipal, Kamini

2010-01-01

The teaching of science is a complex process, involving the use of multiple modalities. This paper illustrates the potential of a multimodal semiotics discourse analysis framework to illuminate meaning-making possibilities during the teaching of a science concept. A multimodal semiotics analytical framework is developed and used to (1) analyze the…

Development and use of the Cytoscape app GFD-Net for measuring semantic dissimilarity of gene networks

PubMed Central

Diaz-Montana, Juan J.; Diaz-Diaz, Norberto

2014-01-01

Gene networks are one of the main computational models used to study the interaction between different elements during biological processes being widely used to represent gene–gene, or protein–protein interaction complexes. We present GFD-Net, a Cytoscape app for visualizing and analyzing the functional dissimilarity of gene networks. PMID:25400907

Modeling the Effects of Light and Sucrose on In Vitro Propagated Plants: A Multiscale System Analysis Using Artificial Intelligence Technology

PubMed Central

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P.

2014-01-01

Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829

Capillary zone electrophoresis for analysis of phytochelatins and other thiol peptides in complex biological samples derivatized with monobromobimane.

PubMed

Perez-Rama, Mónica; Torres Vaamonde, Enrique; Abalde Alonso, Julio

2005-02-01

A new method to improve the analysis of phytochelatins and their precursors (cysteine, gamma-Glu-Cys, and glutathione) derivatized with monobromobimane (mBrB) in complex biological samples by capillary zone electrophoresis is described. The effects of the background electrolyte pH, concentration, and different organic additives (acetonitrile, methanol, and trifluoroethanol) on the separation were studied to achieve optimum resolution and number of theoretical plates of the analyzed compounds in the electropherograms. Optimum separation of the thiol peptides was obtained with 150 mM phosphate buffer at pH 1.60. Separation efficiency was improved when 2.5% v/v methanol was added to the background electrolyte. The electrophoretic conditions were 13 kV and capillary dimensions with 30 cm length from the inlet to the detector (38 cm total length) and 50 microm inner diameter. The injection was by pressure at 50 mbar for 17 s. Under these conditions, the separation between desglycyl-peptides and phytochelatins was also achieved. We also describe the optimum conditions for the derivatization of biological samples with mBrB to increase electrophoretic sensitivity and number of theoretical plates. The improved method was shown to be simple, reproducible, selective, and accurate in measuring thiol peptides in complex biological samples, the detection limit being 2.5 microM glutathione at a wavelength of 390 nm.

Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.

PubMed

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P

2014-01-01

Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1). Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

Computational structure analysis of biomacromolecule complexes by interface geometry.

PubMed

Mahdavi, Sedigheh; Salehzadeh-Yazdi, Ali; Mohades, Ali; Masoudi-Nejad, Ali

2013-12-01

The ability to analyze and compare protein-nucleic acid and protein-protein interaction interface has critical importance in understanding the biological function and essential processes occurring in the cells. Since high-resolution three-dimensional (3D) structures of biomacromolecule complexes are available, computational characterizing of the interface geometry become an important research topic in the field of molecular biology. In this study, the interfaces of a set of 180 protein-nucleic acid and protein-protein complexes are computed to understand the principles of their interactions. The weighted Voronoi diagram of the atoms and the Alpha complex has provided an accurate description of the interface atoms. Our method is implemented in the presence and absence of water molecules. A comparison among the three types of interaction interfaces show that RNA-protein complexes have the largest size of an interface. The results show a high correlation coefficient between our method and the PISA server in the presence and absence of water molecules in the Voronoi model and the traditional model based on solvent accessibility and the high validation parameters in comparison to the classical model. Copyright © 2013 Elsevier Ltd. All rights reserved.

Synthesis, characterization and biological assay of Salicylaldehyde Schiff base Cu(II) complexes and their precursors

NASA Astrophysics Data System (ADS)

Iftikhar, Bushra; Javed, Kanwal; Khan, Muhammad Saif Ullah; Akhter, Zareen; Mirza, Bushra; Mckee, Vickie

2018-03-01

Three new Schiff base ligands were synthesized by the reaction of Salicylaldehyde with semi-aromatic diamines, prepared by the reduction of corresponding dinitro-compounds, and were further used for the formation of complexes with Cu(II) metal ion. The structural features of the synthesized compounds were confirmed by their physical properties and infrared, electronic and NMR spectroscopic techniques. The studies revealed that the synthesized Schiff bases existed as tetradentate ligands and bonded to the metal ion through the phenolic oxygen and azomethine nitrogen. One of the dinitro precursors was also analyzed by single crystal X-ray crystallography, which showed that it crystallizes in monoclinic system with space group P2/n. The thermal behavior of the Cu(II) complexes was determined by thermogravimetric analysis (TGA) and kinetic parameters were evaluated from the data. Schiff base ligands, their precursors and metal complexes were also screened for antibacterial, antifungal, antitumor, Brine shrimp lethality, DPPH free radical scavenging and DNA damage assays. The results of these analyses indicated the substantial potential of the synthesized Schiff bases, their precursors and Cu(II) complexes in biological field as future drugs.

Complex and unexpected dynamics in simple genetic regulatory networks

NASA Astrophysics Data System (ADS)

Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

2014-03-01

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

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

Apel, William A; Thompson, Vicki S

A method for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an embodiment of the invention, the analyte is a drug, such as marijuana, Cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method comprises attaching antigens to a surface of a solid support in a preselected pattern to form an array wherein locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to the antigens in the array to form immunemore » complexes; washing away antibodies that do form immune complexes; and detecting the immune complexes, to form an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to the subject's identity.« less

Antibody profiling sensitivity through increased reporter antibody layering

DOEpatents

Apel, William A.; Thompson, Vicki S.

2013-02-26

A method for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an embodiment of the invention, the analyte is a drug, such as marijuana, Cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method comprises attaching antigens to a surface of a solid support in a preselected pattern to form an array wherein locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to the antigens in the array to form immune complexes; washing away antibodies that do form immune complexes; and detecting the immune complexes, to form an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to the subject's identity.

Antibody profiling sensitivity through increased reporter antibody layering

DOEpatents

Apel, William A.; Thompson, Vicki S.

2017-03-28

A method for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an embodiment of the invention, the analyte is a drug, such as marijuana, Cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method comprises attaching antigens to a surface of a solid support in a preselected pattern to form an array wherein locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to the antigens in the array to form immune complexes; washing away antibodies that do form immune complexes; and detecting the immune complexes, to form an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to the subject's identity.

Computational dynamic approaches for temporal omics data with applications to systems medicine.

PubMed

Liang, Yulan; Kelemen, Arpad

2017-01-01

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

NASA Astrophysics Data System (ADS)

Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

2015-03-01

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

DNAproDB: an interactive tool for structural analysis of DNA–protein complexes

PubMed Central

Sagendorf, Jared M.

2017-01-01

Abstract Many biological processes are mediated by complex interactions between DNA and proteins. Transcription factors, various polymerases, nucleases and histones recognize and bind DNA with different levels of binding specificity. To understand the physical mechanisms that allow proteins to recognize DNA and achieve their biological functions, it is important to analyze structures of DNA–protein complexes in detail. DNAproDB is a web-based interactive tool designed to help researchers study these complexes. DNAproDB provides an automated structure-processing pipeline that extracts structural features from DNA–protein complexes. The extracted features are organized in structured data files, which are easily parsed with any programming language or viewed in a browser. We processed a large number of DNA–protein complexes retrieved from the Protein Data Bank and created the DNAproDB database to store this data. Users can search the database by combining features of the DNA, protein or DNA–protein interactions at the interface. Additionally, users can upload their own structures for processing privately and securely. DNAproDB provides several interactive and customizable tools for creating visualizations of the DNA–protein interface at different levels of abstraction that can be exported as high quality figures. All functionality is documented and freely accessible at http://dnaprodb.usc.edu. PMID:28431131

Computer-aided discovery of biological activity spectra for anti-aging and anti-cancer olive oil oleuropeins.

PubMed

Corominas-Faja, Bruna; Santangelo, Elvira; Cuyàs, Elisabet; Micol, Vicente; Joven, Jorge; Ariza, Xavier; Segura-Carretero, Antonio; García, Jordi; Menendez, Javier A

2014-09-01

Aging is associated with common conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer's disease. The type of multi-targeted pharmacological approach necessary to address a complex multifaceted disease such as aging might take advantage of pleiotropic natural polyphenols affecting a wide variety of biological processes. We have recently postulated that the secoiridoids oleuropein aglycone (OA) and decarboxymethyl oleuropein aglycone (DOA), two complex polyphenols present in health-promoting extra virgin olive oil (EVOO), might constitute a new family of plant-produced gerosuppressant agents. This paper describes an analysis of the biological activity spectra (BAS) of OA and DOA using PASS (Prediction of Activity Spectra for Substances) software. PASS can predict thousands of biological activities, as the BAS of a compound is an intrinsic property that is largely dependent on the compound's structure and reflects pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicities. Using Pharmaexpert, a tool that analyzes the PASS-predicted BAS of substances based on thousands of "mechanism-effect" and "effect-mechanism" relationships, we illuminate hypothesis-generating pharmacological effects, mechanisms of action, and targets that might underlie the anti-aging/anti-cancer activities of the gerosuppressant EVOO oleuropeins.

Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology⋆

PubMed Central

Fu, Wenjiang J.; Stromberg, Arnold J.; Viele, Kert; Carroll, Raymond J.; Wu, Guoyao

2009-01-01

Over the past two decades, there have been revolutionary developments in life science technologies characterized by high throughput, high efficiency, and rapid computation. Nutritionists now have the advanced methodologies for the analysis of DNA, RNA, protein, low-molecular-weight metabolites, as well as access to bioinformatics databases. Statistics, which can be defined as the process of making scientific inferences from data that contain variability, has historically played an integral role in advancing nutritional sciences. Currently, in the era of systems biology, statistics has become an increasingly important tool to quantitatively analyze information about biological macromolecules. This article describes general terms used in statistical analysis of large, complex experimental data. These terms include experimental design, power analysis, sample size calculation, and experimental errors (type I and II errors) for nutritional studies at population, tissue, cellular, and molecular levels. In addition, we highlighted various sources of experimental variations in studies involving microarray gene expression, real-time polymerase chain reaction, proteomics, and other bioinformatics technologies. Moreover, we provided guidelines for nutritionists and other biomedical scientists to plan and conduct studies and to analyze the complex data. Appropriate statistical analyses are expected to make an important contribution to solving major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine fetal retardation). PMID:20233650

An integrative strategy for quantitative analysis of the N-glycoproteome in complex biological samples

PubMed Central

2014-01-01

Background The complexity of protein glycosylation makes it difficult to characterize glycosylation patterns on a proteomic scale. In this study, we developed an integrated strategy for comparatively analyzing N-glycosylation/glycoproteins quantitatively from complex biological samples in a high-throughput manner. This strategy entailed separating and enriching glycopeptides/glycoproteins using lectin affinity chromatography, and then tandem labeling them with 18O/16O to generate a mass shift of 6 Da between the paired glycopeptides, and finally analyzing them with liquid chromatography-mass spectrometry (LC-MS) and the automatic quantitative method we developed based on Mascot Distiller. Results The accuracy and repeatability of this strategy were first verified using standard glycoproteins; linearity was maintained within a range of 1:10–10:1. The peptide concentration ratios obtained by the self-build quantitative method were similar to both the manually calculated and theoretical values, with a standard deviation (SD) of 0.023–0.186 for glycopeptides. The feasibility of the strategy was further confirmed with serum from hepatocellular carcinoma (HCC) patients and healthy individuals; the expression of 44 glycopeptides and 30 glycoproteins were significantly different between HCC patient and control serum. Conclusions This strategy is accurate, repeatable, and efficient, and may be a useful tool for identification of disease-related N-glycosylation/glycoprotein changes. PMID:24428921

On a biologically inspired topology optimization method

NASA Astrophysics Data System (ADS)

Kobayashi, Marcelo H.

2010-03-01

This work concerns the development of a biologically inspired methodology for the study of topology optimization in engineering and natural systems. The methodology is based on L systems and its turtle interpretation for the genotype-phenotype modeling of the topology development. The topology is analyzed using the finite element method, and optimized using an evolutionary algorithm with the genetic encoding of the L system and its turtle interpretation, as well as, body shape and physical characteristics. The test cases considered in this work clearly show the suitability of the proposed method for the study of engineering and natural complex systems.

Valsartan.

PubMed

Ardiana, Febry; Suciati; Indrayanto, Gunawan

2015-01-01

Valsartan is an antihypertensive drug which selectively inhibits angiotensin receptor type II. Generally, valsartan is available as film-coated tablets. This review summarizes thermal analysis, spectroscopy characteristics (UV, IR, MS, and NMR), polymorphism forms, impurities, and related compounds of valsartan. The methods of analysis of valsartan in pharmaceutical dosage forms and in biological fluids using spectrophotometer, CE, TLC, and HPLC methods are discussed in details. Both official and nonofficial methods are described. It is recommended to use LC-MS method for analyzing valsartan in complex matrices such as biological fluids and herbal preparations; in this case, MRM is preferred than SIM method. © 2015 Elsevier Inc. All rights reserved.

Analysis of English language learner performance on the biology Massachusetts comprehensive assessment system: The impact of english proficiency, first language characteristics, and late-entry ELL status

NASA Astrophysics Data System (ADS)

Mitchell, Mary A.

This study analyzed English language learner (ELL) performance on the June 2012 Biology MCAS, namely on item attributes of domain, cognitive skill, and linguistic complexity. It examined the impact of English proficiency, Latinate first language, first language orthography, and late-entry ELL status. The results indicated that English proficiency was a strong predictor of performance and that ELLs at higher levels of English proficiency overwhelmingly passed. The results further indicated that English proficiency introduced a construct-irrelevant variance on the Biology MCAS and raised validity issues for using this assessment at lower levels of English proficiency. This study also found that ELLs with a Latinate first language consistently had statistically significant lower performance. Late-entry ELL status did not predict Biology MCAS performance.

The Evolving Role of the Courts in Educational Policy: The Tension between Judicial, Scientific, and Democratic Decision Making in "Kitzmiller v. Dover"

ERIC Educational Resources Information Center

Superfine, Benjamin Michael

2009-01-01

In "Kitzmiller v. Dover" (2005), a court defined science to decide the legitimacy of teaching intelligent design to high school biology students. This study analyzes "Kitzmiller" in light of the complex and interrelated tensions between judicial, scientific, and democratic decision making that lie at the heart of modern…

The Cell Collective: Toward an open and collaborative approach to systems biology

PubMed Central

2012-01-01

Background Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety. Results The Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time. Conclusions The Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction. PMID:22871178

Systems Biology-Based Platforms to Accelerate Research of Emerging Infectious Diseases.

PubMed

Oh, Soo Jin; Choi, Young Ki; Shin, Ok Sarah

2018-03-01

Emerging infectious diseases (EIDs) pose a major threat to public health and security. Given the dynamic nature and significant impact of EIDs, the most effective way to prevent and protect against them is to develop vaccines in advance. Systems biology approaches provide an integrative way to understand the complex immune response to pathogens. They can lead to a greater understanding of EID pathogenesis and facilitate the evaluation of newly developed vaccine-induced immunity in a timely manner. In recent years, advances in high throughput technologies have enabled researchers to successfully apply systems biology methods to analyze immune responses to a variety of pathogens and vaccines. Despite recent advances, computational and biological challenges impede wider application of systems biology approaches. This review highlights recent advances in the fields of systems immunology and vaccinology, and presents ways that systems biology-based platforms can be applied to accelerate a deeper understanding of the molecular mechanisms of immunity against EIDs. © Copyright: Yonsei University College of Medicine 2018.

Systems Biology-Based Platforms to Accelerate Research of Emerging Infectious Diseases

PubMed Central

2018-01-01

Emerging infectious diseases (EIDs) pose a major threat to public health and security. Given the dynamic nature and significant impact of EIDs, the most effective way to prevent and protect against them is to develop vaccines in advance. Systems biology approaches provide an integrative way to understand the complex immune response to pathogens. They can lead to a greater understanding of EID pathogenesis and facilitate the evaluation of newly developed vaccine-induced immunity in a timely manner. In recent years, advances in high throughput technologies have enabled researchers to successfully apply systems biology methods to analyze immune responses to a variety of pathogens and vaccines. Despite recent advances, computational and biological challenges impede wider application of systems biology approaches. This review highlights recent advances in the fields of systems immunology and vaccinology, and presents ways that systems biology-based platforms can be applied to accelerate a deeper understanding of the molecular mechanisms of immunity against EIDs. PMID:29436184

The Prediction of Botulinum Toxin Structure Based on in Silico and in Vitro Analysis

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2011-01-01

Many of biological system mediated through protein-protein interactions. Knowledge of protein-protein complex structure is required for understanding the function. The determination of huge size and flexible protein-protein complex structure by experimental studies remains difficult, costly and five-consuming, therefore computational prediction of protein structures by homolog modeling and docking studies is valuable method. In addition, MD simulation is also one of the most powerful methods allowing to see the real dynamics of proteins. Here, we predict protein-protein complex structure of botulinum toxin to analyze its property. These bioinformatics methods are useful to report the relation between the flexibility of backbone structure and the activity.

Live-cell imaging of invasion and intravasation in an artificial microvessel platform.

PubMed

Wong, Andrew D; Searson, Peter C

2014-09-01

Methods to visualize metastasis exist, but additional tools to better define the biologic and physical processes underlying invasion and intravasation are still needed. One difficulty in studying metastasis stems from the complexity of the interface between the tumor microenvironment and the vascular system. Here, we report the development of an investigational platform that positions tumor cells next to an artificial vessel embedded in an extracellular matrix. On this platform, we used live-cell fluorescence microscopy to analyze the complex interplay between metastatic cancer cells and a functional artificial microvessel that was lined with endothelial cells. The platform recapitulated known interactions, and its use demonstrated the capabilities for a systematic study of novel physical and biologic parameters involved in invasion and intravasation. In summary, our work offers an important new tool to advance knowledge about metastasis and candidate antimetastatic therapies. ©2014 American Association for Cancer Research.

A new theoretical approach to analyze complex processes in cytoskeleton proteins.

PubMed

Li, Xin; Kolomeisky, Anatoly B

2014-03-20

Cytoskeleton proteins are filament structures that support a large number of important biological processes. These dynamic biopolymers exist in nonequilibrium conditions stimulated by hydrolysis chemical reactions in their monomers. Current theoretical methods provide a comprehensive picture of biochemical and biophysical processes in cytoskeleton proteins. However, the description is only qualitative under biologically relevant conditions because utilized theoretical mean-field models neglect correlations. We develop a new theoretical method to describe dynamic processes in cytoskeleton proteins that takes into account spatial correlations in the chemical composition of these biopolymers. Our approach is based on analysis of probabilities of different clusters of subunits. It allows us to obtain exact analytical expressions for a variety of dynamic properties of cytoskeleton filaments. By comparing theoretical predictions with Monte Carlo computer simulations, it is shown that our method provides a fully quantitative description of complex dynamic phenomena in cytoskeleton proteins under all conditions.

Structural studies of P-type ATPase–ligand complexes using an X-ray free-electron laser

DOE PAGES

Bublitz, Maike; Nass, Karol; Drachmann, Nikolaj D.; ...

2015-06-11

Membrane proteins are key players in biological systems, mediating signalling events and the specific transport ofe.g.ions and metabolites. Consequently, membrane proteins are targeted by a large number of currently approved drugs. Understanding their functions and molecular mechanisms is greatly dependent on structural information, not least on complexes with functionally or medically important ligands. Structure determination, however, is hampered by the difficulty of obtaining well diffracting, macroscopic crystals. Here, the feasibility of X-ray free-electron-laser-based serial femtosecond crystallography (SFX) for the structure determination of membrane protein–ligand complexes using microcrystals of various native-source and recombinant P-type ATPase complexes is demonstrated. The data revealmore » the binding sites of a variety of ligands, including lipids and inhibitors such as the hallmark P-type ATPase inhibitor orthovanadate. By analyzing the resolution dependence of ligand densities and overall model qualities, SFX data quality metrics as well as suitable refinement procedures are discussed. Even at relatively low resolution and multiplicity, the identification of ligands can be demonstrated. This makes SFX a useful tool for ligand screening and thus for unravelling the molecular mechanisms of biologically active proteins.« less

Introducing Proteomics in the Undergraduate Curriculum: A Simple 2D Gel Electrophoresis Exercise with Serum Proteins

ERIC Educational Resources Information Center

Kim, Thomas D.; Craig, Paul A.

2010-01-01

Two-dimensional gel electrophoresis (2DGE) remains an important tool in the study of biological systems by proteomics. While the use of 2DGE is commonplace in research publications, there are few instructional laboratories that address the use of 2DGE for analyzing complex protein samples. One reason for this lack is the fact that the preparation…

Parallel processing of genomics data

NASA Astrophysics Data System (ADS)

Agapito, Giuseppe; Guzzi, Pietro Hiram; Cannataro, Mario

2016-10-01

The availability of high-throughput experimental platforms for the analysis of biological samples, such as mass spectrometry, microarrays and Next Generation Sequencing, have made possible to analyze a whole genome in a single experiment. Such platforms produce an enormous volume of data per single experiment, thus the analysis of this enormous flow of data poses several challenges in term of data storage, preprocessing, and analysis. To face those issues, efficient, possibly parallel, bioinformatics software needs to be used to preprocess and analyze data, for instance to highlight genetic variation associated with complex diseases. In this paper we present a parallel algorithm for the parallel preprocessing and statistical analysis of genomics data, able to face high dimension of data and resulting in good response time. The proposed system is able to find statistically significant biological markers able to discriminate classes of patients that respond to drugs in different ways. Experiments performed on real and synthetic genomic datasets show good speed-up and scalability.

Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models

PubMed Central

Chen, Yang; Shen, Kuang

2017-01-01

To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recordings of the experimental system. We introduce a Bayesian hierarchical model on top of hidden Markov models (HMMs) to analyze these data and use the statistical results to answer the biological questions. In addition to resolving the biological puzzles and delineating the regulating roles of different molecular complexes, our statistical results enable us to propose a more detailed mechanism for the late stages of the protein targeting process. PMID:28943680

Coupled Analysis of In Vitro and Histology Tissue Samples to Quantify Structure-Function Relationship

PubMed Central

Acar, Evrim; Plopper, George E.; Yener, Bülent

2012-01-01

The structure/function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating disease. However, at the tissue level of biological complexity we encounter a gap in the structure/function relationship: having accumulated an extraordinary amount of detailed information about biological tissues at the cellular and subcellular level, we cannot assemble it in a way that explains the correspondingly complex biological functions these structures perform. To help close this information gap we define here several quantitative temperospatial features that link tissue structure to its corresponding biological function. Both histological images of human tissue samples and fluorescence images of three-dimensional cultures of human cells are used to compare the accuracy of in vitro culture models with their corresponding human tissues. To the best of our knowledge, there is no prior work on a quantitative comparison of histology and in vitro samples. Features are calculated from graph theoretical representations of tissue structures and the data are analyzed in the form of matrices and higher-order tensors using matrix and tensor factorization methods, with a goal of differentiating between cancerous and healthy states of brain, breast, and bone tissues. We also show that our techniques can differentiate between the structural organization of native tissues and their corresponding in vitro engineered cell culture models. PMID:22479315

A reductionist approach to extract robust molecular markers from microarray data series - Isolating markers to track osseointegration.

PubMed

Barik, Anwesha; Banerjee, Satarupa; Dhara, Santanu; Chakravorty, Nishant

2017-04-01

Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in microarray data series and thereby extract robust molecular markers to track biological processes. The methodology has been illustrated by analyzing whole genome expression studies on bone-implant integration (ossointegration). Being a biological process, osseointegration is known to leave a trail of genetic footprint during the course. In spite of existence of enormous amount of raw data in public repositories, researchers still do not have access to a panel of genes that can definitively track osseointegration. The results from our study revealed panels comprising of matrix metalloproteinases and collagen genes were able to track osseointegration on implant surfaces (MMP9 and COL1A2 on micro-textured; MMP12 and COL6A3 on superimposed nano-textured surfaces) with 100% classification accuracy, specificity and sensitivity. Further, our analysis showed the importance of the progression of the duration in establishment of the mechanical connection at bone-implant surface. The findings from this study are expected to be useful to researchers investigating osseointegration of novel implant materials especially at the early stage. The methodology demonstrated can be easily adapted by scientists in different fields to analyze large databases for other biological processes. Copyright © 2017 Elsevier Inc. All rights reserved.

MDcons: Intermolecular contact maps as a tool to analyze the interface of protein complexes from molecular dynamics trajectories

PubMed Central

2014-01-01

Background Molecular Dynamics (MD) simulations of protein complexes suffer from the lack of specific tools in the analysis step. Analyses of MD trajectories of protein complexes indeed generally rely on classical measures, such as the RMSD, RMSF and gyration radius, conceived and developed for single macromolecules. As a matter of fact, instead, researchers engaged in simulating the dynamics of a protein complex are mainly interested in characterizing the conservation/variation of its biological interface. Results On these bases, herein we propose a novel approach to the analysis of MD trajectories or other conformational ensembles of protein complexes, MDcons, which uses the conservation of inter-residue contacts at the interface as a measure of the similarity between different snapshots. A "consensus contact map" is also provided, where the conservation of the different contacts is drawn in a grey scale. Finally, the interface area of the complex is monitored during the simulations. To show its utility, we used this novel approach to study two protein-protein complexes with interfaces of comparable size and both dominated by hydrophilic interactions, but having binding affinities at the extremes of the experimental range. MDcons is demonstrated to be extremely useful to analyse the MD trajectories of the investigated complexes, adding important insight into the dynamic behavior of their biological interface. Conclusions MDcons specifically allows the user to highlight and characterize the dynamics of the interface in protein complexes and can thus be used as a complementary tool for the analysis of MD simulations of both experimental and predicted structures of protein complexes. PMID:25077693

MDcons: Intermolecular contact maps as a tool to analyze the interface of protein complexes from molecular dynamics trajectories.

PubMed

Abdel-Azeim, Safwat; Chermak, Edrisse; Vangone, Anna; Oliva, Romina; Cavallo, Luigi

2014-01-01

Molecular Dynamics (MD) simulations of protein complexes suffer from the lack of specific tools in the analysis step. Analyses of MD trajectories of protein complexes indeed generally rely on classical measures, such as the RMSD, RMSF and gyration radius, conceived and developed for single macromolecules. As a matter of fact, instead, researchers engaged in simulating the dynamics of a protein complex are mainly interested in characterizing the conservation/variation of its biological interface. On these bases, herein we propose a novel approach to the analysis of MD trajectories or other conformational ensembles of protein complexes, MDcons, which uses the conservation of inter-residue contacts at the interface as a measure of the similarity between different snapshots. A "consensus contact map" is also provided, where the conservation of the different contacts is drawn in a grey scale. Finally, the interface area of the complex is monitored during the simulations. To show its utility, we used this novel approach to study two protein-protein complexes with interfaces of comparable size and both dominated by hydrophilic interactions, but having binding affinities at the extremes of the experimental range. MDcons is demonstrated to be extremely useful to analyse the MD trajectories of the investigated complexes, adding important insight into the dynamic behavior of their biological interface. MDcons specifically allows the user to highlight and characterize the dynamics of the interface in protein complexes and can thus be used as a complementary tool for the analysis of MD simulations of both experimental and predicted structures of protein complexes.

An updated version of NPIDB includes new classifications of DNA–protein complexes and their families

PubMed Central

Zanegina, Olga; Kirsanov, Dmitriy; Baulin, Eugene; Karyagina, Anna; Alexeevski, Andrei; Spirin, Sergey

2016-01-01

The recent upgrade of nucleic acid–protein interaction database (NPIDB, http://npidb.belozersky.msu.ru/) includes a newly elaborated classification of complexes of protein domains with double-stranded DNA and a classification of families of related complexes. Our classifications are based on contacting structural elements of both DNA: the major groove, the minor groove and the backbone; and protein: helices, beta-strands and unstructured segments. We took into account both hydrogen bonds and hydrophobic interaction. The analyzed material contains 1942 structures of protein domains from 748 PDB entries. We have identified 97 interaction modes of individual protein domain–DNA complexes and 17 DNA–protein interaction classes of protein domain families. We analyzed the sources of diversity of DNA–protein interaction modes in different complexes of one protein domain family. The observed interaction mode is sometimes influenced by artifacts of crystallization or diversity in secondary structure assignment. The interaction classes of domain families are more stable and thus possess more biological sense than a classification of single complexes. Integration of the classification into NPIDB allows the user to browse the database according to the interacting structural elements of DNA and protein molecules. For each family, we present average DNA shape parameters in contact zones with domains of the family. PMID:26656949

Antibody profiling sensitivity through increased reporter antibody layering

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

Apel, William A.; Thompson, Vicki S

A method for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an embodiment of the invention, the analyte is a drug, such as marijuana, Cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method comprises attaching antigens to a surface of a solid support in a preselected pattern to form an array wherein locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to the antigens in the array to form immunemore » complexes; washing away antibodies that do form immune complexes; and detecting the immune complexes, to form an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to the subject's identity.« less

Using graph theory to analyze biological networks

PubMed Central

2011-01-01

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system. PMID:21527005

Quantifying Carbon-14 for Biology Using Cavity Ring-Down Spectroscopy.

PubMed

McCartt, A Daniel; Ognibene, Ted J; Bench, Graham; Turteltaub, Kenneth W

2016-09-06

A cavity ring-down spectroscopy (CRDS) instrument was developed using mature, robust hardware for the measurement of carbon-14 in biological studies. The system was characterized using carbon-14 elevated glucose samples and returned a linear response up to 387 times contemporary carbon-14 concentrations. Carbon-14 free and contemporary carbon-14 samples with varying carbon-13 concentrations were used to assess the method detection limit of approximately one-third contemporary carbon-14 levels. Sources of inaccuracies are presented and discussed, and the capability to measure carbon-14 in biological samples is demonstrated by comparing pharmacokinetics from carbon-14 dosed guinea pigs analyzed by both CRDS and accelerator mass spectrometry. The CRDS approach presented affords easy access to powerful carbon-14 tracer techniques that can characterize complex biochemical systems.

Estimation of Metabolism Characteristics for Heat-Injured Bacteria Using Dielectrophoretic Impedance Measurement Method

NASA Astrophysics Data System (ADS)

Amako, Eri; Enjoji, Takaharu; Uchida, Satoshi; Tochikubo, Fumiyoshi

Constant monitoring and immediate control of fermentation processes have been required for advanced quality preservation in food industry. In the present work, simple estimation of metabolic states for heat-injured Escherichia coli (E. coli) in a micro-cell was investigated using dielectrophoretic impedance measurement (DEPIM) method. Temporal change in the conductance between micro-gap (ΔG) was measured for various heat treatment temperatures. In addition, the dependence of enzyme activity, growth capacity and membrane situation for E. coli on heat treatment temperature was also analyzed with conventional biological methods. Consequently, a correlation between ΔG and those biological properties was obtained quantitatively. This result suggests that DEPIM method will be available for an effective monitoring technique for complex change in various biological states of microorganisms.

Visualizing the 3D Architecture of Multiple Erythrocytes Infected with Plasmodium at Nanoscale by Focused Ion Beam-Scanning Electron Microscopy

PubMed Central

Soares Medeiros, Lia Carolina; De Souza, Wanderley; Jiao, Chengge; Barrabin, Hector; Miranda, Kildare

2012-01-01

Different methods for three-dimensional visualization of biological structures have been developed and extensively applied by different research groups. In the field of electron microscopy, a new technique that has emerged is the use of a focused ion beam and scanning electron microscopy for 3D reconstruction at nanoscale resolution. The higher extent of volume that can be reconstructed with this instrument represent one of the main benefits of this technique, which can provide statistically relevant 3D morphometrical data. As the life cycle of Plasmodium species is a process that involves several structurally complex developmental stages that are responsible for a series of modifications in the erythrocyte surface and cytoplasm, a high number of features within the parasites and the host cells has to be sampled for the correct interpretation of their 3D organization. Here, we used FIB-SEM to visualize the 3D architecture of multiple erythrocytes infected with Plasmodium chabaudi and analyzed their morphometrical parameters in a 3D space. We analyzed and quantified alterations on the host cells, such as the variety of shapes and sizes of their membrane profiles and parasite internal structures such as a polymorphic organization of hemoglobin-filled tubules. The results show the complex 3D organization of Plasmodium and infected erythrocyte, and demonstrate the contribution of FIB-SEM for the obtainment of statistical data for an accurate interpretation of complex biological structures. PMID:22432024

Contrasting ENSO types with novel satellite derived ocean phytoplankton biomass

NASA Astrophysics Data System (ADS)

Sharma, P.; Singh, A. M.; Marinov, I.; Kostadinov, T. S.

2016-12-01

Observed variations in community structure and biogeochemical processes in the tropics and the North Atlantic have been linked, in the first order, to the El Niño Southern Oscillation phenomenon (e.g., Bates, 2001; Karl et al., 2001; Di Lorenzo et al., 2010; Di Lorenzo et al., 2013). Current significant technical advances have allowed for the retrieval of biological data from the optical properties of the water via satellite ocean color remote sensing, providing an opportunity for quantifying the relationships between biological and climate indices. Studies have focused in-depth on contrasting flavors of the ENSO types with various physical (e.g., Singh et al. 2011; Turk et al. 2011) and biological (e.g., Radenac et al. 2012) indices. Here, we analyze the impact of different ENSO types on biology via analysis of recently-derived backscattering-based biomass separated into size-groups (Kostadinov et al. 2010, 2016) over the 17-year (1997-2013). We further contrast the responses of biomass with those of chlorophyll (Chl) and particulate inorganic carbon (PIC). We analyze the complex spatial differences in both physical (SST, mixed layer depth, winds) and biological (Chl, total and size-partitioned biomass) variability across the Pacific warm pool and equatorial tongue via simple EOF, combined regression-EOF and Agglomerative Hierarchical Clustering (AHC) analysis. The interannual variability in the physical and biological fields show clear signatures of the Niño cold-tongue (NCT) and Niño warm pool (NWP). Possible mechanisms responsible for these signatures are discussed.

Analysis of working conditions focusing on biological risk: firefighters in Campo Grande, MS, Brazil.

PubMed

Contrera-Moreno, Luciana; de Andrade, Sonia Maria Oliveira; Motta-Castro, Ana Rita Coimbra; Pinto, Alexandra Maria Almeida Carvalho; Salas, Frederico Reis Pouso; Stief, Alcione Cavalheiros Faro

2012-01-01

Firefighters are exposed to a wide range of risks, among them, biological risk. The objective was to analyze working conditions of firefighters in the city of Campo Grande, MS, Brazil, focusing on risk conditions of exposure to biological material. Three hundred and seven (307) firefighters were interviewed for data collection and observed for ergonomic job analysis (AET). 63.5% of the firefighters suffered some kind of job related accident with blood or body fluids. Statistically significant association was found between having suffered accidents at work and incomplete use of personal protective equipment (PPE). About AET regarding the biological risks, 57.1% of all patients had blood or secretions, which corresponds in average to 16.0% of the total work time, based on a working day of 24 h. Besides biological risks, other stressing factors were identified: emergency and complexity of decision, high responsibility regarding patients and environment, and conflicts. Health promotion and accident prevention actions must be emphasized as measures to minimize these risks.

Chemometric strategy for modeling metabolic biological space along the gastrointestinal tract and assessing microbial influences.

PubMed

Martin, François-Pierre J; Montoliu, Ivan; Kochhar, Sunil; Rezzi, Serge

2010-12-01

Over the past decade, the analysis of metabolic data with advanced chemometric techniques has offered the potential to explore functional relationships among biological compartments in relation to the structure and function of the intestine. However, the employed methodologies, generally based on regression modeling techniques, have given emphasis to region-specific metabolic patterns, while providing only limited insights into the spatiotemporal metabolic features of the complex gastrointestinal system. Hence, novel approaches are needed to analyze metabolic data to reconstruct the metabolic biological space associated with the evolving structures and functions of an organ such as the gastrointestinal tract. Here, we report the application of multivariate curve resolution (MCR) methodology to model metabolic relationships along the gastrointestinal compartments in relation to its structure and function using data from our previous metabonomic analysis. The method simultaneously summarizes metabolite occurrence and contribution to continuous metabolic signatures of the different biological compartments of the gut tract. This methodology sheds new light onto the complex web of metabolic interactions with gut symbionts that modulate host cell metabolism in surrounding gut tissues. In the future, such an approach will be key to provide new insights into the dynamic onset of metabolic deregulations involved in region-specific gastrointestinal disorders, such as Crohn's disease or ulcerative colitis.

Constrained target controllability of complex networks

NASA Astrophysics Data System (ADS)

Guo, Wei-Feng; Zhang, Shao-Wu; Wei, Ze-Gang; Zeng, Tao; Liu, Fei; Zhang, Jingsong; Wu, Fang-Xiang; Chen, Luonan

2017-06-01

It is of great theoretical interest and practical significance to study how to control a system by applying perturbations to only a few driver nodes. Recently, a hot topic of modern network researches is how to determine driver nodes that allow the control of an entire network. However, in practice, to control a complex network, especially a biological network, one may know not only the set of nodes which need to be controlled (i.e. target nodes), but also the set of nodes to which only control signals can be applied (i.e. constrained control nodes). Compared to the general concept of controllability, we introduce the concept of constrained target controllability (CTC) of complex networks, which concerns the ability to drive any state of target nodes to their desirable state by applying control signals to the driver nodes from the set of constrained control nodes. To efficiently investigate the CTC of complex networks, we further design a novel graph-theoretic algorithm called CTCA to estimate the ability of a given network to control targets by choosing driver nodes from the set of constrained control nodes. We extensively evaluate the CTC of numerous real complex networks. The results indicate that biological networks with a higher average degree are easier to control than biological networks with a lower average degree, while electronic networks with a lower average degree are easier to control than web networks with a higher average degree. We also show that our CTCA can more efficiently produce driver nodes for target-controlling the networks than existing state-of-the-art methods. Moreover, we use our CTCA to analyze two expert-curated bio-molecular networks and compare to other state-of-the-art methods. The results illustrate that our CTCA can efficiently identify proven drug targets and new potentials, according to the constrained controllability of those biological networks.

Comparative study of classification algorithms for immunosignaturing data

PubMed Central

2012-01-01

Background High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data. Results We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy. Conclusions ‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties. PMID:22720696

Antimicrobial Applications of Transition Metal Complexes of Benzothiazole Based Terpolymer: Synthesis, Characterization, and Effect on Bacterial and Fungal Strains

PubMed Central

Riswan Ahamed, Mohamed A.; Azarudeen, Raja S.; Kani, N. Mujafar

2014-01-01

Terpolymer of 2-amino-6-nitro-benzothiazole-ethylenediamine-formaldehyde (BEF) has been synthesized and characterized by elemental analysis and various spectral techniques like FTIR, UV-Visible, and 1H and 13C-NMR. The terpolymer metal complexes were prepared with Cu2+, Ni2+, and Zn2+ metal ions using BEF terpolymer as a ligand. The complexes have been characterized by elemental analysis and IR, UV-Visible, ESR, 1H-NMR, and 13C-NMR spectral studies. Gel permeation chromatography was used to determine the molecular weight of the ligand. The surface features and crystalline behavior of the ligand and its complexes were analyzed by scanning electron microscope and X-ray diffraction methods. Thermogravimetric analysis was used to analyze the thermal stability of the ligand and its metal complexes. Kinetic parameters such as activation energy (E a) and order of reaction (n) and thermodynamic parameters, namely, ΔS, ΔF, S*, and Z, were calculated using Freeman-Carroll (FC), Sharp-Wentworth (SW), and Phadnis-Deshpande (PD) methods. Thermal degradation model of the terpolymer and its metal complexes was also proposed using PD method. Biological activities of the ligand and its complexes were tested against Shigella sonnei, Escherichia coli, Klebsiella species, Staphylococcus aureus, Bacillus subtilis, and Salmonella typhimurium bacteria and Aspergillus flavus, Aspergillus niger, Penicillium species, Candida albicans, Cryptococcus neoformans, Mucor species fungi. PMID:25298760

Philosophical Basis and Some Historical Aspects of Systems Biology: From Hegel to Noble - Applications for Bioenergetic Research

PubMed Central

Saks, Valdur; Monge, Claire; Guzun, Rita

2009-01-01

We live in times of paradigmatic changes for the biological sciences. Reductionism, that for the last six decades has been the philosophical basis of biochemistry and molecular biology, is being displaced by Systems Biology, which favors the study of integrated systems. Historically, Systems Biology - defined as the higher level analysis of complex biological systems - was pioneered by Claude Bernard in physiology, Norbert Wiener with the development of cybernetics, and Erwin Schrödinger in his thermodynamic approach to the living. Systems Biology applies methods inspired by cybernetics, network analysis, and non-equilibrium dynamics of open systems. These developments follow very precisely the dialectical principles of development from thesis to antithesis to synthesis discovered by Hegel. Systems Biology opens new perspectives for studies of the integrated processes of energy metabolism in different cells. These integrated systems acquire new, system-level properties due to interaction of cellular components, such as metabolic compartmentation, channeling and functional coupling mechanisms, which are central for regulation of the energy fluxes. State of the art of these studies in the new area of Molecular System Bioenergetics is analyzed. PMID:19399243

Identification of hybrid node and link communities in complex networks

PubMed Central

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-01-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

Identification of hybrid node and link communities in complex networks.

PubMed

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-02

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Identification of hybrid node and link communities in complex networks

NASA Astrophysics Data System (ADS)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

The Meduza experiment: An orbital complex ten weeks in flight

NASA Technical Reports Server (NTRS)

Ovcharov, V.

1979-01-01

The newspaper article discusses the contribution of space research to understanding the origin of life on Earth. Part of this basic research involves studying amino acids, ribonucleic acid and DNA molecules subjected to cosmic radiation. The results from the Meduza experiment are not all analyzed as yet. The article also discusses the psychological changes in cosmonauts as evidenced by their attitude towards biology experiments in space.

Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family

PubMed Central

Uhart, Marina; Flores, Gabriel; Bustos, Diego M.

2016-01-01

Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems. PMID:27195976

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.

Conceptual Foundations of Systems Biology Explaining Complex Cardiac Diseases.

PubMed

Louridas, George E; Lourida, Katerina G

2017-02-21

Systems biology is an important concept that connects molecular biology and genomics with computing science, mathematics and engineering. An endeavor is made in this paper to associate basic conceptual ideas of systems biology with clinical medicine. Complex cardiac diseases are clinical phenotypes generated by integration of genetic, molecular and environmental factors. Basic concepts of systems biology like network construction, modular thinking, biological constraints (downward biological direction) and emergence (upward biological direction) could be applied to clinical medicine. Especially, in the field of cardiology, these concepts can be used to explain complex clinical cardiac phenotypes like chronic heart failure and coronary artery disease. Cardiac diseases are biological complex entities which like other biological phenomena can be explained by a systems biology approach. The above powerful biological tools of systems biology can explain robustness growth and stability during disease process from modulation to phenotype. The purpose of the present review paper is to implement systems biology strategy and incorporate some conceptual issues raised by this approach into the clinical field of complex cardiac diseases. Cardiac disease process and progression can be addressed by the holistic realistic approach of systems biology in order to define in better terms earlier diagnosis and more effective therapy.

Functional complexity and ecosystem stability: an experimental approach

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

Van Voris, P.; O'Neill, R.V.; Shugart, H.H.

1978-01-01

The complexity-stability hypothesis was experimentally tested using intact terrestrial microcosms. Functional complexity was defined as the number and significance of component interactions (i.e., population interactions, physical-chemical reactions, biological turnover rates) influenced by nonlinearities, feedbacks, and time delays. It was postulated that functional complexity could be nondestructively measured through analysis of a signal generated from the system. Power spectral analysis of hourly CO/sub 2/ efflux, from eleven old-field microcosms, was analyzed for the number of low frequency peaks and used to rank the functional complexity of each system. Ranking of ecosystem stability was based on the capacity of the system tomore » retain essential nutrients and was measured by net loss of Ca after the system was stressed. Rank correlation supported the hypothesis that increasing ecosystem functional complexity leads to increasing ecosystem stability. The results indicated that complex functional dynamics can serve to stabilize the system. The results also demonstrated that microcosms are useful tools for system-level investigations.« less

Human Development VI: Supracellular Morphogenesis. The Origin of Biological and Cellular Order

PubMed Central

Ventegodt, Søren; Hermansen, Tyge Dahl; Flensborg-Madsen, Trine; Nielsen, Maj Lyck; Merrick, Joav

2006-01-01

Uninterrupted morphogenesis shows the informational potentials of biological organisms. Experimentally disturbed morphogenesis shows the compensational dynamics of the biological informational system, which is the rich informational redundancy. In this paper, we use these data to describe morphogenesis in terms of the development of supracellular levels of the organism, and we define complex epigenesis and supracellular differentiation. We review the phenomena of regeneration and induction of Hydra and amphibians, and the higher animals informational needs for developing their complex nervous systems. We argue, also building on the NO-GO theorem for ontogenesis as chemistry, that the traditional chemical explanations of high-level informational events in ontogenesis, such as transmutation, regeneration, and induction, are insufficient. We analyze the informational dynamics of three embryonic compensatory reactions to different types of disturbances: (1) transmutations of the imaginal discs of insects, (2) regeneration after removal of embryonic tissue, and (3) embryonic induction, where two tissues that normally are separated experimentally are made to influence each other. We describe morphogenesis as a complex bifurcation, and the resulting morphological levels of the organism as organized in a fractal manner and supported by positional information. We suggest that some kind of real nonchemical phenomenon must be taking form in living organisms as an information-carrying dynamic fractal field, causing morhogenesis and supporting the organisms morphology through time. We argue that only such a phenomenon that provides information-directed self-organization to the organism is able to explain the observed dynamic distribution of biological information through morphogenesis and the organism's ability to rejuvenate and heal. PMID:17115082

A System-Level Pathway-Phenotype Association Analysis Using Synthetic Feature Random Forest

PubMed Central

Pan, Qinxin; Hu, Ting; Malley, James D.; Andrew, Angeline S.; Karagas, Margaret R.; Moore, Jason H.

2015-01-01

As the cost of genome-wide genotyping decreases, the number of genome-wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system-level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high-throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single-marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single-nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene-gene interactions and pathway-pathway relationships, we propose a system-level pathway analysis approach, synthetic feature random forest (SF-RF), which is designed to detect pathway-phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway-phenotype association. We apply SF-RF to a population-based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway-phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations. PMID:24535726

An examination of conceptual change in undergraduate biology majors while learning science concepts including biological evolution

NASA Astrophysics Data System (ADS)

McQuaide, Glenn G.

2006-12-01

Without adequate understanding of science, we cannot make responsible personal, regional, national, or global decisions about any aspect of life dealing with science. Better understanding how we learn about science can contribute to improving the quality of our educational experiences. Promoting pathways leading to life-long learning and deep understanding in our world should be a goal for all educators. This dissertation project was a phenomenological investigation into undergraduate understanding and acceptance of scientific theories, including biological evolution. Specifically, student descriptions of conceptual change while learning science theory were recorded and analyzed. These qualitative investigations were preceded by a survey that provided a means of selecting students who had a firmer understanding of science theory. Background information and survey data were collected in an undergraduate biology class at a small, Southern Baptist-affiliated liberal arts school located in south central Kentucky. Responses to questions on the MATE (Rutledge and Warden, 1999) instrument were used to screen students for interviews, which investigated the way by which students came to understand and accept scientific theories. This study identifies some ways by which individuals learn complex science theories, including biological evolution. Initial understanding and acceptance often occurs by the conceptual change method described by Posner et al. (1982). Three principle ways by which an individual may reach a level of understanding and acceptance of science theory were documented in this study. They were conceptual change through application of logic and reasoning; conceptual change through modification of religious views; and conceptual change through acceptance of authoritative knowledge. Development of a deeper, richer understanding and acceptance of complex, multi-faceted concepts such as biological evolution occurs in some individuals by means of conceptual enrichment. Conceptual enrichment occurs through addition of new knowledge, and then examining prior knowledge through the perspective of this new knowledge. In the field of science, enrichment reinforces complex concepts when multiple, convergent lines of supporting evidences point to the same rational scientific conclusion.

Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer.

PubMed

Khalid, Samra; Hanif, Rumeza; Tareen, Samar H K; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

2016-01-01

Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER- α ) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis. A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities. In-silico based discrete and continuous modeling of ER- α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER- α , IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER- α , IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

The emergence of mind and brain: an evolutionary, computational, and philosophical approach.

PubMed

Mainzer, Klaus

2008-01-01

Modern philosophy of mind cannot be understood without recent developments in computer science, artificial intelligence (AI), robotics, neuroscience, biology, linguistics, and psychology. Classical philosophy of formal languages as well as symbolic AI assume that all kinds of knowledge must explicitly be represented by formal or programming languages. This assumption is limited by recent insights into the biology of evolution and developmental psychology of the human organism. Most of our knowledge is implicit and unconscious. It is not formally represented, but embodied knowledge, which is learnt by doing and understood by bodily interacting with changing environments. That is true not only for low-level skills, but even for high-level domains of categorization, language, and abstract thinking. The embodied mind is considered an emergent capacity of the brain as a self-organizing complex system. Actually, self-organization has been a successful strategy of evolution to handle the increasing complexity of the world. Genetic programs are not sufficient and cannot prepare the organism for all kinds of complex situations in the future. Self-organization and emergence are fundamental concepts in the theory of complex dynamical systems. They are also applied in organic computing as a recent research field of computer science. Therefore, cognitive science, AI, and robotics try to model the embodied mind in an artificial evolution. The paper analyzes these approaches in the interdisciplinary framework of complex dynamical systems and discusses their philosophical impact.

Quantifying Carbon-14 for Biology Using Cavity Ring-Down Spectroscopy

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

McCartt, A. Daniel; Ognibene, Ted J.; Bench, Graham

A cavity ring-down spectroscopy (CRDS) instrument was developed using mature, robust hardware for the measurement of carbon-14 in biological studies. The system was characterized using carbon-14 elevated glucose samples and returned a linear response up to 387 times contemporary carbon-14 concentrations. Carbon-14 free and contemporary carbon-14 samples with varying carbon-13 concentrations were used to assess the method detection limit of approximately one-third contemporary carbon-14 levels. Sources of inaccuracies are presented and discussed, and the capability to measure carbon-14 in biological samples is demonstrated by comparing pharmacokinetics from carbon-14 dosed guinea pigs analyzed by both CRDS and accelerator mass spectrometry. Here,more » the CRDS approach presented affords easy access to powerful carbon-14 tracer techniques that can characterize complex biochemical systems.« less

Quantifying Carbon-14 for Biology Using Cavity Ring-Down Spectroscopy

DOE PAGES

McCartt, A. Daniel; Ognibene, Ted J.; Bench, Graham; ...

2016-07-26

A cavity ring-down spectroscopy (CRDS) instrument was developed using mature, robust hardware for the measurement of carbon-14 in biological studies. The system was characterized using carbon-14 elevated glucose samples and returned a linear response up to 387 times contemporary carbon-14 concentrations. Carbon-14 free and contemporary carbon-14 samples with varying carbon-13 concentrations were used to assess the method detection limit of approximately one-third contemporary carbon-14 levels. Sources of inaccuracies are presented and discussed, and the capability to measure carbon-14 in biological samples is demonstrated by comparing pharmacokinetics from carbon-14 dosed guinea pigs analyzed by both CRDS and accelerator mass spectrometry. Here,more » the CRDS approach presented affords easy access to powerful carbon-14 tracer techniques that can characterize complex biochemical systems.« less

Conceptual Demand of Practical Work in Science Curricula. A Methodological Approach

NASA Astrophysics Data System (ADS)

Ferreira, Sílvia; Morais, Ana M.

2014-02-01

This article addresses the issue of the level of complexity of practical work in science curricula and is focused on the discipline of Biology and Geology at high school. The level of complexity is seen in terms of the emphasis on and types of practical work and, most importantly, in terms of its level of conceptual demand as given by the complexity of scientific knowledge, the degree of inter-relation between knowledges, and the complexity of cognitive skills. The study also analyzes recontextualizing processes that may occur within the official recontextualizing field. The study is psychologically and sociologically grounded, particularly on Bernstein's theory of pedagogic discourse. It uses a mixed methodology. The results show that practical work is poorly represented in the curriculum, particularly in the case of laboratory work. The level of conceptual demand of practical work varies according to the text under analysis, between the two subjects Biology and Geology, and, within each of them, between general and specific guidelines. Aspects studied are not clearly explicated to curriculum receivers (teachers and textbooks authors). The meaning of these findings is discussed in the article. In methodological terms, the study explores assumptions used in the analysis of the level of conceptual demand and presents innovative instruments constructed for developing this analysis.

Towards human-computer synergetic analysis of large-scale biological data.

PubMed

Singh, Rahul; Yang, Hui; Dalziel, Ben; Asarnow, Daniel; Murad, William; Foote, David; Gormley, Matthew; Stillman, Jonathan; Fisher, Susan

2013-01-01

Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/.

Towards human-computer synergetic analysis of large-scale biological data

PubMed Central

2013-01-01

Background Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. Results In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. Conclusions The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/. PMID:24267485

Multiscale entropy-based methods for heart rate variability complexity analysis

NASA Astrophysics Data System (ADS)

Silva, Luiz Eduardo Virgilio; Cabella, Brenno Caetano Troca; Neves, Ubiraci Pereira da Costa; Murta Junior, Luiz Otavio

2015-03-01

Physiologic complexity is an important concept to characterize time series from biological systems, which associated to multiscale analysis can contribute to comprehension of many complex phenomena. Although multiscale entropy has been applied to physiological time series, it measures irregularity as function of scale. In this study we purpose and evaluate a set of three complexity metrics as function of time scales. Complexity metrics are derived from nonadditive entropy supported by generation of surrogate data, i.e. SDiffqmax, qmax and qzero. In order to access accuracy of proposed complexity metrics, receiver operating characteristic (ROC) curves were built and area under the curves was computed for three physiological situations. Heart rate variability (HRV) time series in normal sinus rhythm, atrial fibrillation, and congestive heart failure data set were analyzed. Results show that proposed metric for complexity is accurate and robust when compared to classic entropic irregularity metrics. Furthermore, SDiffqmax is the most accurate for lower scales, whereas qmax and qzero are the most accurate when higher time scales are considered. Multiscale complexity analysis described here showed potential to assess complex physiological time series and deserves further investigation in wide context.

From data to knowledge: The future of multi-omics data analysis for the rhizosphere

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

Allen White, Richard; Borkum, Mark I.; Rivas-Ubach, Albert

The rhizosphere is the interface between a plant's roots and its surrounding soil. The rhizosphere microbiome, a complex microbial ecosystem, nourishes the terrestrial biosphere. Integrated multi-omics is a modern approach to systems biology that analyzes and interprets the datasets of multiple -omes of both individual organisms and multi-organism communities and consortia. The successful usage and application of integrated multi-omics to rhizospheric science is predicated upon the availability of rhizosphere-specific data, metadata and software. This review analyzes the availability of multi-omics data, metadata and software for rhizospheric science, identifying potential issues, challenges and opportunities.

What experimental approaches (eg, in vivo, in vitro, tissue retrieval) are effective in investigating the biologic effects of particles?

PubMed Central

Bostrom, Mathias; O'Keefe, Regis

2009-01-01

Understanding the complex cellular and tissue mechanisms and interactions resulting in periprosthetic osteolysis requires a number of experimental approaches, each of which has its own set of advantages and limitations. In vitro models allow for the isolation of individual cell populations and have furthered our understanding of particle-cell interactions; however, they are limited because they do not mimic the complex tissue environment in which multiple cell interactions occur. In vivo animal models investigate the tissue interactions associated with periprosthetic osteolysis, but the choice of species and whether the implant system is subjected to mechanical load or to unloaded conditions are critical in assessing whether these models can be extrapolated to the clinical condition. Rigid analysis of retrieved tissue from clinical cases of osteolysis offers a different approach to studying the biologic process of osteolysis, but it is limited in that the tissue analyzed represents the end-stage of this process and, thus, may not reflect this process adequately. PMID:18612016

What experimental approaches (eg, in vivo, in vitro, tissue retrieval) are effective in investigating the biologic effects of particles?

PubMed

Bostrom, Mathias; O'Keefe, Regis

2008-01-01

Understanding the complex cellular and tissue mechanisms and interactions resulting in periprosthetic osteolysis requires a number of experimental approaches, each of which has its own set of advantages and limitations. In vitro models allow for the isolation of individual cell populations and have furthered our understanding of particle-cell interactions; however, they are limited because they do not mimic the complex tissue environment in which multiple cell interactions occur. In vivo animal models investigate the tissue interactions associated with periprosthetic osteolysis, but the choice of species and whether the implant system is subjected to mechanical load or to unloaded conditions are critical in assessing whether these models can be extrapolated to the clinical condition. Rigid analysis of retrieved tissue from clinical cases of osteolysis offers a different approach to studying the biologic process of osteolysis, but it is limited in that the tissue analyzed represents the end-stage of this process and, thus, may not reflect this process adequately.

High in Vivo Stability of 64Cu-Labeled Cross-Bridged Chelators Is a Crucial Factor in Improved Tumor Imaging of RGD Peptide Conjugates.

PubMed

Sarkar, Swarbhanu; Bhatt, Nikunj; Ha, Yeong Su; Huynh, Phuong Tu; Soni, Nisarg; Lee, Woonghee; Lee, Yong Jin; Kim, Jung Young; Pandya, Darpan N; An, Gwang Il; Lee, Kyo Chul; Chang, Yongmin; Yoo, Jeongsoo

2018-01-11

Although the importance of bifunctional chelators (BFCs) is well recognized, the chemophysical parameters of chelators that govern the biological behavior of the corresponding bioconjugates have not been clearly elucidated. Here, five BFCs closely related in structure were conjugated with a cyclic RGD peptide and radiolabeled with Cu-64 ions. Various biophysical and chemical properties of the Cu(II) complexes were analyzed with the aim of identifying correlations between individual factors and the biological behavior of the conjugates. Tumor uptake and body clearance of the 64 Cu-labeled bioconjugates were directly compared by animal PET imaging in animal models, which was further supported by biodistribution studies. Conjugates containing propylene cross-bridged chelators showed higher tumor uptake, while a closely related ethylene cross-bridged analogue exhibited rapid body clearance. High in vivo stability of the copper-chelator complex was strongly correlated with high tumor uptake, while the overall lipophilicity of the bioconjugate affected both tumor uptake and body clearance.

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

PubMed Central

2010-01-01

Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks. PMID:21070623

78 FR 58311 - Complex Issues in Developing Drug and Biological Products for Rare Diseases; Public Workshop...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-23

...] Complex Issues in Developing Drug and Biological Products for Rare Diseases; Public Workshop; Request for... Issues in Developing Drug and Biological Products for Rare Diseases.'' The purpose of the public workshop is twofold: To discuss complex issues in clinical trials for developing drug and biological products...

Site-specific identification of heparan and chondroitin sulfate glycosaminoglycans in hybrid proteoglycans.

PubMed

Noborn, Fredrik; Gomez Toledo, Alejandro; Green, Anders; Nasir, Waqas; Sihlbom, Carina; Nilsson, Jonas; Larson, Göran

2016-10-03

Heparan sulfate (HS) and chondroitin sulfate (CS) are complex polysaccharides that regulate important biological pathways in virtually all metazoan organisms. The polysaccharides often display opposite effects on cell functions with HS and CS structural motifs presenting unique binding sites for specific ligands. Still, the mechanisms by which glycan biosynthesis generates complex HS and CS polysaccharides required for the regulation of mammalian physiology remain elusive. Here we present a glycoproteomic approach that identifies and differentiates between HS and CS attachment sites and provides identity to the core proteins. Glycopeptides were prepared from perlecan, a complex proteoglycan known to be substituted with both HS and CS chains, further digested with heparinase or chondroitinase ABC to reduce the HS and CS chain lengths respectively, and thereafter analyzed by nLC-MS/MS. This protocol enabled the identification of three consensus HS sites and one hybrid site, carrying either a HS or a CS chain. Inspection of the amino acid sequence at the hybrid attachment locus indicates that certain peptide motifs may encode for the chain type selection process. This analytical approach will become useful when addressing fundamental questions in basic biology specifically in elucidating the functional roles of site-specific glycosylations of proteoglycans.

Integration of systems biology with organs-on-chips to humanize therapeutic development

NASA Astrophysics Data System (ADS)

Edington, Collin D.; Cirit, Murat; Chen, Wen Li Kelly; Clark, Amanda M.; Wells, Alan; Trumper, David L.; Griffith, Linda G.

2017-02-01

"Mice are not little people" - a refrain becoming louder as the gaps between animal models and human disease become more apparent. At the same time, three emerging approaches are headed toward integration: powerful systems biology analysis of cell-cell and intracellular signaling networks in patient-derived samples; 3D tissue engineered models of human organ systems, often made from stem cells; and micro-fluidic and meso-fluidic devices that enable living systems to be sustained, perturbed and analyzed for weeks in culture. Integration of these rapidly moving fields has the potential to revolutionize development of therapeutics for complex, chronic diseases, including those that have weak genetic bases and substantial contributions from gene-environment interactions. Technical challenges in modeling complex diseases with "organs on chips" approaches include the need for relatively large tissue masses and organ-organ cross talk to capture systemic effects, such that current microfluidic formats often fail to capture the required scale and complexity for interconnected systems. These constraints drive development of new strategies for designing in vitro models, including perfusing organ models, as well as "mesofluidic" pumping and circulation in platforms connecting several organ systems, to achieve the appropriate physiological relevance.

A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

PubMed Central

Hruz, Tomas; Lucas, Christoph; Laule, Oliver; Zimmermann, Philip

2013-01-01

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. PMID:23864855

Quantitative interaction analysis permits molecular insights into functional NOX4 NADPH oxidase heterodimer assembly.

PubMed

O'Neill, Sharon; Mathis, Magalie; Kovačič, Lidija; Zhang, Suisheng; Reinhardt, Jürgen; Scholz, Dimitri; Schopfer, Ulrich; Bouhelal, Rochdi; Knaus, Ulla G

2018-06-08

Protein-protein interactions critically regulate many biological systems, but quantifying functional assembly of multipass membrane complexes in their native context is still challenging. Here, we combined modeling-assisted protein modification and information from human disease variants with a minimal-size fusion tag, split-luciferase-based approach to probe assembly of the NADPH oxidase 4 (NOX4)-p22 phox enzyme, an integral membrane complex with unresolved structure, which is required for electron transfer and generation of reactive oxygen species (ROS). Integrated analyses of heterodimerization, trafficking, and catalytic activity identified determinants for the NOX4-p22 phox interaction, such as heme incorporation into NOX4 and hot spot residues in transmembrane domains 1 and 4 in p22 phox Moreover, their effect on NOX4 maturation and ROS generation was analyzed. We propose that this reversible and quantitative protein-protein interaction technique with its small split-fragment approach will provide a protein engineering and discovery tool not only for NOX research, but also for other intricate membrane protein complexes, and may thereby facilitate new drug discovery strategies for managing NOX-associated diseases. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

A photo-cross-linking approach to monitor folding and assembly of newly synthesized proteins in a living cell.

PubMed

Miyazaki, Ryoji; Myougo, Naomi; Mori, Hiroyuki; Akiyama, Yoshinori

2018-01-12

Many proteins form multimeric complexes that play crucial roles in various cellular processes. Studying how proteins are correctly folded and assembled into such complexes in a living cell is important for understanding the physiological roles and the qualitative and quantitative regulation of the complex. However, few methods are suitable for analyzing these rapidly occurring processes. Site-directed in vivo photo-cross-linking is an elegant technique that enables analysis of protein-protein interactions in living cells with high spatial resolution. However, the conventional site-directed in vivo photo-cross-linking method is unsuitable for analyzing dynamic processes. Here, by combining an improved site-directed in vivo photo-cross-linking technique with a pulse-chase approach, we developed a new method that can analyze the folding and assembly of a newly synthesized protein with high spatiotemporal resolution. We demonstrate that this method, named the pulse-chase and in vivo photo-cross-linking experiment (PiXie), enables the kinetic analysis of the formation of an Escherichia coli periplasmic (soluble) protein complex (PhoA). We also used our new technique to investigate assembly/folding processes of two membrane complexes (SecD-SecF in the inner membrane and LptD-LptE in the outer membrane), which provided new insights into the biogenesis of these complexes. Our PiXie method permits analysis of the dynamic behavior of various proteins and enables examination of protein-protein interactions at the level of individual amino acid residues. We anticipate that our new technique will have valuable utility for studies of protein dynamics in many organisms. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

Decoding Mechanisms by which Silent Codon Changes Influence Protein Biogenesis and Function

PubMed Central

Bali, Vedrana; Bebok, Zsuzsanna

2015-01-01

Scope Synonymous codon usage has been a focus of investigation since the discovery of the genetic code and its redundancy. The occurrences of synonymous codons vary between species and within genes of the same genome, known as codon usage bias. Today, bioinformatics and experimental data allow us to compose a global view of the mechanisms by which the redundancy of the genetic code contributes to the complexity of biological systems from affecting survival in prokaryotes, to fine tuning the structure and function of proteins in higher eukaryotes. Studies analyzing the consequences of synonymous codon changes in different organisms have revealed that they impact nucleic acid stability, protein levels, structure and function without altering amino acid sequence. As such, synonymous mutations inevitably contribute to the pathogenesis of complex human diseases. Yet, fundamental questions remain unresolved regarding the impact of silent mutations in human disorders. In the present review we describe developments in this area concentrating on mechanisms by which synonymous mutations may affect protein function and human health. Purpose This synopsis illustrates the significance of synonymous mutations in disease pathogenesis. We review the different steps of gene expression affected by silent mutations, and assess the benefits and possible harmful effects of codon optimization applied in the development of therapeutic biologics. Physiological and medical relevance Understanding mechanisms by which synonymous mutations contribute to complex diseases such as cancer, neurodegeneration and genetic disorders, including the limitations of codon-optimized biologics, provides insight concerning interpretation of silent variants and future molecular therapies. PMID:25817479

Preparation of Degradable Biological Carrier With LCC and its Application in Culture of Hepatocytes

NASA Astrophysics Data System (ADS)

Zhao, H. K.; Chen, X. K.; Wu, H. F.; Li, J. L.; Xie, Y. M.

2018-05-01

The purpose of this article is to extract lignin-carbohydrate complexes (LCC) with poplar as raw material, which was used to prepare bio-carrier by freeze-drying method. The chemical properties and morphological of LCC porous biological carriers were analyzed by GPC, FT-IR, scanning electron microscopy (SEM) and optical microscopy. The FT-IR spectrum results indicated that LCC which are composed of lignin and polysaccharide, with a typical LCC structure. Galactose have a specific ability to recognize liver cells owing to the presence of receptors on hepatocytes. Cell counting results showed that the cells increases fastest while the proliferation rate of the liver cell in LCC is obviously higher than that of control group. These results indicated that poplar LCC is very biocompatible, in which it might be a great potential biological carrier material for human hepatocyte culture.

Chemometric analysis of soil pollution data using the Tucker N-way method.

PubMed

Stanimirova, I; Zehl, K; Massart, D L; Vander Heyden, Y; Einax, J W

2006-06-01

N-way methods, particularly the Tucker method, are often the methods of choice when analyzing data sets arranged in three- (or higher) way arrays, which is the case for most environmental data sets. In the future, applying N-way methods will become an increasingly popular way to uncover hidden information in complex data sets. The reason for this is that classical two-way approaches such as principal component analysis are not as good at revealing the complex relationships present in data sets. This study describes in detail the application of a chemometric N-way approach, namely the Tucker method, in order to evaluate the level of pollution in soil from a contaminated site. The analyzed soil data set was five-way in nature. The samples were collected at different depths (way 1) from two locations (way 2) and the levels of thirteen metals (way 3) were analyzed using a four-step-sequential extraction procedure (way 4), allowing detailed information to be obtained about the bioavailability and activity of the different binding forms of the metals. Furthermore, the measurements were performed under two conditions (way 5), inert and non-inert. The preferred Tucker model of definite complexity showed that there was no significant difference in measurements analyzed under inert or non-inert conditions. It also allowed two depth horizons, characterized by different accumulation pathways, to be distinguished, and it allowed the relationships between chemical elements and their biological activities and mobilities in the soil to be described in detail.

Pathway-Based Genome-Wide Association Studies for Two Meat Production Traits in Simmental Cattle.

PubMed

Fan, Huizhong; Wu, Yang; Zhou, Xiaojing; Xia, Jiangwei; Zhang, Wengang; Song, Yuxin; Liu, Fei; Chen, Yan; Zhang, Lupei; Gao, Xue; Gao, Huijiang; Li, Junya

2015-12-17

Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.

A supramolecular complex between proteinases and beta-cyclodextrin that preserves enzymatic activity: physicochemical characterization.

PubMed

Denadai, Angelo M L; Santoro, Marcelo M; Lopes, Miriam T P; Chenna, Angélica; de Sousa, Frederico B; Avelar, Gabriela M; Gomes, Marco R Túlio; Guzman, Fanny; Salas, Carlos E; Sinisterra, Rubén D

2006-01-01

Cyclodextrins are suitable drug delivery systems because of their ability to subtly modify the physical, chemical, and biological properties of guest molecules through labile interactions by formation of inclusion and/or association complexes. Plant cysteine proteinases from Caricaceae and Bromeliaceae are the subject of therapeutic interest, because of their anti-inflammatory, antitumoral, immunogenic, and wound-healing properties. In this study, we analyzed the association between beta-cyclodextrin (betaCD) and fraction P1G10 containing the bioactive proteinases from Carica candamarcensis, and described the physicochemical nature of the solid-state self-assembled complexes by Fourier transform infrared (FTIR) spectroscopy, thermogravimetry (TG), differential scanning calorimetry (DSC), X-ray powder diffraction (XRD), and nuclear magnetic resonance (NMR), as well as in solution by circular dichroism (CD), isothermal titration calorimetry (ITC), and amidase activity. The physicochemical analyses suggest the formation of a complex between P1G10 and betaCD. Higher secondary interactions, namely hydrophobic interactions, hydrogen bonding and van der Waals forces were observed at higher P1G10 : betaCD mass ratios. These results provide evidence of the occurrence of strong solid-state supramolecular non-covalent interactions between P1G10 and betaCD. Microcalorimetric analysis demonstrates that complexation results in a favorable enthalpic contribution, as has already been described during formation of similar betaCD inclusion compounds. The amidase activity of the complex shows that the enzyme activity is not readily available at 24 hours after dissolution of the complex in aqueous buffer; the proteinase becomes biologically active by the second day and remains stable until day 16, when a gradual decrease occurs, with basal activity attained by day 29. The reported results underscore the potential for betaCDs as candidates for complexing cysteine proteinases, resulting in supramolecular arrays with sustained proteolytic activity.

iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states.

PubMed

Bordbar, Aarash; Jamshidi, Neema; Palsson, Bernhard O

2011-07-12

The development of high-throughput technologies capable of whole cell measurements of genes, proteins, and metabolites has led to the emergence of systems biology. Integrated analysis of the resulting omic data sets has proved to be hard to achieve. Metabolic network reconstructions enable complex relationships amongst molecular components to be represented formally in a biologically relevant manner while respecting physical constraints. In silico models derived from such reconstructions can then be queried or interrogated through mathematical simulations. Proteomic profiling studies of the mature human erythrocyte have shown more proteins present related to metabolic function than previously thought; however the significance and the causal consequences of these findings have not been explored. Erythrocyte proteomic data was used to reconstruct the most expansive description of erythrocyte metabolism to date, following extensive manual curation, assessment of the literature, and functional testing. The reconstruction contains 281 enzymes representing functions from glycolysis to cofactor and amino acid metabolism. Such a comprehensive view of erythrocyte metabolism implicates the erythrocyte as a potential biomarker for different diseases as well as a 'cell-based' drug-screening tool. The analysis shows that 94 erythrocyte enzymes are implicated in morbid single nucleotide polymorphisms, representing 142 pathologies. In addition, over 230 FDA-approved and experimental pharmaceuticals have enzymatic targets in the erythrocyte. The advancement of proteomic technologies and increased generation of high-throughput proteomic data have created the need for a means to analyze these data in a coherent manner. Network reconstructions provide a systematic means to integrate and analyze proteomic data in a biologically meaning manner. Analysis of the red cell proteome has revealed an unexpected level of complexity in the functional capabilities of human erythrocyte metabolism.

What is microbial community ecology?

PubMed

Konopka, Allan

2009-11-01

The activities of complex communities of microbes affect biogeochemical transformations in natural, managed and engineered ecosystems. Meaningfully defining what constitutes a community of interacting microbial populations is not trivial, but is important for rigorous progress in the field. Important elements of research in microbial community ecology include the analysis of functional pathways for nutrient resource and energy flows, mechanistic understanding of interactions between microbial populations and their environment, and the emergent properties of the complex community. Some emergent properties mirror those analyzed by community ecologists who study plants and animals: biological diversity, functional redundancy and system stability. However, because microbes possess mechanisms for the horizontal transfer of genetic information, the metagenome may also be considered as a community property.

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

Konopka, Allan

The activities of complex communities of microbes affect biogeochemical transformations in natural, managed and engineered ecosystems. Meaningfully defining what constitutes a community of interacting microbial populations is not trivial, but is important for rigorous progress in the field. Important elements of research in microbial community ecology include the analysis of functional pathways for nutrient resource and energy flows, mechanistic understanding of interactions between microbial populations and their environment, and the emergent properties of the complex community. Some emergent properties mirror those analyzed by community ecologists who study plants and animals: biological diversity, functional redundancy and system stability. However, because microbesmore » possess mechanisms for the horizontal transfer of genetic information, the metagenome may also be considered a community property.« less

Bioimage informatics for experimental biology

PubMed Central

Swedlow, Jason R.; Goldberg, Ilya G.; Eliceiri, Kevin W.

2012-01-01

Over the last twenty years there have been great advances in light microscopy with the result that multi-dimensional imaging has driven a revolution in modern biology. The development of new approaches of data acquisition are reportedly frequently, and yet the significant data management and analysis challenges presented by these new complex datasets remains largely unsolved. Like the well-developed field of genome bioinformatics, central repositories are and will be key resources, but there is a critical need for informatics tools in individual laboratories to help manage, share, visualize, and analyze image data. In this article we present the recent efforts by the bioimage informatics community to tackle these challenges and discuss our own vision for future development of bioimage informatics solution. PMID:19416072

Analyzing and Understanding Lipids of Yeast: A Challenging Endeavor.

PubMed

Kohlwein, Sepp D

2017-05-01

Lipids are essential biomolecules with diverse biological functions, ranging from building blocks for all biological membranes to energy substrates, signaling molecules, and protein modifiers. Despite advances in lipid analytics by mass spectrometry, the extraction and quantitative analysis of the diverse classes of lipids are still an experimental challenge. Yeast is a model organism that provides several advantages for studying lipid metabolism, because most biosynthetic pathways are well described and a great deal of information is available on the regulatory mechanisms that control lipid homeostasis. In addition, the composition of yeast lipids is much less complex than that of mammalian lipids, making yeast an excellent reference system for studying lipid-associated cell functions. © 2017 Cold Spring Harbor Laboratory Press.

NETTAB 2012 on "Integrated Bio-Search"

PubMed Central

2014-01-01

The NETTAB 2012 workshop, held in Como on November 14-16, 2012, was devoted to "Integrated Bio-Search", that is to technologies, methods, architectures, systems and applications for searching, retrieving, integrating and analyzing data, information, and knowledge with the aim of answering complex bio-medical-molecular questions, i.e. some of the most challenging issues in bioinformatics today. It brought together about 80 researchers working in the field of Bioinformatics, Computational Biology, Biology, Computer Science and Engineering. More than 50 scientific contributions, including keynote and tutorial talks, oral communications, posters and software demonstrations, were presented at the workshop. This preface provides a brief overview of the workshop and shortly introduces the peer-reviewed manuscripts that were accepted for publication in this Supplement. PMID:24564635

TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages

PubMed Central

Bontempi, Gianluca; Ceccarelli, Michele; Noushmehr, Houtan

2016-01-01

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox,  TCGAbiolinks. PMID:28232861

TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages.

PubMed

Silva, Tiago C; Colaprico, Antonio; Olsen, Catharina; D'Angelo, Fulvio; Bontempi, Gianluca; Ceccarelli, Michele; Noushmehr, Houtan

2016-01-01

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox,  TCGAbiolinks.

Computational systems biology and dose-response modeling in relation to new directions in toxicity testing.

PubMed

Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E; Conolly, Rory B

2010-02-01

The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

Integration of biological networks and gene expression data using Cytoscape

PubMed Central

Cline, Melissa S; Smoot, Michael; Cerami, Ethan; Kuchinsky, Allan; Landys, Nerius; Workman, Chris; Christmas, Rowan; Avila-Campilo, Iliana; Creech, Michael; Gross, Benjamin; Hanspers, Kristina; Isserlin, Ruth; Kelley, Ryan; Killcoyne, Sarah; Lotia, Samad; Maere, Steven; Morris, John; Ono, Keiichiro; Pavlovic, Vuk; Pico, Alexander R; Vailaya, Aditya; Wang, Peng-Liang; Adler, Annette; Conklin, Bruce R; Hood, Leroy; Kuiper, Martin; Sander, Chris; Schmulevich, Ilya; Schwikowski, Benno; Warner, Guy J; Ideker, Trey; Bader, Gary D

2013-01-01

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape. PMID:17947979

Advanced techniques in placental biology -- workshop report.

PubMed

Nelson, D M; Sadovsky, Y; Robinson, J M; Croy, B A; Rice, G; Kniss, D A

2006-04-01

Major advances in placental biology have been realized as new technologies have been developed and existing methods have been refined in many areas of biological research. Classical anatomy and whole-organ physiology tools once used to analyze placental structure and function have been supplanted by more sophisticated techniques adapted from molecular biology, proteomics, and computational biology and bioinformatics. In addition, significant refinements in morphological study of the placenta and its constituent cell types have improved our ability to assess form and function in highly integrated manner. To offer an overview of modern technologies used by investigators to study the placenta, this workshop: Advanced techniques in placental biology, assembled experts who discussed fundamental principles and real time examples of four separate methodologies. Y. Sadovsky presented the principles of microRNA function as an endogenous mechanism of gene regulation. J. Robinson demonstrated the utility of correlative microscopy in which light-level and transmission electron microscopy are combined to provide cellular and subcellular views of placental cells. A. Croy provided a lecture on the use of microdissection techniques which are invaluable for isolating very small subsets of cell types for molecular analysis. Finally, G. Rice presented an overview methods on profiling of complex protein mixtures within tissue and/or fluid samples that, when refined, will offer databases that will underpin a systems approach to modern trophoblast biology.

Formal reasoning about systems biology using theorem proving

PubMed Central

Hasan, Osman; Siddique, Umair; Tahar, Sofiène

2017-01-01

System biology provides the basis to understand the behavioral properties of complex biological organisms at different levels of abstraction. Traditionally, analysing systems biology based models of various diseases have been carried out by paper-and-pencil based proofs and simulations. However, these methods cannot provide an accurate analysis, which is a serious drawback for the safety-critical domain of human medicine. In order to overcome these limitations, we propose a framework to formally analyze biological networks and pathways. In particular, we formalize the notion of reaction kinetics in higher-order logic and formally verify some of the commonly used reaction based models of biological networks using the HOL Light theorem prover. Furthermore, we have ported our earlier formalization of Zsyntax, i.e., a deductive language for reasoning about biological networks and pathways, from HOL4 to the HOL Light theorem prover to make it compatible with the above-mentioned formalization of reaction kinetics. To illustrate the usefulness of the proposed framework, we present the formal analysis of three case studies, i.e., the pathway leading to TP53 Phosphorylation, the pathway leading to the death of cancer stem cells and the tumor growth based on cancer stem cells, which is used for the prognosis and future drug designs to treat cancer patients. PMID:28671950

Using an Adoption–Biological Family Design to Examine Associations Between Maternal Trauma, Maternal Depressive Symptoms, and Child Internalizing and Externalizing Behaviors

PubMed Central

Grabow, Aleksandria Perez; Khurana, Atika; Natsuaki, Misaki N.; Neiderhiser, Jenae M.; Harold, Gordon T.; Shaw, Daniel S.; Ganiban, Jody M.; Reiss, David; Leve, Leslie D.

2017-01-01

Maternal trauma is a complex risk factor that has been linked to adverse child outcomes, yet the mechanisms underlying this association are not well understood. This study, which included adoptive and biological families, examined the heritable and environmental mechanisms by which maternal trauma and associated depressive symptoms are linked to child internalizing and externalizing behaviors. Path analyses were used to analyze data from 541 adoptive mother–adopted child (AM–AC) dyads and 126 biological mother–biological child (BM–BC) dyads; the two family types were linked through the same biological mother. Rearing mother’s trauma was associated with child internalizing and externalizing behaviors in AM–AC and BM–BC dyads, and this association was mediated by rearing mothers’ depressive symptoms, with the exception of biological child externalizing behavior, for which biological mother trauma had a direct influence only. Significant associations between maternal trauma and child behavior in dyads that share only environment (i.e., AM–AC dyads) suggest an environmental mechanism of influence for maternal trauma. Significant associations were also observed between maternal depressive symptoms and child internalizing and externalizing behavior in dyads that were only genetically related, with no shared environment (i.e., BM–AC dyads), suggesting a heritable pathway of influence via maternal depressive symptoms. PMID:29162177

Using an adoption-biological family design to examine associations between maternal trauma, maternal depressive symptoms, and child internalizing and externalizing behaviors.

PubMed

Grabow, Aleksandria Perez; Khurana, Atika; Natsuaki, Misaki N; Neiderhiser, Jenae M; Harold, Gordon T; Shaw, Daniel S; Ganiban, Jody M; Reiss, David; Leve, Leslie D

2017-12-01

Maternal trauma is a complex risk factor that has been linked to adverse child outcomes, yet the mechanisms underlying this association are not well understood. This study, which included adoptive and biological families, examined the heritable and environmental mechanisms by which maternal trauma and associated depressive symptoms are linked to child internalizing and externalizing behaviors. Path analyses were used to analyze data from 541 adoptive mother-adopted child (AM-AC) dyads and 126 biological mother-biological child (BM-BC) dyads; the two family types were linked through the same biological mother. Rearing mother's trauma was associated with child internalizing and externalizing behaviors in AM-AC and BM-BC dyads, and this association was mediated by rearing mothers' depressive symptoms, with the exception of biological child externalizing behavior, for which biological mother trauma had a direct influence only. Significant associations between maternal trauma and child behavior in dyads that share only environment (i.e., AM-AC dyads) suggest an environmental mechanism of influence for maternal trauma. Significant associations were also observed between maternal depressive symptoms and child internalizing and externalizing behavior in dyads that were only genetically related, with no shared environment (i.e., BM-AC dyads), suggesting a heritable pathway of influence via maternal depressive symptoms.

COBRApy: COnstraints-Based Reconstruction and Analysis for Python.

PubMed

Ebrahim, Ali; Lerman, Joshua A; Palsson, Bernhard O; Hyduke, Daniel R

2013-08-08

COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. http://opencobra.sourceforge.net/

Exploring (novel) gene expression during retinoid-induced maturation and cell death of acute promyelocytic leukemia.

PubMed

Benoit, G R; Tong, J H; Balajthy, Z; Lanotte, M

2001-01-01

During recent years, reports have shown that biological responses of acute promyelocytic leukemia (APL) cells to retinoids are more complex than initially envisioned. PML-RARalpha chimeric protein disturbs various biological processes such as cell proliferation, differentiation, and apoptosis. The distinct biological programs that regulate these processes stem from specific transcriptional activation of distinct (but overlapping) sets of genes. These programs are sometimes mutually exclusive and depend on whether the signals are delivered by RAR or RXR agonists. Furthermore, evidence that retinoid nuclear signaling by retinoid, on its own, is not enough to trigger these cellular responses is rapidly accumulating. Indeed, work with NB4 cells show that the fate of APL cells treated by retinoid depends on complex signaling cross-talk. Elucidation of the sequence of events and cascades of transcriptional regulation necessary for APL cell maturation will be an additional tool with which to further improve therapy by retinoids. In this task, the classical techniques used to analyze gene expression have proved time consuming, and their yield has been limited. Global analyses of the APL cell transcriptome are needed. We review the technical approaches currently available (differential display, complementary DNA microarrays), to identify novel genes involved in the determination of cell fate.

ViSEN: methodology and software for visualization of statistical epistasis networks

PubMed Central

Hu, Ting; Chen, Yuanzhu; Kiralis, Jeff W.; Moore, Jason H.

2013-01-01

The non-linear interaction effect among multiple genetic factors, i.e. epistasis, has been recognized as a key component in understanding the underlying genetic basis of complex human diseases and phenotypic traits. Due to the statistical and computational complexity, most epistasis studies are limited to interactions with an order of two. We developed ViSEN to analyze and visualize epistatic interactions of both two-way and three-way. ViSEN not only identifies strong interactions among pairs or trios of genetic attributes, but also provides a global interaction map that shows neighborhood and clustering structures. This visualized information could be very helpful to infer the underlying genetic architecture of complex diseases and to generate plausible hypotheses for further biological validations. ViSEN is implemented in Java and freely available at https://sourceforge.net/projects/visen/. PMID:23468157

Can nutraceuticals prevent Alzheimer's disease? Potential therapeutic role of a formulation containing shilajit and complex B vitamins.

PubMed

Carrasco-Gallardo, Carlos; Farías, Gonzalo A; Fuentes, Patricio; Crespo, Fernando; Maccioni, Ricardo B

2012-11-01

Alzheimer's disease (AD) is a brain disorder displaying a prevalence and impact in constant expansion. This expansive and epidemic behavior is concerning medical and public opinion while focusing efforts on its prevention and treatment. One important strategy to prevent this brain impairment is based on dietary changes and nutritional supplements, functional foods and nutraceuticals. In this review we discuss the potential contributions of shilajit and complex B vitamins to AD prevention. We analyze the status of biological studies and present data of a clinical trial developed in patients with mild AD. Studies suggest that shilajit and its active principle fulvic acid, as well as a formula of shilajit with B complex vitamins, emerge as novel nutraceutical with potential uses against this brain disorder. Copyright © 2012 IMSS. Published by Elsevier Inc. All rights reserved.

Towards physical principles of biological evolution

NASA Astrophysics Data System (ADS)

Katsnelson, Mikhail I.; Wolf, Yuri I.; Koonin, Eugene V.

2018-03-01

Biological systems reach organizational complexity that far exceeds the complexity of any known inanimate objects. Biological entities undoubtedly obey the laws of quantum physics and statistical mechanics. However, is modern physics sufficient to adequately describe, model and explain the evolution of biological complexity? Detailed parallels have been drawn between statistical thermodynamics and the population-genetic theory of biological evolution. Based on these parallels, we outline new perspectives on biological innovation and major transitions in evolution, and introduce a biological equivalent of thermodynamic potential that reflects the innovation propensity of an evolving population. Deep analogies have been suggested to also exist between the properties of biological entities and processes, and those of frustrated states in physics, such as glasses. Such systems are characterized by frustration whereby local state with minimal free energy conflict with the global minimum, resulting in ‘emergent phenomena’. We extend such analogies by examining frustration-type phenomena, such as conflicts between different levels of selection, in biological evolution. These frustration effects appear to drive the evolution of biological complexity. We further address evolution in multidimensional fitness landscapes from the point of view of percolation theory and suggest that percolation at level above the critical threshold dictates the tree-like evolution of complex organisms. Taken together, these multiple connections between fundamental processes in physics and biology imply that construction of a meaningful physical theory of biological evolution might not be a futile effort. However, it is unrealistic to expect that such a theory can be created in one scoop; if it ever comes to being, this can only happen through integration of multiple physical models of evolutionary processes. Furthermore, the existing framework of theoretical physics is unlikely to suffice for adequate modeling of the biological level of complexity, and new developments within physics itself are likely to be required.

The effects of academic literacy instruction on engagement and conceptual understanding of biology of ninth-grade students

NASA Astrophysics Data System (ADS)

Larson, Susan C.

Academic language, discourse, vocabulary, motivation, and comprehension of complex texts and concepts are keys to learning subject-area content. The need for a disciplinary literacy approach in high school classrooms accelerates as students become increasing disengaged in school and as content complexity increases. In the present quasi-experimental mixed-method study, a ninth-grade biology unit was designed with an emphasis on promoting academic literacy skills, discourse, meaningful constructivist learning, interest development, and positive learning experiences in order to learn science content. Quantitative and qualitative analyses on a variety of measures completed by 222 students in two high schools revealed that those who received academic literacy instruction in science class performed at significantly higher levels of conceptual understanding of biology content, academic language and vocabulary use, reasoned thought, engagement, and quality of learning experience than control-group students receiving traditionally-organized instruction. Academic literacy was embedded into biology instruction to engage students in meaning-making discourses of science to promote learning. Academic literacy activities were organized according the phases of interest development to trigger and sustain interest and goal-oriented engagement throughout the unit. Specific methods included the Generative Vocabulary Matrix (GVM), scenario-based writing, and involvement in a variety of strategically-placed discourse activities to sustain or "boost" engagement for learning. Traditional instruction for the control group included teacher lecture, whole-group discussion, a conceptual organizer, and textbook reading. Theoretical foundations include flow theory, sociocultural learning theory, and interest theory. Qualitative data were obtained from field notes and participants' journals. Quantitative survey data were collected and analyzed using the Experience Sampling Method (ESM) to measure cognitive and emotional states, revealing patterns of engagement, quality of experience, and flow over the course of the instructional unit. Conceptual understanding was measured using the state persuasive writing rubric to analyze science essays in which students supported a claim with scientific evidence. The study contributes an Engagement Model of Academic Literacy for Learning (EngageALL), a Rubric for Academic Persuasive Writing (RAPW), a unique classification system for analyzing academic vocabulary, and suggestions for situated professional development around a research-based planning framework. A discussion addresses a new direction for future research that explores academic identity development.

Directed evolution and synthetic biology applications to microbial systems.

PubMed

Bassalo, Marcelo C; Liu, Rongming; Gill, Ryan T

2016-06-01

Biotechnology applications require engineering complex multi-genic traits. The lack of knowledge on the genetic basis of complex phenotypes restricts our ability to rationally engineer them. However, complex phenotypes can be engineered at the systems level, utilizing directed evolution strategies that drive whole biological systems toward desired phenotypes without requiring prior knowledge of the genetic basis of the targeted trait. Recent developments in the synthetic biology field accelerates the directed evolution cycle, facilitating engineering of increasingly complex traits in biological systems. In this review, we summarize some of the most recent advances in directed evolution and synthetic biology that allows engineering of complex traits in microbial systems. Then, we discuss applications that can be achieved through engineering at the systems level. Copyright © 2016 Elsevier Ltd. All rights reserved.

Highly Reproducible Label Free Quantitative Proteomic Analysis of RNA Polymerase Complexes*

PubMed Central

Mosley, Amber L.; Sardiu, Mihaela E.; Pattenden, Samantha G.; Workman, Jerry L.; Florens, Laurence; Washburn, Michael P.

2011-01-01

The use of quantitative proteomics methods to study protein complexes has the potential to provide in-depth information on the abundance of different protein components as well as their modification state in various cellular conditions. To interrogate protein complex quantitation using shotgun proteomic methods, we have focused on the analysis of protein complexes using label-free multidimensional protein identification technology and studied the reproducibility of biological replicates. For these studies, we focused on three highly related and essential multi-protein enzymes, RNA polymerase I, II, and III from Saccharomyces cerevisiae. We found that label-free quantitation using spectral counting is highly reproducible at the protein and peptide level when analyzing RNA polymerase I, II, and III. In addition, we show that peptide sampling does not follow a random sampling model, and we show the need for advanced computational models to predict peptide detection probabilities. In order to address these issues, we used the APEX protocol to model the expected peptide detectability based on whole cell lysate acquired using the same multidimensional protein identification technology analysis used for the protein complexes. Neither method was able to predict the peptide sampling levels that we observed using replicate multidimensional protein identification technology analyses. In addition to the analysis of the RNA polymerase complexes, our analysis provides quantitative information about several RNAP associated proteins including the RNAPII elongation factor complexes DSIF and TFIIF. Our data shows that DSIF and TFIIF are the most highly enriched RNAP accessory factors in Rpb3-TAP purifications and demonstrate our ability to measure low level associated protein abundance across biological replicates. In addition, our quantitative data supports a model in which DSIF and TFIIF interact with RNAPII in a dynamic fashion in agreement with previously published reports. PMID:21048197

Towards Cost-Effective Operational Monitoring Systems for Complex Waters: Analyzing Small-Scale Coastal Processes with Optical Transmissometry

PubMed Central

Gonçalves-Araujo, Rafael; Wiegmann, Sonja; Torrecilla, Elena; Bardaji, Raul; Röttgers, Rüdiger; Bracher, Astrid; Piera, Jaume

2017-01-01

The detection and prediction of changes in coastal ecosystems require a better understanding of the complex physical, chemical and biological interactions, which involves that observations should be performed continuously. For this reason, there is an increasing demand for small, simple and cost-effective in situ sensors to analyze complex coastal waters at a broad range of scales. In this context, this study seeks to explore the potential of beam attenuation spectra, c(λ), measured in situ with an advanced-technology optical transmissometer, for assessing temporal and spatial patterns in the complex estuarine waters of Alfacs Bay (NW Mediterranean) as a test site. In particular, the information contained in the spectral beam attenuation coefficient was assessed and linked with different biogeochemical variables. The attenuation at λ = 710 nm was used as a proxy for particle concentration, TSM, whereas a novel parameter was adopted as an optical indicator for chlorophyll a (Chl-a) concentration, based on the local maximum of c(λ) observed at the long-wavelength side of the red band Chl-a absorption peak. In addition, since coloured dissolved organic matter (CDOM) has an important influence on the beam attenuation spectral shape and complementary measurements of particle size distribution were available, the beam attenuation spectral slope was used to analyze the CDOM content. Results were successfully compared with optical and biogeochemical variables from laboratory analysis of collocated water samples, and statistically significant correlations were found between the attenuation proxies and the biogeochemical variables TSM, Chl-a and CDOM. This outcome depicted the potential of high-frequency beam attenuation measurements as a simple, continuous and cost-effective approach for rapid detection of changes and patterns in biogeochemical properties in complex coastal environments. PMID:28107539

Electrochemical Sensors for the Detection of Lead and Other Toxic Heavy Metals: The Next Generation of Personal Exposure Biomonitors

PubMed Central

Yantasee, Wassana; Lin, Yuehe; Hongsirikarn, Kitiya; Fryxell, Glen E.; Addleman, Raymond; Timchalk, Charles

2007-01-01

To support the development and implementation of biological monitoring programs, we need quantitative technologies for measuring xenobiotic exposure. Microanalytical based sensors that work with complex biomatrices such as blood, urine, or saliva are being developed and validated and will improve our ability to make definitive associations between chemical exposures and disease. Among toxic metals, lead continues to be one of the most problematic. Despite considerable efforts to identify and eliminate Pb exposure sources, this metal remains a significant health concern, particularly for young children. Ongoing research focuses on the development of portable metal analyzers that have many advantages over current available technologies, thus potentially representing the next generation of toxic metal analyzers. In this article, we highlight the development and validation of two classes of metal analyzers for the voltammetric detection of Pb, including: a) an analyzer based on flow injection analysis and anodic stripping voltammetry at a mercury-film electrode, and b) Hg-free metal analyzers employing adsorptive stripping voltammetry and novel nanostructure materials that include the self-assembled monolayers on mesoporous supports and carbon nanotubes. These sensors have been optimized to detect Pb in urine, blood, and saliva as accurately as the state-of-the-art inductively coupled plasma-mass spectrometry with high reproducibility, and sensitivity allows. These improved and portable analytical sensor platforms will facilitate our ability to conduct biological monitoring programs to understand the relationship between chemical exposure assessment and disease outcomes. PMID:18087583

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

PubMed

Srinivasulu, Yerukala Sathipati; Wang, Jyun-Rong; Hsu, Kai-Ti; Tsai, Ming-Ju; Charoenkwan, Phasit; Huang, Wen-Lin; Huang, Hui-Ling; Ho, Shinn-Ying

2015-01-01

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

PubMed Central

2015-01-01

Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. PMID:26681483

Force-induced bone growth and adaptation: A system theoretical approach to understanding bone mechanotransduction

NASA Astrophysics Data System (ADS)

Maldonado, Solvey; Findeisen, Rolf

2010-06-01

The modeling, analysis, and design of treatment therapies for bone disorders based on the paradigm of force-induced bone growth and adaptation is a challenging task. Mathematical models provide, in comparison to clinical, medical and biological approaches an structured alternative framework to understand the concurrent effects of the multiple factors involved in bone remodeling. By now, there are few mathematical models describing the appearing complex interactions. However, the resulting models are complex and difficult to analyze, due to the strong nonlinearities appearing in the equations, the wide range of variability of the states, and the uncertainties in parameters. In this work, we focus on analyzing the effects of changes in model structure and parameters/inputs variations on the overall steady state behavior using systems theoretical methods. Based on an briefly reviewed existing model that describes force-induced bone adaptation, the main objective of this work is to analyze the stationary behavior and to identify plausible treatment targets for remodeling related bone disorders. Identifying plausible targets can help in the development of optimal treatments combining both physical activity and drug-medication. Such treatments help to improve/maintain/restore bone strength, which deteriorates under bone disorder conditions, such as estrogen deficiency.

The impact of network medicine in gastroenterology and hepatology.

PubMed

Baffy, György

2013-10-01

In the footsteps of groundbreaking achievements made by biomedical research, another scientific revolution is unfolding. Systems biology draws from the chaos and complexity theory and applies computational models to predict emerging behavior of the interactions between genes, gene products, and environmental factors. Adaptation of systems biology to translational and clinical sciences has been termed network medicine, and is likely to change the way we think about preventing, predicting, diagnosing, and treating complex human diseases. Network medicine finds gene-disease associations by analyzing the unparalleled digital information discovered and created by high-throughput technologies (dubbed as "omics" science) and links genetic variance to clinical disease phenotypes through intermediate organizational levels of life such as the epigenome, transcriptome, proteome, and metabolome. Supported by large reference databases, unprecedented data storage capacity, and innovative computational analysis, network medicine is poised to find links between conditions that were thought to be distinct, uncover shared disease mechanisms and key drivers of the pathogenesis, predict individual disease outcomes and trajectories, identify novel therapeutic applications, and help avoid off-target and undesirable drug effects. Recent advances indicate that these perspectives are increasingly within our reach for understanding and managing complex diseases of the digestive system. Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.

When physics is not "just physics": complexity science invites new measurement frames for exploring the physics of cognitive and biological development.

PubMed

Kelty-Stephen, Damian; Dixon, James A

2012-01-01

The neurobiological sciences have struggled to resolve the physical foundations for biological and cognitive phenomena with a suspicion that biological and cognitive systems, capable of exhibiting and contributing to structure within themselves and through their contexts, are fundamentally distinct or autonomous from purely physical systems. Complexity science offers new physics-based approaches to explaining biological and cognitive phenomena. In response to controversy over whether complexity science might seek to "explain away" biology and cognition as "just physics," we propose that complexity science serves as an application of recent advances in physics to phenomena in biology and cognition without reducing or undermining the integrity of the phenomena to be explained. We highlight that physics is, like the neurobiological sciences, an evolving field and that the threat of reduction is overstated. We propose that distinctions between biological and cognitive systems from physical systems are pretheoretical and thus optional. We review our own work applying insights from post-classical physics regarding turbulence and fractal fluctuations to the problems of developing cognitive structure. Far from hoping to reduce biology and cognition to "nothing but" physics, we present our view that complexity science offers new explanatory frameworks for considering physical foundations of biological and cognitive phenomena.

Multilayer network modeling of integrated biological systems. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

De Domenico, Manlio

2018-03-01

Biological systems, from a cell to the human brain, are inherently complex. A powerful representation of such systems, described by an intricate web of relationships across multiple scales, is provided by complex networks. Recently, several studies are highlighting how simple networks - obtained by aggregating or neglecting temporal or categorical description of biological data - are not able to account for the richness of information characterizing biological systems. More complex models, namely multilayer networks, are needed to account for interdependencies, often varying across time, of biological interacting units within a cell, a tissue or parts of an organism.

s-core network decomposition: A generalization of k-core analysis to weighted networks

NASA Astrophysics Data System (ADS)

Eidsaa, Marius; Almaas, Eivind

2013-12-01

A broad range of systems spanning biology, technology, and social phenomena may be represented and analyzed as complex networks. Recent studies of such networks using k-core decomposition have uncovered groups of nodes that play important roles. Here, we present s-core analysis, a generalization of k-core (or k-shell) analysis to complex networks where the links have different strengths or weights. We demonstrate the s-core decomposition approach on two random networks (ER and configuration model with scale-free degree distribution) where the link weights are (i) random, (ii) correlated, and (iii) anticorrelated with the node degrees. Finally, we apply the s-core decomposition approach to the protein-interaction network of the yeast Saccharomyces cerevisiae in the context of two gene-expression experiments: oxidative stress in response to cumene hydroperoxide (CHP), and fermentation stress response (FSR). We find that the innermost s-cores are (i) different from innermost k-cores, (ii) different for the two stress conditions CHP and FSR, and (iii) enriched with proteins whose biological functions give insight into how yeast manages these specific stresses.

NUTS and BOLTS: Applications of Fluorescence Detected Sedimentation

PubMed Central

Kroe, Rachel R.; Laue, Thomas M.

2008-01-01

Analytical ultracentrifugation is a widely used method for characterizing the solution behavior of macromolecules. However, the two commonly used detectors (absorbance and interference) impose some fundamental restrictions on the concentrations and complexity of the solutions that can be analyzed. The recent addition of a fluorescence detector for the XL-I analytical ultracentrifuge (AU-FDS) enables two different types of sedimentation experiments. First, the AU-FDS can detect picomolar concentrations of labeled solutes allowing the characterization of very dilute solutions of macromolecules, applications we call Normal Use Tracer Sedimentation (NUTS). The great sensitivity of NUTS analysis allows the characterization of small quantities of materials and high affinity interactions. Second, AU-FDS allows characterization of trace quantities of labeled molecules in solutions containing high concentrations and complex mixtures of unlabeled molecules, applications we call Biological On Line Tracer Sedimentation (BOLTS). The discrimination of BOLTS enables the size distribution of a labeled macromolecule to be determined in biological milieu such as cell lysates and serum. Examples are presented that embody features of both NUTS and BOLTS applications, along with our observations on these applications. PMID:19103145

Identification of karyopherins involved in the nuclear import of RNA exosome subunit Rrp6 in Saccharomyces cerevisiae.

PubMed

Gonzales-Zubiate, Fernando A; Okuda, Ellen K; Da Cunha, Julia P C; Oliveira, Carla Columbano

2017-07-21

The exosome is a conserved multiprotein complex essential for RNA processing and degradation. The nuclear exosome is a key factor for pre-rRNA processing through the activity of its catalytic subunits, Rrp6 and Rrp44. In Saccharomyces cerevisiae , Rrp6 is exclusively nuclear and has been shown to interact with exosome cofactors. With the aim of analyzing proteins associated with the nuclear exosome, in this work, we purified the complex with Rrp6-TAP, identified the co-purified proteins by mass spectrometry, and found karyopherins to be one of the major groups of proteins enriched in the samples. By investigating the biological importance of these protein interactions, we identified Srp1, Kap95, and Sxm1 as the most important karyopherins for Rrp6 nuclear import and the nuclear localization signals recognized by them. Based on the results shown here, we propose a model of multiple pathways for the transport of Rrp6 to the nucleus. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

A molecular signaling approach to linking intraspecific variation and macro-evolutionary patterns.

PubMed

Swanson, Eli M; Snell-Rood, Emilie C

2014-11-01

Macro-evolutionary comparisons are a valued tool in evolutionary biology. Nevertheless, our understanding of how systems involved in molecular signaling change in concert with phenotypic diversification has lagged. We argue that integrating our understanding of the evolution of molecular signaling systems with phylogenetic comparative methods is an important step toward understanding the processes linking variation among individuals with variation among species. Focusing mostly on the endocrine system, we discuss how the complexity and mechanistic nature of molecular signaling systems may influence the application and interpretation of macro-evolutionary comparisons. We also detail five hypotheses concerning the role that physiological mechanisms can play in shaping macro-evolutionary patterns, and discuss ways in which these hypotheses could influence phenotypic diversification. Finally, we review a series of tools able to analyze the complexity of physiological systems and the way they change in concert with the phenotypes for which they coordinate development. © The Author 2014. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

The expanding universe of noncoding RNAs.

PubMed

Hannon, G J; Rivas, F V; Murchison, E P; Steitz, J A

2006-01-01

The 71st Cold Spring Harbor Symposium on Quantitative Biology celebrated the numerous and expanding roles of regulatory RNAs in systems ranging from bacteria to mammals. It was clearly evident that noncoding RNAs are undergoing a renaissance, with reports of their involvement in nearly every cellular process. Previously known classes of longer noncoding RNAs were shown to function by every possible means-acting catalytically, sensing physiological states through adoption of complex secondary and tertiary structures, or using their primary sequences for recognition of target sites. The many recently discovered classes of small noncoding RNAs, generally less than 35 nucleotides in length, most often exert their effects by guiding regulatory complexes to targets via base-pairing. With the ability to analyze the RNA products of the genome in ever greater depth, it has become clear that the universe of noncoding RNAs may extend far beyond the boundaries we had previously imagined. Thus, as much as the Symposium highlighted exciting progress in the field, it also revealed how much farther we must go to understand fully the biological impact of noncoding RNAs.

Specificity of molecular interactions in transient protein-protein interaction interfaces.

PubMed

Cho, Kyu-il; Lee, KiYoung; Lee, Kwang H; Kim, Dongsup; Lee, Doheon

2006-11-15

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of antibody-antigen complexes, the sign is somewhat ambiguous. From the evolutionary perspective, while protease-inhibitors and sig-naling proteins have optimized their interfaces to suit their biological functions, antibody-antigen interactions are the happenstance, implying that antibody-antigen complexes do not show distinctive interaction types. Persistent interaction types such as pi...pi, amide-carbonyl, and hydroxyl-carbonyl interaction, are also investigated. Analyzing the structural orientations of the pi...pi stacking interactions, we find that herringbone shape is a major configuration in transient protein-protein interfaces. This result is different from that of protein core, where parallel-displaced configurations are the major configuration. We also analyze overall trend of amide-carbonyl and hydroxyl-carbonyl interactions. It is noticeable that nearly 82% of the interfaces have at least one hydroxyl-carbonyl interactions. (c) 2006 Wiley-Liss, Inc.

Biologically-Inspired Concepts for Self-Management of Complexity

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, G.

2006-01-01

Inherent complexity in large-scale applications may be impossible to eliminate or even ameliorate despite a number of promising advances. In such cases, the complexity must be tolerated and managed. Such management may be beyond the abilities of humans, or require such overhead as to make management by humans unrealistic. A number of initiatives inspired by concepts in biology have arisen for self-management of complex systems. We present some ideas and techniques we have been experimenting with, inspired by lesser-known concepts in biology that show promise in protecting complex systems and represent a step towards self-management of complexity.

How do precision medicine and system biology response to human body's complex adaptability?

PubMed

Yuan, Bing

2016-12-01

In the field of life sciences, although system biology and "precision medicine" introduce some complex scientifific methods and techniques, it is still based on the "analysis-reconstruction" of reductionist theory as a whole. Adaptability of complex system increase system behaviour uncertainty as well as the difficulties of precise identifification and control. It also put systems biology research into trouble. To grasp the behaviour and characteristics of organism fundamentally, systems biology has to abandon the "analysis-reconstruction" concept. In accordance with the guidelines of complexity science, systems biology should build organism model from holistic level, just like the Chinese medicine did in dealing with human body and disease. When we study the living body from the holistic level, we will fifind the adaptability of complex system is not the obstacle that increases the diffificulty of problem solving. It is the "exceptional", "right-hand man" that helping us to deal with the complexity of life more effectively.

[Pressing problems of labor hygiene and occupational pathology among office workers].

PubMed

Dudarev, A A; Sorokin, G A

2012-01-01

Northwest public health research center, Ministry of health and social affairs, St.-Petersburg. The article substantiates the conception of "office room", "office worker", estimates the basic diseases and symptoms among office workers (SBS-syndrome, BRI-illnesses, BRS-symptoms). Complex of indoor factors of office environment are analyzed, which influence the health status of personnel--indoor air quality (microclimate, aerosols, chemical, biological pollution, air ionization), external physical factors, ergonomics, intensity and tension of work, psychosocial factors. Comparison of Russian and foreign approaches to the hygienic estimation and rating of these factors was carried out. Owing to inadequacy of Russian hygienic rules to modern requirements, the necessity of working out of a complex of sanitary rules focused particularly on office workers is proved.

The BiolAD-DB system : an informatics system for clinical and genetic data.

PubMed

Nielsen, David A; Leidner, Marty; Haynes, Chad; Krauthammer, Michael; Kreek, Mary Jeanne

2007-01-01

The Biology of Addictive Diseases-Database (BiolAD-DB) system is a research bioinformatics system for archiving, analyzing, and processing of complex clinical and genetic data. The database schema employs design principles for handling complex clinical information, such as response items in genetic questionnaires. Data access and validation is provided by the BiolAD-DB client application, which features a data validation engine tightly coupled to a graphical user interface. Data integrity is provided by the password-protected BiolAD-DB SQL compliant server and database. BiolAD-DB tools further provide functionalities for generating customized reports and views. The BiolAD-DB system schema, client, and installation instructions are freely available at http://www.rockefeller.edu/biolad-db/.

A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.

PubMed

Gregoretti, Francesco; Belcastro, Vincenzo; di Bernardo, Diego; Oliva, Gennaro

2010-04-21

The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.

Supervised Semi-Automated Data Analysis Software for Gas Chromatography / Differential Mobility Spectrometry (GC/DMS) Metabolomics Applications.

PubMed

Peirano, Daniel J; Pasamontes, Alberto; Davis, Cristina E

2016-09-01

Modern differential mobility spectrometers (DMS) produce complex and multi-dimensional data streams that allow for near-real-time or post-hoc chemical detection for a variety of applications. An active area of interest for this technology is metabolite monitoring for biological applications, and these data sets regularly have unique technical and data analysis end user requirements. While there are initial publications on how investigators have individually processed and analyzed their DMS metabolomic data, there are no user-ready commercial or open source software packages that are easily used for this purpose. We have created custom software uniquely suited to analyze gas chromatograph / differential mobility spectrometry (GC/DMS) data from biological sources. Here we explain the implementation of the software, describe the user features that are available, and provide an example of how this software functions using a previously-published data set. The software is compatible with many commercial or home-made DMS systems. Because the software is versatile, it can also potentially be used for other similarly structured data sets, such as GC/GC and other IMS modalities.

Analyzing the substitution effect on the CoMFA results within the framework of density functional theory (DFT).

PubMed

Morales-Bayuelo, Alejandro

2016-07-01

Though QSAR was originally developed in the context of physical organic chemistry, it has been applied very extensively to chemicals (drugs) which act on biological systems, in this idea one of the most important QSAR methods is the 3D QSAR model. However, due to the complexity of understanding the results it is necessary to postulate new methodologies to highlight their physical-chemical meaning. In this sense, this work postulates new insights to understand the CoMFA results using molecular quantum similarity and chemical reactivity descriptors within the framework of density functional theory. To obtain these insights a simple theoretical scheme involving quantum similarity (overlap, coulomb operators, their euclidean distances) and chemical reactivity descriptors such as chemical potential (μ), hardness (ɳ), softness (S), electrophilicity (ω), and the Fukui functions, was used to understand the substitution effect. In this sense, this methodology can be applied to analyze the biological activity and the stabilization process in the non-covalent interactions on a particular molecular set taking a reference compound.

A study on locating the sonic source of sinusoidal magneto-acoustic signals using a vector method.

PubMed

Zhang, Shunqi; Zhou, Xiaoqing; Ma, Ren; Yin, Tao; Liu, Zhipeng

2015-01-01

Methods based on the magnetic-acoustic effect are of great significance in studying the electrical imaging properties of biological tissues and currents. The continuous wave method, which is commonly used, can only detect the current amplitude without the sound source position. Although the pulse mode adopted in magneto-acoustic imaging can locate the sonic source, the low measuring accuracy and low SNR has limited its application. In this study, a vector method was used to solve and analyze the magnetic-acoustic signal based on the continuous sine wave mode. This study includes theory modeling of the vector method, simulations to the line model, and experiments with wire samples to analyze magneto-acoustic (MA) signal characteristics. The results showed that the amplitude and phase of the MA signal contained the location information of the sonic source. The amplitude and phase obeyed the vector theory in the complex plane. This study sets a foundation for a new technique to locate sonic sources for biomedical imaging of tissue conductivity. It also aids in studying biological current detecting and reconstruction based on the magneto-acoustic effect.

Identification of three critical regions within mouse interleukin 2 by fine structural deletion analysis.

PubMed Central

Zurawski, S M; Zurawski, G

1988-01-01

We have analyzed structure--function relationships of the protein hormone murine interleukin 2 by fine structural deletion mapping. A total of 130 deletion mutant proteins, together with some substitution and insertion mutant proteins, was expressed in Escherichia coli and analyzed for their ability to sustain the proliferation of a cloned murine T cell line. This analysis has permitted a functional map of the protein to be drawn and classifies five segments of the protein, which together contain 48% of the sequence, as unessential to the biological activity of the protein. A further 26% of the protein is classified as important, but not crucial, for the activity. Three regions, consisting of amino acids 32-35, 66-77 and 119-141 contain the remaining 26% of the protein and are critical to the biological activity of the protein. The functional map is discussed in the context of the possible role of the identified critical regions in the structure of the hormone and its binding to the interleukin 2 receptor complex. Images PMID:3261239

openBIS: a flexible framework for managing and analyzing complex data in biology research

PubMed Central

2011-01-01

Background Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it. Results We have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies. Conclusions openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain. PMID:22151573

Mesoscale modeling: solving complex flows in biology and biotechnology.

PubMed

Mills, Zachary Grant; Mao, Wenbin; Alexeev, Alexander

2013-07-01

Fluids are involved in practically all physiological activities of living organisms. However, biological and biorelated flows are hard to analyze due to the inherent combination of interdependent effects and processes that occur on a multitude of spatial and temporal scales. Recent advances in mesoscale simulations enable researchers to tackle problems that are central for the understanding of such flows. Furthermore, computational modeling effectively facilitates the development of novel therapeutic approaches. Among other methods, dissipative particle dynamics and the lattice Boltzmann method have become increasingly popular during recent years due to their ability to solve a large variety of problems. In this review, we discuss recent applications of these mesoscale methods to several fluid-related problems in medicine, bioengineering, and biotechnology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

PubMed Central

Lex, Alexander; Partl, Christian; Kalkofen, Denis; Streit, Marc; Gratzl, Samuel; Wassermann, Anne Mai; Schmalstieg, Dieter; Pfister, Hanspeter

2014-01-01

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types. Fig. 1Entourage showing the Glioma pathway in detail and contextual information of multiple related pathways. PMID:24051820

In search of the Golden Fleece: Unraveling principles of morphogenesis by studying the integrative biology of skin appendages

PubMed Central

Hughes, Michael W.; Wu, Ping; Jiang, Ting-Xin; Lin, Sung-Jan; Dong, Chen-Yuan; Li, Ang; Hsieh, Fon-Jou; Widelitz, Randall B.; Choung, Cheng Ming

2013-01-01

Summary The mythological story of the Golden Fleece symbolizes the magical regenerative power of skin appendages. Similar to the adventurous pursuit of the Golden Fleece by the multi-talented Argonauts, today we also need an integrated multi-disciplined approach to understand the cellular and molecular processes during development, regeneration and evolution of skin appendages. To this end, we have explored several aspects of skin appendage biology that contribute to the Turing activator / inhibitor model in feather pattern formation, the topo-biological arrangement of stem cells in organ shape determination, the macro-environmental regulation of stem cells in regenerative hair waves, and potential novel molecular pathways in the morphological evolution of feathers. Here we show our current integrative biology efforts to unravel the complex cellular behavior in patterning stem cells and the control of regional specificity in skin appendages. We use feather / scale tissue recombination to demonstrate the timing control of competence and inducibility. Feathers from different body regions are used to study skin regional specificity. Bioinformatic analyses of transcriptome microarrays show the potential involvement of candidate molecular pathways. We further show Hox genes exhibit some region specific expression patterns. To visualize real time events, we applied time-lapse movies, confocal microscopy and multiphoton microscopy to analyze the morphogenesis of cultured embryonic chicken skin explants. These modern imaging technologies reveal unexpectedly complex cellular flow and organization of extracellular matrix molecules in three dimensions. While these approaches are in preliminary stages, this perspective highlights the challenges we face and new integrative tools we will use. Future work will follow these leads to develop a systems biology view and understanding in the morphogenetic principles that govern the development and regeneration of ectodermal organs. PMID:21437328

Loads Bias Genetic and Signaling Switches in Synthetic and Natural Systems

PubMed Central

Medford, June; Prasad, Ashok

2014-01-01

Biological protein interactions networks such as signal transduction or gene transcription networks are often treated as modular, allowing motifs to be analyzed in isolation from the rest of the network. Modularity is also a key assumption in synthetic biology, where it is similarly expected that when network motifs are combined together, they do not lose their essential characteristics. However, the interactions that a network module has with downstream elements change the dynamical equations describing the upstream module and thus may change the dynamic and static properties of the upstream circuit even without explicit feedback. In this work we analyze the behavior of a ubiquitous motif in gene transcription and signal transduction circuits: the switch. We show that adding an additional downstream component to the simple genetic toggle switch changes its dynamical properties by changing the underlying potential energy landscape, and skewing it in favor of the unloaded side, and in some situations adding loads to the genetic switch can also abrogate bistable behavior. We find that an additional positive feedback motif found in naturally occurring toggle switches could tune the potential energy landscape in a desirable manner. We also analyze autocatalytic signal transduction switches and show that a ubiquitous positive feedback switch can lose its switch-like properties when connected to a downstream load. Our analysis underscores the necessity of incorporating the effects of downstream components when understanding the physics of biochemical network motifs, and raises the question as to how these effects are managed in real biological systems. This analysis is particularly important when scaling synthetic networks to more complex organisms. PMID:24676102

Exploratory Visual Analysis of Statistical Results from Microarray Experiments Comparing High and Low Grade Glioma

PubMed Central

Reif, David M.; Israel, Mark A.; Moore, Jason H.

2007-01-01

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org. PMID:19390666

Complexities, Catastrophes and Cities: Emergency Dynamics in Varying Scenarios and Urban Topologies

NASA Astrophysics Data System (ADS)

Narzisi, Giuseppe; Mysore, Venkatesh; Byeon, Jeewoong; Mishra, Bud

Complex Systems are often characterized by agents capable of interacting with each other dynamically, often in non-linear and non-intuitive ways. Trying to characterize their dynamics often results in partial differential equations that are difficult, if not impossible, to solve. A large city or a city-state is an example of such an evolving and self-organizing complex environment that efficiently adapts to different and numerous incremental changes to its social, cultural and technological infrastructure [1]. One powerful technique for analyzing such complex systems is Agent-Based Modeling (ABM) [9], which has seen an increasing number of applications in social science, economics and also biology. The agent-based paradigm facilitates easier transfer of domain specific knowledge into a model. ABM provides a natural way to describe systems in which the overall dynamics can be described as the result of the behavior of populations of autonomous components: agents, with a fixed set of rules based on local information and possible central control. As part of the NYU Center for Catastrophe Preparedness and Response (CCPR1), we have been exploring how ABM can serve as a powerful simulation technique for analyzing large-scale urban disasters. The central problem in Disaster Management is that it is not immediately apparent whether the current emergency plans are robust against such sudden, rare and punctuated catastrophic events.

Relation extraction for biological pathway construction using node2vec.

PubMed

Kim, Munui; Baek, Seung Han; Song, Min

2018-06-13

Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically. In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. To this end, we extracted genes from paper abstracts using pkde4j, a text-mining tool for detecting entities and relationships. Using the extracted genes, a co-occurrence network was constructed and node2vec was used with the network to generate a latent representation. To demonstrate the efficacy of node2vec in extracting relationships between genes, performance was evaluated for gene-gene interactions involved in a type 2 diabetes pathway. Moreover, we compared the results of node2vec to those of baseline methods such as co-occurrence and DeepWalk. Node2vec outperformed existing methods in detecting relationships in the type 2 diabetes pathway, demonstrating that this method is appropriate for capturing the relatedness between pairs of biological entities involved in biological pathways. The results demonstrated that node2vec is useful for automatic pathway construction.

Systems medicine: a new approach to clinical practice.

PubMed

Cardinal-Fernández, Pablo; Nin, Nicolás; Ruíz-Cabello, Jesús; Lorente, José A

2014-10-01

Most respiratory diseases are considered complex diseases as their susceptibility and outcomes are determined by the interaction between host-dependent factors (genetic factors, comorbidities, etc.) and environmental factors (exposure to microorganisms or allergens, treatments received, etc.) The reductionist approach in the study of diseases has been of fundamental importance for the understanding of the different components of a system. Systems biology or systems medicine is a complementary approach aimed at analyzing the interactions between the different components within one organizational level (genome, transcriptome, proteome), and then between the different levels. Systems medicine is currently used for the interpretation and understanding of the pathogenesis and pathophysiology of different diseases, biomarker discovery, design of innovative therapeutic targets, and the drawing up of computational models for different biological processes. In this review we discuss the most relevant concepts of the theory underlying systems medicine, as well as its applications in the various biological processes in humans. Copyright © 2013 SEPAR. Published by Elsevier Espana. All rights reserved.

Direct analysis of terpenes from biological buffer systems using SESI and IR-MALDESI.

PubMed

Nazari, Milad; Malico, Alexandra A; Ekelöf, Måns; Lund, Sean; Williams, Gavin J; Muddiman, David C

2018-01-01

Terpenes are the largest class of natural products with a wide range of applications including use as pharmaceuticals, fragrances, flavorings, and agricultural products. Terpenes are biosynthesized by the condensation of a variable number of isoprene units resulting in linear polyisoprene diphosphate units, which can then be cyclized by terpene synthases into a range of complex structures. While these cyclic structures have immense diversity and potential in different applications, their direct analysis in biological buffer systems requires intensive sample preparation steps such as salt cleanup, extraction with organic solvents, and chromatographic separations. Electrospray post-ionization can be used to circumvent many sample cleanup and desalting steps. SESI and IR-MALDESI are two examples of ionization methods that employ electrospray post-ionization at atmospheric pressure and temperature. By coupling the two techniques and doping the electrospray solvent with silver ions, olefinic terpenes of different classes and varying degrees of volatility were directly analyzed from a biological buffer system with no sample workup steps.

Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces.

PubMed

Rodrigo, Daniel; Tittl, Andreas; Ait-Bouziad, Nadine; John-Herpin, Aurelian; Limaj, Odeta; Kelly, Christopher; Yoo, Daehan; Wittenberg, Nathan J; Oh, Sang-Hyun; Lashuel, Hilal A; Altug, Hatice

2018-06-04

A multitude of biological processes are enabled by complex interactions between lipid membranes and proteins. To understand such dynamic processes, it is crucial to differentiate the constituent biomolecular species and track their individual time evolution without invasive labels. Here, we present a label-free mid-infrared biosensor capable of distinguishing multiple analytes in heterogeneous biological samples with high sensitivity. Our technology leverages a multi-resonant metasurface to simultaneously enhance the different vibrational fingerprints of multiple biomolecules. By providing up to 1000-fold near-field intensity enhancement over both amide and methylene bands, our sensor resolves the interactions of lipid membranes with different polypeptides in real time. Significantly, we demonstrate that our label-free chemically specific sensor can analyze peptide-induced neurotransmitter cargo release from synaptic vesicle mimics. Our sensor opens up exciting possibilities for gaining new insights into biological processes such as signaling or transport in basic research as well as provides a valuable toolkit for bioanalytical and pharmaceutical applications.

Programming Light-Harvesting Efficiency Using DNA Origami

PubMed Central

2016-01-01

The remarkable performance and quantum efficiency of biological light-harvesting complexes has prompted a multidisciplinary interest in engineering biologically inspired antenna systems as a possible route to novel solar cell technologies. Key to the effectiveness of biological “nanomachines” in light capture and energy transport is their highly ordered nanoscale architecture of photoactive molecules. Recently, DNA origami has emerged as a powerful tool for organizing multiple chromophores with base-pair accuracy and full geometric freedom. Here, we present a programmable antenna array on a DNA origami platform that enables the implementation of rationally designed antenna structures. We systematically analyze the light-harvesting efficiency with respect to number of donors and interdye distances of a ring-like antenna using ensemble and single-molecule fluorescence spectroscopy and detailed Förster modeling. This comprehensive study demonstrates exquisite and reliable structural control over multichromophoric geometries and points to DNA origami as highly versatile platform for testing design concepts in artificial light-harvesting networks. PMID:26906456

HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data.

PubMed

Trivedi, Prinal; Edwards, Jode W; Wang, Jelai; Gadbury, Gary L; Srinivasasainagendra, Vinodh; Zakharkin, Stanislav O; Kim, Kyoungmi; Mehta, Tapan; Brand, Jacob P L; Patki, Amit; Page, Grier P; Allison, David B

2005-04-06

Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

[Big Data Revolution or Data Hubris? : On the Data Positivism of Molecular Biology].

PubMed

Gramelsberger, Gabriele

2017-12-01

Genome data, the core of the 2008 proclaimed big data revolution in biology, are automatically generated and analyzed. The transition from the manual laboratory practice of electrophoresis sequencing to automated DNA-sequencing machines and software-based analysis programs was completed between 1982 and 1992. This transition facilitated the first data deluge, which was considerably increased by the second and third generation of DNA-sequencers during the 2000s. However, the strategies for evaluating sequence data were also transformed along with this transition. The paper explores both the computational strategies of automation, as well as the data evaluation culture connected with it, in order to provide a complete picture of the complexity of today's data generation and its intrinsic data positivism. This paper is thereby guided by the question, whether this data positivism is the basis of the big data revolution of molecular biology announced today, or it marks the beginning of its data hubris.

Interaction of bombesin and its fragments with gold nanoparticles analyzed using surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Tąta, Agnieszka; Szkudlarek, Aleksandra; Kim, Younkyoo; Proniewicz, Edyta

2017-02-01

This work demonstrates the application of commercially available stable surface composed of gold nanograins with diameters ranging from 70 to 226 nm deposited onto silicon wafer for surface-enhanced Raman scattering investigations of biologically active compounds, such as bombesin (BN) and its fragments. BN is an important neurotransmitter involved in a complex signaling pathways and biological responses; for instance, hypertensive action, contractive on uterus, colon or ileum, locomotor activity, stimulation of gastric and insulin secretion as well as growth promotion of various tumor cell lines, including: lung, prostate, stomach, colon, and breast. It has also been shown that 8-14 BN C-terminal fragment partially retains the biological activity of BN. The SERS results for BN and its fragment demonstrated that (1) three amino acids from these peptides sequence; i.e., L-histidine, L-methionine, and L-tryptophan, are involved in the interaction with gold coated silicon wafer and (2) the strength of these interactions depends upon the aforementioned amino acids position in the peptide sequence.

GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models.

PubMed

Ligon, Thomas S; Fröhlich, Fabian; Chis, Oana T; Banga, Julio R; Balsa-Canto, Eva; Hasenauer, Jan

2018-04-15

Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed. We introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models. GenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI. thomas.ligon@physik.uni-muenchen.de or jan.hasenauer@helmholtz-muenchen.de. Supplementary data are available at Bioinformatics online.

Association of a Platinum Complex to a G-Quadruplex Ligand Enhances Telomere Disruption.

PubMed

Charif, Razan; Granotier-Beckers, Christine; Bertrand, Hélène Charlotte; Poupon, Joël; Ségal-Bendirdjian, Evelyne; Teulade-Fichou, Marie-Paule; Boussin, François D; Bombard, Sophie

2017-08-21

Telomeres protect the ends of chromosomes against illegitimate recombination and repair. They can be targets for G-quadruplex ligands and platinum complexes due to their repeated G-rich sequences. Protection of telomeres is ensured by a complex of six proteins, including TRF2, which inhibits the DNA damage response pathway. We analyzed telomere modifications induced in cancer cells by the experimental hybrid platinum complex, Pt-MPQ, comprising both an ethylene diamine monofunctional platinum complex and a G-quadruplex recognition moiety (MPQ). Pt-MPQ promotes the displacement of two telomeric proteins (TRF2 and TRF1) from telomeres, as well as the formation of telomere damage and telomere sister losses, whereas the control compound MPQ does not. This suggests that the platinum moiety potentiates the targeting of the G-quadruplex ligand to telomeres, opening a new perspective for telomere biology and anticancer therapy. Interestingly, the chemotherapy drug cisplatin, which has no specific affinity for G-quadruplex structures, partially induces the TRF2 delocalization from telomeres but produces less telomeric DNA damage, suggesting that this TRF2 displacement could be independent of G-quadruplex recognition.

Reactivity and biological properties of a series of cytotoxic PtI2(amine)2 complexes, either cis or trans configured.

PubMed

Messori, Luigi; Cubo, Leticia; Gabbiani, Chiara; Álvarez-Valdés, Amparo; Michelucci, Elena; Pieraccini, Giuseppe; Ríos-Luci, Carla; León, Leticia G; Padrón, José M; Navarro-Ranninger, Carmen; Casini, Angela; Quiroga, Adoración G

2012-02-06

Six diiodido-diamine platinum(II) complexes, either cis or trans configured, were prepared, differing only in the nature of the amine ligand (isopropylamine, dimethylamine, or methylamine), and their antiproliferative properties were evaluated against a panel of human tumor cell lines. Both series of complexes manifested pronounced cytotoxic effects, with the trans isomers being, generally, more effective than their cis counterparts. Cell cycle analysis revealed different modes of action for these new Pt(II) complexes with respect to cisplatin. The reactivity of these platinum compounds with a number of biomolecules, including cytochrome c, two sulfur containing modified amino acids, 9-ethylguanine, and a single strand oligonucleotide, was analyzed in depth by mass spectrometry and NMR spectroscopy. Interestingly, significant differences in the reactivity of the investigated compounds toward the various model biomolecules were observed: in particular we observed that trans complexes preferentially release their iodide ligands upon biomolecule binding, while the cis isomers may release the amine ligands with retention of iodides. Such differences in reactivity may have important mechanistic implications and a relevant impact on the respective pharmacological profiles.

Dynamical complexity detection in geomagnetic activity indices using wavelet transforms and Tsallis entropy

NASA Astrophysics Data System (ADS)

Balasis, G.; Daglis, I. A.; Papadimitriou, C.; Kalimeri, M.; Anastasiadis, A.; Eftaxias, K.

2008-12-01

Dynamical complexity detection for output time series of complex systems is one of the foremost problems in physics, biology, engineering, and economic sciences. Especially in magnetospheric physics, accurate detection of the dissimilarity between normal and abnormal states (e.g. pre-storm activity and magnetic storms) can vastly improve space weather diagnosis and, consequently, the mitigation of space weather hazards. Herein, we examine the fractal spectral properties of the Dst data using a wavelet analysis technique. We show that distinct changes in associated scaling parameters occur (i.e., transition from anti- persistent to persistent behavior) as an intense magnetic storm approaches. We then analyze Dst time series by introducing the non-extensive Tsallis entropy, Sq, as an appropriate complexity measure. The Tsallis entropy sensitively shows the complexity dissimilarity among different "physiological" (normal) and "pathological" states (intense magnetic storms). The Tsallis entropy implies the emergence of two distinct patterns: (i) a pattern associated with the intense magnetic storms, which is characterized by a higher degree of organization, and (ii) a pattern associated with normal periods, which is characterized by a lower degree of organization.

NNAlign: A Web-Based Prediction Method Allowing Non-Expert End-User Discovery of Sequence Motifs in Quantitative Peptide Data

PubMed Central

Andreatta, Massimo; Schafer-Nielsen, Claus; Lund, Ole; Buus, Søren; Nielsen, Morten

2011-01-01

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points. NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign. PMID:22073191

NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data.

PubMed

Andreatta, Massimo; Schafer-Nielsen, Claus; Lund, Ole; Buus, Søren; Nielsen, Morten

2011-01-01

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new "omics"-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points. NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign.

Bayesian spatiotemporal analysis of zero-inflated biological population density data by a delta-normal spatiotemporal additive model.

PubMed

Arcuti, Simona; Pollice, Alessio; Ribecco, Nunziata; D'Onghia, Gianfranco

2016-03-01

We evaluate the spatiotemporal changes in the density of a particular species of crustacean known as deep-water rose shrimp, Parapenaeus longirostris, based on biological sample data collected during trawl surveys carried out from 1995 to 2006 as part of the international project MEDITS (MEDiterranean International Trawl Surveys). As is the case for many biological variables, density data are continuous and characterized by unusually large amounts of zeros, accompanied by a skewed distribution of the remaining values. Here we analyze the normalized density data by a Bayesian delta-normal semiparametric additive model including the effects of covariates, using penalized regression with low-rank thin-plate splines for nonlinear spatial and temporal effects. Modeling the zero and nonzero values by two joint processes, as we propose in this work, allows to obtain great flexibility and easily handling of complex likelihood functions, avoiding inaccurate statistical inferences due to misclassification of the high proportion of exact zeros in the model. Bayesian model estimation is obtained by Markov chain Monte Carlo simulations, suitably specifying the complex likelihood function of the zero-inflated density data. The study highlights relevant nonlinear spatial and temporal effects and the influence of the annual Mediterranean oscillations index and of the sea surface temperature on the distribution of the deep-water rose shrimp density. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[New materia medica project: synthetic biology based bioactive metabolites research in medicinal plant].

PubMed

Wang, Yong

2017-03-25

In the last decade, synthetic biology research has been gradually transited from monocellular parts or devices toward more complex multicellular systems. The emerging plant synthetic biology is regarded as the "next chapter" of synthetic biology. The complex and diverse plant metabolism as the entry point, plant synthetic biology research not only helps us understand how real life is working, but also facilitates us to learn how to design and construct more complex artificial life. Bioactive compounds innovation and large-scale production are expected to be breakthrough with the redesigned plant metabolism as well. In this review, we discuss the research progress in plant synthetic biology and propose the new materia medica project to lift the level of traditional Chinese herbal medicine research.

An analysis of the Petri net based model of the human body iron homeostasis process.

PubMed

Sackmann, Andrea; Formanowicz, Dorota; Formanowicz, Piotr; Koch, Ina; Blazewicz, Jacek

2007-02-01

In the paper a Petri net based model of the human body iron homeostasis is presented and analyzed. The body iron homeostasis is an important but not fully understood complex process. The modeling of the process presented in the paper is expressed in the language of Petri net theory. An application of this theory to the description of biological processes allows for very precise analysis of the resulting models. Here, such an analysis of the body iron homeostasis model from a mathematical point of view is given.

Orthotopic Patient-Derived Glioblastoma Xenografts in Mice.

PubMed

Xu, Zhongye; Kader, Michael; Sen, Rajeev; Placantonakis, Dimitris G

2018-01-01

Patient-derived xenografts (PDX) provide in vivo glioblastoma (GBM) models that recapitulate actual tumors. Orthotopic tumor xenografts within the mouse brain are obtained by injection of GBM stem-like cells derived from fresh surgical specimens. These xenografts reproduce GBM's histologic complexity and hallmark biological behaviors, such as brain invasion, angiogenesis, and resistance to therapy. This method has become essential for analyzing mechanisms of tumorigenesis and testing the therapeutic effect of candidate agents in the preclinical setting. Here, we describe a protocol for establishing orthotopic tumor xenografts in the mouse brain with human GBM cells.

Identification of Phosphorylated Proteins on a Global Scale.

PubMed

Iliuk, Anton

2018-05-31

Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) has enabled researchers to analyze complex biological samples with unprecedented depth. It facilitates the identification and quantification of modifications within thousands of proteins in a single large-scale proteomic experiment. Analysis of phosphorylation, one of the most common and important post-translational modifications, has particularly benefited from such progress in the field. Here, detailed protocols are provided for a few well-regarded, common sample preparation methods for an effective phosphoproteomic experiment. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

Feedback systems for nontraditional medicines: a case for the signal flow diagram.

PubMed

Tice, B S

1998-11-01

The signal flow diagram is a graphic method used to represent complex data that is found in the field of biology and hence the field of medicine. The signal flow diagram is analyzed against a table of data and a flow chart of data and evaluated on the clarity and simplicity of imparting this information. The data modeled is from previous clinical studies and nontraditional medicine from Africa, China, and South America. This report is a development from previous presentations of the signal flow diagram.1-4

Modularization of biochemical networks based on classification of Petri net t-invariants.

PubMed

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-02-08

Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.

Modularization of biochemical networks based on classification of Petri net t-invariants

PubMed Central

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-01-01

Background Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior. With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Methods Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. Results We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. Conclusion We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis. PMID:18257938

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data.

PubMed

Vella, Danila; Zoppis, Italo; Mauri, Giancarlo; Mauri, Pierluigi; Di Silvestre, Dario

2017-12-01

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

Genome Scale Modeling in Systems Biology: Algorithms and Resources

PubMed Central

Najafi, Ali; Bidkhori, Gholamreza; Bozorgmehr, Joseph H.; Koch, Ina; Masoudi-Nejad, Ali

2014-01-01

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics. PMID:24822031

Analysis of a dynamic model of guard cell signaling reveals the stability of signal propagation

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, RéKa

Analyzing the long-term behaviors (attractors) of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability. We identified the long-term behaviors of a multi-level, 70-node discrete dynamic model of the stomatal opening process in plants. We reduce the model's huge state space by reducing unregulated nodes and simple mediator nodes, and by simplifying the regulatory functions of selected nodes while keeping the model consistent with experimental observations. We perform attractor analysis on the resulting 32-node reduced model by two methods: 1. converting it into a Boolean model, then applying two attractor-finding algorithms; 2. theoretical analysis of the regulatory functions. We conclude that all nodes except two in the reduced model have a single attractor; and only two nodes can admit oscillations. The multistability or oscillations do not affect the stomatal opening level in any situation. This conclusion applies to the original model as well in all the biologically meaningful cases. We further demonstrate the robustness of signal propagation by showing that a large percentage of single-node knockouts does not affect the stomatal opening level. Thus, we conclude that the complex structure of this signal transduction network provides multiple information propagation pathways while not allowing extensive multistability or oscillations, resulting in robust signal propagation. Our innovative combination of methods offers a promising way to analyze multi-level models.

Luminescent Rhenium(I) and Iridium(III) Polypyridine Complexes as Biological Probes, Imaging Reagents, and Photocytotoxic Agents.

PubMed

Lo, Kenneth Kam-Wing

2015-12-15

Although the interactions of transition metal complexes with biological molecules have been extensively studied, the use of luminescent transition metal complexes as intracellular sensors and bioimaging reagents has not been a focus of research until recently. The main advantages of luminescent transition metal complexes are their high photostability, long-lived phosphorescence that allows time-resolved detection, and large Stokes shifts that can minimize the possible self-quenching effect. Also, by the use of transition metal complexes, the degree of cellular uptake can be readily determined using inductively coupled plasma mass spectrometry. For more than a decade, we have been interested in the development of luminescent transition metal complexes as covalent labels and noncovalent probes for biological molecules. We argue that many transition metal polypyridine complexes display triplet charge transfer ((3)CT) emission that is highly sensitive to the local environment of the complexes. Hence, the biological labeling and binding interactions can be readily reflected by changes in the photophysical properties of the complexes. In this laboratory, we have modified luminescent tricarbonylrhenium(I) and bis-cyclometalated iridium(III) polypyridine complexes of general formula [Re(bpy-R(1))(CO)3(py-R(2))](+) and [Ir(ppy-R(3))2(bpy-R(4))](+), respectively, with reactive functional groups and used them to label the amine and sulfhydryl groups of biomolecules such as oligonucleotides, amino acids, peptides, and proteins. Additionally, using a range of biological substrates such as biotin, estradiol, and indole, we have designed luminescent rhenium(I) and iridium(III) polypyridine complexes as noncovalent probes for biological receptors. The interesting results generated from these studies have prompted us to investigate the possible applications of luminescent transition metal complexes in intracellular systems. Thus, in the past few years, we have developed an interest in the cytotoxic activity, cellular uptake, and bioimaging applications of these complexes. Additionally, we and other research groups have demonstrated that many transition metal complexes have facile cellular uptake and organelle-localization properties and that their cytotoxic activity can be readily controlled. For example, complexes that can target the nucleus, nucleolus, mitochondria, lysosomes, endoplasmic reticulum, and Golgi apparatus have been identified. We anticipate that this selective localization property can be utilized in the development of intracellular sensors and bioimaging reagents. Thus, we have functionalized luminescent rhenium(I) and iridium(III) polypyridine complexes with various pendants, including molecule-binding moieties, sugar molecules, bioorthogonal functional groups, and polymeric chains such as poly(ethylene glycol) and polyethylenimine, and examined their potentials as biological reagents. This Account describes our design of luminescent rhenium(I) and iridium(III) polypyridine complexes and explains how they can serve as a new generation of biological reagents for diagnostic and therapeutic applications.

Analysis of undergraduate cell biology contents in Brazilian public universities.

PubMed

Mermelstein, Claudia; Costa, Manoel Luis

2017-04-01

The enormous amount of information available in cell biology has created a challenge in selecting the core concepts we should be teaching our undergraduates. One way to define a set of essential core ideas in cell biology is to analyze what a specific cell biology community is teaching their students. Our main objective was to analyze the cell biology content currently being taught in Brazilian universities. We collected the syllabi of cell biology courses from public universities in Brazil and analyzed the frequency of cell biology topics in each course. We also compared the Brazilian data with the contents of a major cell biology textbook. Our analysis showed that while some cell biology topics such as plasma membrane and cytoskeleton was present in ∼100% of the Brazilian curricula analyzed others such as cell signaling and cell differentiation were present in only ∼35%. The average cell biology content taught in the Brazilian universities is quite different from what is presented in the textbook. We discuss several possible explanations for these observations. We also suggest a list with essential cell biology topics for any biological or biomedical undergraduate course. The comparative discussion of cell biology topics presented here could be valuable in other educational contexts. © 2017 The Authors. Cell Biology International Published by John Wiley & Sons Ltd on behalf of International Federation of Cell Biology.

Network dynamics and systems biology

NASA Astrophysics Data System (ADS)

Norrell, Johannes A.

The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior. In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects---an asymmetry between on and off states, and a decaying memory of events in each element's inputs---that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors. Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the transition point, called critical, exhibit many of the features of regulatory networks, and recent studies suggest that some specific regulatory networks are indeed near-critical. We ask whether certain statistical measures of the ensemble behavior of large continuous networks are reproduced by Boolean models. We find that, in spite of the lack of correspondence between attractors observed in smaller systems, the statistical characterization given by the continuous and Boolean models show close agreement, and the transition between order and disorder known in Boolean systems can occur in continuous systems as well. One effect that is not present in Boolean systems, the failure of information to propagate down chains of elements of arbitrary length, is present in a class of continuous networks. In these systems, a modified Boolean theory that takes into account the collective effect of propagation failure on chains throughout the network gives a good description of the observed behavior. We find that propagation failure pushes the system toward greater order, resulting in a partial or complete suppression of the disordered phase. Finally, we explore a dynamical process of direct biological relevance: asymmetric cell division in A. thaliana. The long term goal is to develop a model for the process that accurately accounts for both wild type and mutant behavior. To contribute to this endeavor, we use confocal microscopy to image roots in a SHORT-ROOT inducible mutant. We compute correlation functions between the locations of asymmetrically divided cells, and we construct stochastic models based on a few simple assumptions that accurately predict the non-zero correlations. Our result shows that intracellular processes alone cannot be responsible for the observed divisions, and that an intercell signaling mechanism could account for the measured correlations.

Effects of conformational ordering on protein/polyelectrolyte electrostatic complexation: ionic binding and chain stiffening

PubMed Central

Cao, Yiping; Fang, Yapeng; Nishinari, Katsuyoshi; Phillips, Glyn O.

2016-01-01

Coupling of electrostatic complexation with conformational transition is rather general in protein/polyelectrolyte interaction and has important implications in many biological processes and practical applications. This work studied the electrostatic complexation between κ-carrageenan (κ-car) and type B gelatin, and analyzed the effects of the conformational ordering of κ-car induced upon cooling in the presence of potassium chloride (KCl) or tetramethylammonium iodide (Me4NI). Experimental results showed that the effects of conformational ordering on protein/polyelectrolyte electrostatic complexation can be decomposed into ionic binding and chain stiffening. At the initial stage of conformational ordering, electrostatic complexation can be either suppressed or enhanced due to the ionic bindings of K+ and I− ions, which significantly alter the charge density of κ-car or occupy the binding sites of gelatin. Beyond a certain stage of conformational ordering, i.e., helix content θ > 0.30, the effect of chain stiffening, accompanied with a rapid increase in helix length ζ, becomes dominant and tends to dissociate the electrostatic complexation. The effect of chain stiffening can be theoretically interpreted in terms of double helix association. PMID:27030165

Are 1,4- and 1,5-disubstituted 1,2,3-triazoles good pharmacophoric groups?

PubMed

Massarotti, Alberto; Aprile, Silvio; Mercalli, Valentina; Del Grosso, Erika; Grosa, Giorgio; Sorba, Giovanni; Tron, Gian Cesare

2014-11-01

Over the last decade, 1,2,3-triazoles have received increasing attention in medicinal chemistry thanks to the discovery of the highly useful and widely applicable 1,3-dipolar cycloaddition reaction between azides and alkynes (click chemistry) catalyzed by copper salts and ruthenium complexes. After a decade of medicinal chemistry research on 1,2,3-triazoles, we feel that the time is ripe to demonstrate the real ability of this heterocycle to participate in important and pivotal binding interactions with biological targets while maintaining a good pharmacokinetic profile. In this study, we retrieved and analyzed X-ray crystal structures of complexes between 1,2,3-triazoles and either proteins or DNA to understand the pharmacophoric role of the triazole. Furthermore, the metabolic stability, the capacity to inhibit cytochromes, and the contribution of 1,2,3-triazoles to the overall aqueous solubility of compounds containing them have been analyzed. This information should furnish fresh insight for medicinal chemists in the design of novel bioactive molecules that contain the triazole nucleus. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Post-flight Analysis of the Argon Filled Ion Chamber

NASA Technical Reports Server (NTRS)

Tai, H.; Goldhagen, P.; Jones, I. W.; Wilson, J. W.; Maiden, D. L.; Shinn, J. L.

2003-01-01

Atmospheric ionizing radiation is a complex mixture of primary galactic and solar cosmic rays and a multitude of secondary particles produced in collision with air nuclei. The first series of Atmospheric Ionizing Radiation (AIR) measurement flights on the NASA research aircraft ER-2 took place in June 1997. The ER-2 flight package consisted of fifteen instruments from six countries and were chosen to provide varying sensitivity to specific components. These AIR ER-2 flight measurements are to characterize the AIR environment during solar minimum to allow the continued development of environmental models of this complex mixture of ionizing radiation. This will enable scientists to study the ionizing radiation health hazard associated with the high-altitude operation of a commercial supersonic transport and to allow estimates of single event upsets for advanced avionics systems design. The argon filled ion chamber representing about 40 percent of the contributions to radiation risks are analyzed herein and model discrepancies for solar minimum environment are on the order of 5 percent and less. Other biologically significant components remain to be analyzed.

Structural and functional similarities between osmotin from Nicotiana tabacum seeds and human adiponectin.

PubMed

Miele, Marco; Costantini, Susan; Colonna, Giovanni

2011-02-02

Osmotin, a plant protein, specifically binds a seven transmembrane domain receptor-like protein to exert its biological activity via a RAS2/cAMP signaling pathway. The receptor protein is encoded in the gene ORE20/PHO36 and the mammalian homolog of PHO36 is a receptor for the human hormone adiponectin (ADIPOR1). Moreover it is known that the osmotin domain I can be overlapped to the β-barrel domain of adiponectin. Therefore, these observations and some already existing structural and biological data open a window on a possible use of the osmotin or of its derivative as adiponectin agonist. We have modelled the three-dimensional structure of the adiponectin trimer (ADIPOQ), and two ADIPOR1 and PHO36 receptors. Moreover, we have also modelled the following complexes: ADIPOQ/ADIPOR1, osmotin/PHO36 and osmotin/ADIPOR1. We have then shown the structural determinants of these interactions and their physico-chemical features and analyzed the related interaction residues involved in the formation of the complexes. The stability of the modelled structures and their complexes was always evaluated and controlled by molecular dynamics. On the basis of these results a 9 residues osmotin peptide was selected and its interaction with ADIPOR1 and PHO36 was modelled and analysed in term of energetic stability by molecular dynamics. To confirm in vivo the molecular modelling data, osmotin has been purified from nicotiana tabacum seeds and its nine residues peptide synthesized. We have used cultured human synovial fibroblasts that respond to adiponectin by increasing the expression of IL-6, TNF-alpha and IL-1beta via ADIPOR1. The biological effect on fibroblasts of osmotin and its peptide derivative has been found similar to that of adiponectin confirming the results found in silico.

Coupling of g proteins to reconstituted monomers and tetramers of the M2 muscarinic receptor.

PubMed

Redka, Dar'ya S; Morizumi, Takefumi; Elmslie, Gwendolynne; Paranthaman, Pranavan; Shivnaraine, Rabindra V; Ellis, John; Ernst, Oliver P; Wells, James W

2014-08-29

G protein-coupled receptors can be reconstituted as monomers in nanodiscs and as tetramers in liposomes. When reconstituted with G proteins, both forms enable an allosteric interaction between agonists and guanylyl nucleotides. Both forms, therefore, are candidates for the complex that controls signaling at the level of the receptor. To identify the biologically relevant form, reconstituted monomers and tetramers of the purified M2 muscarinic receptor were compared with muscarinic receptors in sarcolemmal membranes for the effect of guanosine 5'-[β,γ-imido]triphosphate (GMP-PNP) on the inhibition of N-[(3)H]methylscopolamine by the agonist oxotremorine-M. With monomers, a stepwise increase in the concentration of GMP-PNP effected a lateral, rightward shift in the semilogarithmic binding profile (i.e. a progressive decrease in the apparent affinity of oxotremorine-M). With tetramers and receptors in sarcolemmal membranes, GMP-PNP effected a vertical, upward shift (i.e. an apparent redistribution of sites from a state of high affinity to one of low affinity with no change in affinity per se). The data were analyzed in terms of a mechanistic scheme based on a ligand-regulated equilibrium between uncoupled and G protein-coupled receptors (the "ternary complex model"). The model predicts a rightward shift in the presence of GMP-PNP and could not account for the effects at tetramers in vesicles or receptors in sarcolemmal membranes. Monomers present a special case of the model in which agonists and guanylyl nucleotides interact within a complex that is both constitutive and stable. The results favor oligomers of the M2 receptor over monomers as the biologically relevant state for coupling to G proteins. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Multi-scale clustering by building a robust and self correcting ultrametric topology on data points.

PubMed

Fushing, Hsieh; Wang, Hui; Vanderwaal, Kimberly; McCowan, Brenda; Koehl, Patrice

2013-01-01

The advent of high-throughput technologies and the concurrent advances in information sciences have led to an explosion in size and complexity of the data sets collected in biological sciences. The biggest challenge today is to assimilate this wealth of information into a conceptual framework that will help us decipher biological functions. A large and complex collection of data, usually called a data cloud, naturally embeds multi-scale characteristics and features, generically termed geometry. Understanding this geometry is the foundation for extracting knowledge from data. We have developed a new methodology, called data cloud geometry-tree (DCG-tree), to resolve this challenge. This new procedure has two main features that are keys to its success. Firstly, it derives from the empirical similarity measurements a hierarchy of clustering configurations that captures the geometric structure of the data. This hierarchy is then transformed into an ultrametric space, which is then represented via an ultrametric tree or a Parisi matrix. Secondly, it has a built-in mechanism for self-correcting clustering membership across different tree levels. We have compared the trees generated with this new algorithm to equivalent trees derived with the standard Hierarchical Clustering method on simulated as well as real data clouds from fMRI brain connectivity studies, cancer genomics, giraffe social networks, and Lewis Carroll's Doublets network. In each of these cases, we have shown that the DCG trees are more robust and less sensitive to measurement errors, and that they provide a better quantification of the multi-scale geometric structures of the data. As such, DCG-tree is an effective tool for analyzing complex biological data sets.

Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets*

PubMed Central

Yu, Xueping; Ivanic, Joseph; Memišević, Vesna; Wallqvist, Anders; Reifman, Jaques

2011-01-01

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge. PMID:21876202

Nonparametric Combinatorial Sequence Models

NASA Astrophysics Data System (ADS)

Wauthier, Fabian L.; Jordan, Michael I.; Jojic, Nebojsa

This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This paper presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three sequence datasets which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution induced by the prior. By integrating out the posterior our method compares favorably to leading binding predictors.

Probing protein-lipid interactions by FRET between membrane fluorophores

NASA Astrophysics Data System (ADS)

Trusova, Valeriya M.; Gorbenko, Galyna P.; Deligeorgiev, Todor; Gadjev, Nikolai

2016-09-01

Förster resonance energy transfer (FRET) is a powerful fluorescence technique that has found numerous applications in medicine and biology. One area where FRET proved to be especially informative involves the intermolecular interactions in biological membranes. The present study was focused on developing and verifying a Monte-Carlo approach to analyzing the results of FRET between the membrane-bound fluorophores. This approach was employed to quantify FRET from benzanthrone dye ABM to squaraine dye SQ-1 in the model protein-lipid system containing a polycationic globular protein lysozyme and negatively charged lipid vesicles composed of phosphatidylcholine and phosphatidylglycerol. It was found that acceptor redistribution between the lipid bilayer and protein binding sites resulted in the decrease of FRET efficiency. Quantification of this effect in terms of the proposed methodology yielded both structural and binding parameters of lysozyme-lipid complexes.

Global identifiability of linear compartmental models--a computer algebra algorithm.

PubMed

Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

1998-01-01

A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

Chemical cross-linking and native mass spectrometry: A fruitful combination for structural biology

PubMed Central

Sinz, Andrea; Arlt, Christian; Chorev, Dror; Sharon, Michal

2015-01-01

Mass spectrometry (MS) is becoming increasingly popular in the field of structural biology for analyzing protein three-dimensional-structures and for mapping protein–protein interactions. In this review, the specific contributions of chemical crosslinking and native MS are outlined to reveal the structural features of proteins and protein assemblies. Both strategies are illustrated based on the examples of the tetrameric tumor suppressor protein p53 and multisubunit vinculin-Arp2/3 hybrid complexes. We describe the distinct advantages and limitations of each technique and highlight synergistic effects when both techniques are combined. Integrating both methods is especially useful for characterizing large protein assemblies and for capturing transient interactions. We also point out the future directions we foresee for a combination of in vivo crosslinking and native MS for structural investigation of intact protein assemblies. PMID:25970732

Study on molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM, HOMO-LUMO energies and docking studies of 5-fluorouracil, a substance used to treat cancer

NASA Astrophysics Data System (ADS)

Almeida, Michell O.; Barros, Daiane A. S.; Araujo, Sheila C.; Faria, Sergio H. D. M.; Maltarollo, Vinicius G.; Honorio, Kathia M.

2017-09-01

Cancer cells can expand to other parts of body through blood system and nodes from a mechanism known as metastasis. Due to the large annual growth of cancer cases, various biological targets have been studied and related to this disorder. A very interesting target related to cancer is human epidermal growth factor receptor 2 (HER2). In this study, we analyzed the main intermolecular interactions between a drug used in the cancer treatment (5-fluorouracil) and HER2. Molecular modeling methods were also employed to assess the molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM and HOMO-LUMO energies of 5-FU. From the docking simulations it was possible to analyze the interactions that occur between some residues in the binding site of HER2 and 5-FU. To validate the choice of basis set that was used in the NBO and QTAIM analyses, theoretical calculations were performed to obtain FT-IR and UV/Vis spectra, and the theoretical results are consistent with the experimental data, showing that the basis set chosen is suitable. For the maximum λ from the theoretical calculation (254.89 nm) of UV/Vis, the electronic transition from HOMO to LUMO occurs at 4.89 eV. From NBO analyses, we observed interactions between Asp863 and 5-FU, i.e. the orbitals with high transfer of electrons are LP O15 (donor NBO) and BD* (π) N1-H10 (acceptor NBO), being that the value of this interaction is 7.72 kcal/mol. Results from QTAIM indicate one main intermolecular H bond, which is necessary to stabilize the complex formed between the ligands and the biological target. Therefore, this study allowed a careful evaluation on the main structural, spectroscopic and electronic properties involved in the interaction between 5-FU and HER2, an important biological complex related to the cancer treatment.

Targeted Proteomic Quantification on Quadrupole-Orbitrap Mass Spectrometer*

PubMed Central

Gallien, Sebastien; Duriez, Elodie; Crone, Catharina; Kellmann, Markus; Moehring, Thomas; Domon, Bruno

2012-01-01

There is an immediate need for improved methods to systematically and precisely quantify large sets of peptides in complex biological samples. To date protein quantification in biological samples has been routinely performed on triple quadrupole instruments operated in selected reaction monitoring mode (SRM), and two major challenges remain. Firstly, the number of peptides to be included in one survey experiment needs to be increased to routinely reach several hundreds, and secondly, the degree of selectivity should be improved so as to reliably discriminate the targeted analytes from background interferences. High resolution and accurate mass (HR/AM) analysis on the recently developed Q-Exactive mass spectrometer can potentially address these issues. This instrument presents a unique configuration: it is constituted of an orbitrap mass analyzer equipped with a quadrupole mass filter as the front-end for precursor ion mass selection. This configuration enables new quantitative methods based on HR/AM measurements, including targeted analysis in MS mode (single ion monitoring) and in MS/MS mode (parallel reaction monitoring). The ability of the quadrupole to select a restricted m/z range allows one to overcome the dynamic range limitations associated with trapping devices, and the MS/MS mode provides an additional stage of selectivity. When applied to targeted protein quantification in urine samples and benchmarked with the reference SRM technique, the quadrupole-orbitrap instrument exhibits similar or better performance in terms of selectivity, dynamic range, and sensitivity. This high performance is further enhanced by leveraging the multiplexing capability of the instrument to design novel acquisition methods and apply them to large targeted proteomic studies for the first time, as demonstrated on 770 tryptic yeast peptides analyzed in one 60-min experiment. The increased quality of quadrupole-orbitrap data has the potential to improve existing protein quantification methods in complex samples and address the pressing demand of systems biology or biomarker evaluation studies. PMID:22962056

Computer-based fluorescence quantification: a novel approach to study nucleolar biology

PubMed Central

2011-01-01

Background Nucleoli are composed of possibly several thousand different proteins and represent the most conspicuous compartments in the nucleus; they play a crucial role in the proper execution of many cellular processes. As such, nucleoli carry out ribosome biogenesis and sequester or associate with key molecules that regulate cell cycle progression, tumorigenesis, apoptosis and the stress response. Nucleoli are dynamic compartments that are characterized by a constant flux of macromolecules. Given the complex and dynamic composition of the nucleolar proteome, it is challenging to link modifications in nucleolar composition to downstream effects. Results In this contribution, we present quantitative immunofluorescence methods that rely on computer-based image analysis. We demonstrate the effectiveness of these techniques by monitoring the dynamic association of proteins and RNA with nucleoli under different physiological conditions. Thus, the protocols described by us were employed to study stress-dependent changes in the nucleolar concentration of endogenous and GFP-tagged proteins. Furthermore, our methods were applied to measure de novo RNA synthesis that is associated with nucleoli. We show that the techniques described here can be easily combined with automated high throughput screening (HTS) platforms, making it possible to obtain large data sets and analyze many of the biological processes that are located in nucleoli. Conclusions Our protocols set the stage to analyze in a quantitative fashion the kinetics of shuttling nucleolar proteins, both at the single cell level as well as for a large number of cells. Moreover, the procedures described here are compatible with high throughput image acquisition and analysis using HTS automated platforms, thereby providing the basis to quantify nucleolar components and activities for numerous samples and experimental conditions. Together with the growing amount of information obtained for the nucleolar proteome, improvements in quantitative microscopy as they are described here can be expected to produce new insights into the complex biological functions that are orchestrated by the nucleolus. PMID:21639891

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model

PubMed Central

Xia, Kai; Dong, Dong; Han, Jing-Dong J

2006-01-01

Background Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. Results By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. Conclusion IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer. PMID:17112386

Describing the complexity of systems: multivariable "set complexity" and the information basis of systems biology.

PubMed

Galas, David J; Sakhanenko, Nikita A; Skupin, Alexander; Ignac, Tomasz

2014-02-01

Context dependence is central to the description of complexity. Keying on the pairwise definition of "set complexity," we use an information theory approach to formulate general measures of systems complexity. We examine the properties of multivariable dependency starting with the concept of interaction information. We then present a new measure for unbiased detection of multivariable dependency, "differential interaction information." This quantity for two variables reduces to the pairwise "set complexity" previously proposed as a context-dependent measure of information in biological systems. We generalize it here to an arbitrary number of variables. Critical limiting properties of the "differential interaction information" are key to the generalization. This measure extends previous ideas about biological information and provides a more sophisticated basis for the study of complexity. The properties of "differential interaction information" also suggest new approaches to data analysis. Given a data set of system measurements, differential interaction information can provide a measure of collective dependence, which can be represented in hypergraphs describing complex system interaction patterns. We investigate this kind of analysis using simulated data sets. The conjoining of a generalized set complexity measure, multivariable dependency analysis, and hypergraphs is our central result. While our focus is on complex biological systems, our results are applicable to any complex system.

Elucidating complicated assembling systems in biology using size-and-shape analysis of sedimentation velocity data

PubMed Central

Chaton, Catherine T.

2017-01-01

Sedimentation velocity analytical ultracentrifugation (SV-AUC) has seen a resurgence in popularity as a technique for characterizing macromolecules and complexes in solution. SV-AUC is a particularly powerful tool for studying protein conformation, complex stoichiometry, and interacting systems in general. Deconvoluting velocity data to determine a sedimentation coefficient distribution c(s) allows for the study of either individual proteins or multi-component mixtures. The standard c(s) approach estimates molar masses of the sedimenting species based on determination of the frictional ratio (f/f0) from boundary shapes. The frictional ratio in this case is a weight-averaged parameter, which can lead to distortion of mass estimates and loss of information when attempting to analyze mixtures of macromolecules with different shapes. A two-dimensional extension of the c(s) analysis approach provides size-and-shape distributions that describe the data in terms of a sedimentation coefficient and frictional ratio grid. This allows for better resolution of species with very distinct shapes that may co-sediment and provides better molar mass determinations for multi-component mixtures. An example case is illustrated using globular and non-globular proteins of different masses with nearly identical sedimentation coefficients that could only be resolved using the size-and-shape distribution. Other applications of this analytical approach to complex biological systems are presented, focusing on proteins involved in the innate immune response to cytosolic microbial DNA. PMID:26412652

Velocity and displacement statistics in a stochastic model of nonlinear friction showing bounded particle speed

NASA Astrophysics Data System (ADS)

Menzel, Andreas M.

2015-11-01

Diffusion of colloidal particles in a complex environment such as polymer networks or biological cells is a topic of high complexity with significant biological and medical relevance. In such situations, the interaction between the surroundings and the particle motion has to be taken into account. We analyze a simplified diffusion model that includes some aspects of a complex environment in the framework of a nonlinear friction process: at low particle speeds, friction grows linearly with the particle velocity as for regular viscous friction; it grows more than linearly at higher particle speeds; finally, at a maximum of the possible particle speed, the friction diverges. In addition to bare diffusion, we study the influence of a constant drift force acting on the diffusing particle. While the corresponding stationary velocity distributions can be derived analytically, the displacement statistics generally must be determined numerically. However, as a benefit of our model, analytical progress can be made in one case of a special maximum particle speed. The effect of a drift force in this case is analytically determined by perturbation theory. It will be interesting in the future to compare our results to real experimental systems. One realization could be magnetic colloidal particles diffusing through a shear-thickening environment such as starch suspensions, possibly exposed to an external magnetic field gradient.

Application of dynamic topic models to toxicogenomics data.

PubMed

Lee, Mikyung; Liu, Zhichao; Huang, Ruili; Tong, Weida

2016-10-06

All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data. A large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with 131 compounds (most are drugs) at two doses (control and high dose) in a repeated schedule containing four separate time points (4-, 8-, 15- and 29-day). We analyzed, with DTM, the topics (consisting of a set of genes) and their biological interpretations over these four time points. We identified hidden patterns embedded in this time-series gene expression profiles. From the topic distribution for compound-time condition, a number of drugs were successfully clustered by their shared mode-of-action such as PPARɑ agonists and COX inhibitors. The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. Additionally, we found that sample clusters produced by DTM are much more coherent in terms of functional categories when compared to traditional clustering algorithms. We demonstrated that DTM, a text mining technique, can be a powerful computational approach for clustering time-series gene expression profiles with the probabilistic representation of their dynamic features along sequential time frames. The method offers an alternative way for uncovering hidden patterns embedded in time series gene expression profiles to gain enhanced understanding of dynamic behavior of gene regulation in the biological system.

Mass spectrometry for the detection of bioterrorism agents: from environmental to clinical applications.

PubMed

Duriez, Elodie; Armengaud, Jean; Fenaille, François; Ezan, Eric

2016-03-01

In the current context of international conflicts and localized terrorist actions, there is unfortunately a permanent threat of attacks with unconventional warfare agents. Among these, biological agents such as toxins, microorganisms, and viruses deserve particular attention owing to their ease of production and dissemination. Mass spectrometry (MS)-based techniques for the detection and quantification of biological agents have a decisive role to play for countermeasures in a scenario of biological attacks. The application of MS to every field of both organic and macromolecular species has in recent years been revolutionized by the development of soft ionization techniques (MALDI and ESI), and by the continuous development of MS technologies (high resolution, accurate mass HR/AM instruments, novel analyzers, hybrid configurations). New possibilities have emerged for exquisite specific and sensitive detection of biological warfare agents. MS-based strategies for clinical application can now address a wide range of analytical questions mainly including issues related to the complexity of biological samples and their available volume. Multiplexed toxin detection, discovery of new markers through omics approaches, and identification of untargeted microbiological or of novel molecular targets are examples of applications. In this paper, we will present these technological advances along with the novel perspectives offered by omics approaches to clinical detection and follow-up. Copyright © 2016 John Wiley & Sons, Ltd.

SLEPR: A Sample-Level Enrichment-Based Pathway Ranking Method — Seeking Biological Themes through Pathway-Level Consistency

PubMed Central

Yi, Ming; Stephens, Robert M.

2008-01-01

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data. PMID:18818771

Genetic Pedagogical Content Knowledge (PCK) Ability Profile of Prospective Biology Teacher

NASA Astrophysics Data System (ADS)

Purwianingsih, W.; Muthmainnah, E.; Hidayat, T.

2017-02-01

Genetics is one of the topics or subject matter in biology that are considered difficult. Student difficulties of understanding genetics, can be caused by lack of understanding this concept and the way of teachers teach. Pedagogical Content Knowledge (PCK) is a way to understand the complex relationships between teaching and content taught through the use of specific teaching approaches. The aims of study was to analyze genetic PCK ability profile of prospective biology teacher.13 student of sixth semester Biology education department who learned Kapita Selekta Biologi SMA course, participated in this study. PCK development was measured by CoRes (Content Representation). Before students fill CoRes, students are tested mastery genetic concepts through a multiple-choice test with three tier-test. Data was obtained from the prior CoRes and its revisions, as well as the mastery concept in pre and post test. Results showed that pre-test of genetic mastery concepts average on 55.4% (low category) and beginning of the writing CoRes, student get 43.2% (Pra PCK). After students get lecture and simulating learning, the post-test increased to 63.8% (sufficient category) and PCK revision is also increase 58.1% (growing PCK). It can be concluded that mastery of subject matter could affects the ability of genetic PCK.

Simple neural substrate predicts complex rhythmic structure in duetting birds

NASA Astrophysics Data System (ADS)

Amador, Ana; Trevisan, M. A.; Mindlin, G. B.

2005-09-01

Horneros (Furnarius Rufus) are South American birds well known for their oven-looking nests and their ability to sing in couples. Previous work has analyzed the rhythmic organization of the duets, unveiling a mathematical structure behind the songs. In this work we analyze in detail an extended database of duets. The rhythms of the songs are compatible with the dynamics presented by a wide class of dynamical systems: forced excitable systems. Compatible with this nonlinear rule, we build a biologically inspired model for how the neural and the anatomical elements may interact to produce the observed rhythmic patterns. This model allows us to synthesize songs presenting the acoustic and rhythmic features observed in real songs. We also make testable predictions in order to support our hypothesis.

Statistical analysis and machine learning algorithms for optical biopsy

NASA Astrophysics Data System (ADS)

Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R.

2018-02-01

Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.

Excitation energy transfer in photosynthetic protein-pigment complexes

NASA Astrophysics Data System (ADS)

Yeh, Shu-Hao

Quantum biology is a relatively new research area which investigates the rules that quantum mechanics plays in biology. One of the most intriguing systems in this field is the coherent excitation energy transport (EET) in photosynthesis. In this document I will discuss the theories that are suitable for describing the photosynthetic EET process and the corresponding numerical results on several photosynthetic protein-pigment complexes (PPCs). In some photosynthetic EET processes, because of the electronic coupling between the chromophores within the system is about the same order of magnitude as system-bath coupling (electron-phonon coupling), a non-perturbative method called hierarchy equation of motion (HEOM) is applied to study the EET dynamics. The first part of this thesis includes brief introduction and derivation to the HEOM approach. The second part of this thesis the HEOM method will be applied to investigate the EET process within the B850 ring of the light harvesting complex 2 (LH2) from purple bacteria, Rhodopseudomonas acidophila. The dynamics of the exciton population and coherence will be analyzed under different initial excitation configurations and temperatures. Finally, how HEOM can be implemented to simulate the two-dimensional electronic spectra of photosynthetic PPCs will be discussed. Two-dimensional electronic spectroscopy is a crucial experimental technique to probe EET dynamics in multi-chromophoric systems. The system we are interested in is the 7-chromophore Fenna-Matthews-Olson (FMO) complex from green sulfur bacteria, Prosthecochloris aestuarii. Recent crystallographic studies report the existence of an additional (eighth) chromophore in some of the FMO monomers. By applying HEOM we are able to calculate the two-dimensional electronic spectra of the 7-site and 8-site FMO complexes and investigate the functionality of the eighth chromophore.

Extracting physics of life at the molecular level: A review of single-molecule data analyses.

PubMed

Colomb, Warren; Sarkar, Susanta K

2015-06-01

Studying individual biomolecules at the single-molecule level has proved very insightful recently. Single-molecule experiments allow us to probe both the equilibrium and nonequilibrium properties as well as make quantitative connections with ensemble experiments and equilibrium thermodynamics. However, it is important to be careful about the analysis of single-molecule data because of the noise present and the lack of theoretical framework for processes far away from equilibrium. Biomolecular motion, whether it is free in solution, on a substrate, or under force, involves thermal fluctuations in varying degrees, which makes the motion noisy. In addition, the noise from the experimental setup makes it even more complex. The details of biologically relevant interactions, conformational dynamics, and activities are hidden in the noisy single-molecule data. As such, extracting biological insights from noisy data is still an active area of research. In this review, we will focus on analyzing both fluorescence-based and force-based single-molecule experiments and gaining biological insights at the single-molecule level. Inherently nonequilibrium nature of biological processes will be highlighted. Simulated trajectories of biomolecular diffusion will be used to compare and validate various analysis techniques. Copyright © 2015 Elsevier B.V. All rights reserved.

From metaphor to practices: The introduction of "information engineers" into the first DNA sequence database.

PubMed

García-Sancho, Miguel

2011-01-01

This paper explores the introduction of professional systems engineers and information management practices into the first centralized DNA sequence database, developed at the European Molecular Biology Laboratory (EMBL) during the 1980s. In so doing, it complements the literature on the emergence of an information discourse after World War II and its subsequent influence in biological research. By the careers of the database creators and the computer algorithms they designed, analyzing, from the mid-1960s onwards information in biology gradually shifted from a pervasive metaphor to be embodied in practices and professionals such as those incorporated at the EMBL. I then investigate the reception of these database professionals by the EMBL biological staff, which evolved from initial disregard to necessary collaboration as the relationship between DNA, genes, and proteins turned out to be more complex than expected. The trajectories of the database professionals at the EMBL suggest that the initial subject matter of the historiography of genomics should be the long-standing practices that emerged after World War II and to a large extent originated outside biomedicine and academia. Only after addressing these practices, historians may turn to their further disciplinary assemblage in fields such as bioinformatics or biotechnology.

Collaboratively charting the gene-to-phenotype network of human congenital heart defects

PubMed Central

2010-01-01

Background How to efficiently integrate the daily practice of molecular biologists, geneticists, and clinicians with the emerging computational strategies from systems biology is still much of an open question. Description We built on the recent advances in Wiki-based technologies to develop a collaborative knowledge base and gene prioritization portal aimed at mapping genes and genomic regions, and untangling their relations with corresponding human phenotypes, congenital heart defects (CHDs). This portal is not only an evolving community repository of current knowledge on the genetic basis of CHDs, but also a collaborative environment for the study of candidate genes potentially implicated in CHDs - in particular by integrating recent strategies for the statistical prioritization of candidate genes. It thus serves and connects the broad community that is facing CHDs, ranging from the pediatric cardiologist and clinical geneticist to the basic investigator of cardiogenesis. Conclusions This study describes the first specialized portal to collaboratively annotate and analyze gene-phenotype networks. Of broad interest to the biological community, we argue that such portals will play a significant role in systems biology studies of numerous complex biological processes. CHDWiki is accessible at http://www.esat.kuleuven.be/~bioiuser/chdwiki PMID:20193066

Enhancing glycan isomer separations with metal ions and positive and negative polarity ion mobility spectrometry-mass spectrometry analyses

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

Zheng, Xueyun; Zhang, Xing; Schocker, Nathaniel S.

Glycomics has become an increasingly important field of research since glycans play critical roles in biology processes ranging from molecular recognition and signaling to cellular communication. Glycans often conjugate with other biomolecules such as proteins and lipids, and alter their properties and functions, so understanding the effect glycans have on cellular systems is essential. However the analysis of glycans is extremely difficult due to their complexity and structural diversity (i.e., the number and identity of monomer units, and configuration of their glycosidic linkages and connectivities). In this work, we coupled ion mobility spectrometry with mass spectrometry (IMS-MS) to characterize glycanmore » standards and biologically important isomers of synthetic αGal-containing O-glycans including glycotopes of the protozoan parasite Trypanosoma cruzi, which is the causative agent of Chagas disease. IMS-MS results showed significant differences for the glycan structural isomers when analyzed in positive and negative polarity and complexed with different metal cations. These results suggest specific metal ions or ion polarities could be used to target and baseline separate glycan isomers of interest with IMS-MS.« less

Comparative Study of the Minichromosome Maintenance Proteins Complex (MCM 4/5/6) in Ameloblastoma and Unicystic Ameloblastoma.

PubMed

Apellániz, Delmira; Pereira-Prado, Vanesa; Sicco, Estefania; Vigil-Bastitta, Gabriela; González-González, Rogelio; Mosqueda-Taylor, Adalberto; Molina-Frechero, Nelly; Hernandez, Marcela; Sánchez-Romero, Celeste; Bologna-Molina, Ronell

2018-05-01

Solid/conventional ameloblastoma (AM) and unicystic ameloblastoma (UAM) are the most frequent benign epithelial odontogenic tumors located in the maxillary region, and their treatment usually consists of extensive surgical resection. Therefore, it is relevant to study molecular markers to better understand the biological behavior of these tumors. The aim of this study was to describe and compare the expression of proteins related to cellular proliferation: Ki-67 and MCM4-6 complex. An immunohistochemistry technique was performed, with antibodies against Ki-67, MCM4, MCM5, and MCM6, in 10 AM and 10 UAM tumors. The results were quantified using label index and analyzed statistically. AM and UAM had greater expression of MCM6, followed by MCM5, MCM4, and Ki-67 ( P < .05). Immunoexpression of Ki-67 and MCM5 was exclusively nuclear, whereas the expression of MCM4 and MCM6 was nuclear and cytoplasmic. The results suggest that MCM5 is a trustable cell proliferation marker with higher sensitivity compared with Ki-67 and may be useful to predict the biological behavior of AM and UAM. Despite this, further studies are necessary, including a correlation with clinical parameters to confirm these findings.

Chromatographic analysis of tryptophan metabolites

PubMed Central

Sadok, Ilona; Gamian, Andrzej

2017-01-01

The kynurenine pathway generates multiple tryptophan metabolites called collectively kynurenines and leads to formation of the enzyme cofactor nicotinamide adenine dinucleotide. The first step in this pathway is tryptophan degradation, initiated by the rate‐limiting enzymes indoleamine 2,3‐dioxygenase, or tryptophan 2,3‐dioxygenase, depending on the tissue. The balanced kynurenine metabolism, which has been a subject of multiple studies in last decades, plays an important role in several physiological and pathological conditions such as infections, autoimmunity, neurological disorders, cancer, cataracts, as well as pregnancy. Understanding the regulation of tryptophan depletion provide novel diagnostic and treatment opportunities, however it requires reliable methods for quantification of kynurenines in biological samples with complex composition (body fluids, tissues, or cells). Trace concentrations, interference of sample components, and instability of some tryptophan metabolites need to be addressed using analytical methods. The novel separation approaches and optimized extraction protocols help to overcome difficulties in analyzing kynurenines within the complex tissue material. Recent developments in chromatography coupled with mass spectrometry provide new opportunity for quantification of tryptophan and its degradation products in various biological samples. In this review, we present current accomplishments in the chromatographic methodologies proposed for detection of tryptophan metabolites and provide a guide for choosing the optimal approach. PMID:28590049

Allosteric effects of gold nanoparticles on human serum albumin.

PubMed

Shao, Qing; Hall, Carol K

2017-01-07

The ability of nanoparticles to alter protein structure and dynamics plays an important role in their medical and biological applications. We investigate allosteric effects of gold nanoparticles on human serum albumin protein using molecular simulations. The extent to which bound nanoparticles influence the structure and dynamics of residues distant from the binding site is analyzed. The root mean square deviation, root mean square fluctuation and variation in the secondary structure of individual residues on a human serum albumin protein are calculated for four protein-gold nanoparticle binding complexes. The complexes are identified in a brute-force search process using an implicit-solvent coarse-grained model for proteins and nanoparticles. They are then converted to atomic resolution and their structural and dynamic properties are investigated using explicit-solvent atomistic molecular dynamics simulations. The results show that even though the albumin protein remains in a folded structure, the presence of a gold nanoparticle can cause more than 50% of the residues to decrease their flexibility significantly, and approximately 10% of the residues to change their secondary structure. These affected residues are distributed on the whole protein, even on regions that are distant from the nanoparticle. We analyze the changes in structure and flexibility of amino acid residues on a variety of binding sites on albumin and confirm that nanoparticles could allosterically affect the ability of albumin to bind fatty acids, thyroxin and metals. Our simulations suggest that allosteric effects must be considered when designing and deploying nanoparticles in medical and biological applications that depend on protein-nanoparticle interactions.

Biological and nonbiological complex drugs for multiple sclerosis in Latin America: regulations and risk management.

PubMed

Carrá, Adriana; Macías Islas, Miguel Angel; Tarulla, Adriana; Bichuetti, Denis Bernardi; Finkelsztejn, Alessandro; Fragoso, Yara Dadalti; Árcega-Revilla, Raul; Cárcamo Rodríguez, Claudia; Durán, Juan Carlos; Bonitto, Juan García; León, Rosalba; Oehninger Gatti, Carlos; Orozco, Geraldine; Vizcarra Escobar, Darwin

2015-06-01

Biological drugs and nonbiological complex drugs with expired patents are followed by biosimilars and follow-on drugs that are supposedly similar and comparable with the reference product in terms of quality, safety and efficacy. Unlike simple molecules that can be copied and reproduced, biosimilars and follow-on complex drugs are heterogeneous and need specific regulations from health and pharmacovigilance agencies. A panel of 14 Latin American experts on multiple sclerosis from nine different countries met to discuss the recommendations regarding biosimilars and follow-on complex drugs for treating multiple sclerosis. Specific measures relating to manufacturing, therapeutic equivalence assessment and pharmacovigilance reports need to be implemented before commercialization. Physical, chemical, biological and immunogenic characterizations of the new product need to be available before clinical trials start. The new product must maintain the same immunogenicity as the original. Automatic substitution of biological and complex drugs poses unacceptable risks to the patient.

Beyond bilateral symmetry: geometric morphometric methods for any type of symmetry

PubMed Central

2011-01-01

Background Studies of symmetric structures have made important contributions to evolutionary biology, for example, by using fluctuating asymmetry as a measure of developmental instability or for investigating the mechanisms of morphological integration. Most analyses of symmetry and asymmetry have focused on organisms or parts with bilateral symmetry. This is not the only type of symmetry in biological shapes, however, because a multitude of other types of symmetry exists in plants and animals. For instance, some organisms have two axes of reflection symmetry (biradial symmetry; e.g. many algae, corals and flowers) or rotational symmetry (e.g. sea urchins and many flowers). So far, there is no general method for the shape analysis of these types of symmetry. Results We generalize the morphometric methods currently used for the shape analysis of bilaterally symmetric objects so that they can be used for analyzing any type of symmetry. Our framework uses a mathematical definition of symmetry based on the theory of symmetry groups. This approach can be used to divide shape variation into a component of symmetric variation among individuals and one or more components of asymmetry. We illustrate this approach with data from a colonial coral that has ambiguous symmetry and thus can be analyzed in multiple ways. Our results demonstrate that asymmetric variation predominates in this dataset and that its amount depends on the type of symmetry considered in the analysis. Conclusions The framework for analyzing symmetry and asymmetry is suitable for studying structures with any type of symmetry in two or three dimensions. Studies of complex symmetries are promising for many contexts in evolutionary biology, such as fluctuating asymmetry, because these structures can potentially provide more information than structures with bilateral symmetry. PMID:21958045

Encoding of a spectrally-complex communication sound in the bullfrog's auditory nerve.

PubMed

Schwartz, J J; Simmons, A M

1990-02-01

1. A population study of eighth nerve responses in the bullfrog, Rana catesbeiana, was undertaken to analyze how the eighth nerve codes the complex spectral and temporal structure of the species-specific advertisement call over a biologically-realistic range of intensities. Synthetic advertisement calls were generated by Fourier synthesis and presented to individual eighth nerve fibers of anesthetized bullfrogs. Fiber responses were analyzed by calculating rate responses based on post-stimulus-time (PST) histograms and temporal responses based on Fourier transforms of period histograms. 2. At stimulus intensities of 70 and 80 dB SPL, normalized rate responses provide a fairly good representation of the complex spectral structure of the stimulus, particularly in the low- and mid-frequency range. At higher intensities, rate responses saturate, and very little of the spectral structure of the complex stimulus can be seen in the profile of rate responses of the population. 3. Both AP and BP fibers phase-lock strongly to the fundamental (100 Hz) of the complex stimulus. These effects are relatively resistant to changes in stimulus intensity. Only a small number of fibers synchronize to the low-frequency spectral energy in the stimulus. The underlying spectral complexity of the stimulus is not accurately reflected in the timing of fiber firing, presumably because firing is 'captured' by the fundamental frequency. 4. Plots of average localized synchronized rate (ALSR), which combine both spectral and temporal information, show a similar, low-pass shape at all stimulus intensities. ALSR plots do not generally provide an accurate representation of the structure of the advertisement call. 5. The data suggest that anuran peripheral auditory fibers may be particularly sensitive to the amplitude envelope of sounds.

Use of Graph Database for the Integration of Heterogeneous Biological Data.

PubMed

Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young

2017-03-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

Use of Graph Database for the Integration of Heterogeneous Biological Data

PubMed Central

Yoon, Byoung-Ha; Kim, Seon-Kyu

2017-01-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data. PMID:28416946

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

PubMed

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks

PubMed Central

Li, Min; Li, Dongyan; Tang, Yu; Wang, Jianxin

2017-01-01

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster. PMID:28858211

Synthetic biology: insights into biological computation.

PubMed

Manzoni, Romilde; Urrios, Arturo; Velazquez-Garcia, Silvia; de Nadal, Eulàlia; Posas, Francesc

2016-04-18

Organisms have evolved a broad array of complex signaling mechanisms that allow them to survive in a wide range of environmental conditions. They are able to sense external inputs and produce an output response by computing the information. Synthetic biology attempts to rationally engineer biological systems in order to perform desired functions. Our increasing understanding of biological systems guides this rational design, while the huge background in electronics for building circuits defines the methodology. In this context, biocomputation is the branch of synthetic biology aimed at implementing artificial computational devices using engineered biological motifs as building blocks. Biocomputational devices are defined as biological systems that are able to integrate inputs and return outputs following pre-determined rules. Over the last decade the number of available synthetic engineered devices has increased exponentially; simple and complex circuits have been built in bacteria, yeast and mammalian cells. These devices can manage and store information, take decisions based on past and present inputs, and even convert a transient signal into a sustained response. The field is experiencing a fast growth and every day it is easier to implement more complex biological functions. This is mainly due to advances in in vitro DNA synthesis, new genome editing tools, novel molecular cloning techniques, continuously growing part libraries as well as other technological advances. This allows that digital computation can now be engineered and implemented in biological systems. Simple logic gates can be implemented and connected to perform novel desired functions or to better understand and redesign biological processes. Synthetic biological digital circuits could lead to new therapeutic approaches, as well as new and efficient ways to produce complex molecules such as antibiotics, bioplastics or biofuels. Biological computation not only provides possible biomedical and biotechnological applications, but also affords a greater understanding of biological systems.

Role of carbon nano-materials in the analysis of biological materials by laser desorption/ionization-mass spectrometry.

PubMed

Najam-ul-Haq, M; Rainer, M; Szabó, Z; Vallant, R; Huck, C W; Bonn, G K

2007-03-10

At present, carbon nano-materials are being utilized in various procedures, especially in laser desorption/ionization-mass spectrometry (LDI-MS) for analyzing a range of analytes, which include peptides, proteins, metabolites, and polymers. Matrix-oriented LDI-MS techniques are very well established, with weak organic acids as energy-absorbing substances. Carbon materials, such as nano-tubes and fullerenes are being successfully applied in the small-mass range, where routine matrices have strong background signals. In addition, the role of carbon nano-materials is very well established in the fractionation and purification fields. Modified diamond powder and surfaces are utilized in binding peptides and proteins from complex biological fluids and analyzed by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS). Polylysine-coated diamond is used for solid-phase extraction to pre-concentrate DNA oligonucleotides. Graphite is useful for desalting, pre-concentration, and as energy-absorbing material (matrix) in desorption/ionization. Carbon nano-tubes in their different derivatized forms are used as matrix materials for the analysis of a range of analytes, such as carbohydrates, amino acids, peptides, proteins, and some environmental samples by LDI-MS. Fullerenes are modified in different ways to bind serum entities analyzed through MALDI/TOF-MS and are subsequently utilized in their identifications. In addition, the fullerenes are a promising matrix in LDI-MS, but improvements are needed.

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.

Complex chromosome aberrations persist in individuals many years after occupational exposure to densely ionizing radiation: an mFISH study.

PubMed

Hande, M Prakash; Azizova, Tamara V; Burak, Ludmilla E; Khokhryakov, Valentin F; Geard, Charles R; Brenner, David J

2005-09-01

Long-lived, sensitive, and specific biomarkers of particular mutagenic agents are much sought after and potentially have broad applications in the fields of cancer biology, epidemiology, and prevention. Many clastogens induce a spectrum of chromosome aberrations, and some of them can be exploited as biomarkers of exposure. Densely ionizing radiation, for example, alpha particle radiation (from radon or plutonium) and neutron radiation, preferentially induces complex chromosome aberrations, which can be detected by the 24-color multifluor fluorescence in situ hybridization (mFISH) technique. We report the detection and quantification of stable complex chromosome aberrations in lymphocytes of healthy former nuclear-weapons workers, who were exposed many years ago to plutonium, gamma rays, or both, at the Mayak weapons complex in Russia. We analyzed peripheral-blood lymphocytes from these individuals for the presence of persistent complex chromosome aberrations. A significantly elevated frequency of complex chromosome translocations was detected in the highly exposed plutonium workers but not in the group exposed only to high doses of gamma radiation. No such differences were found for simple chromosomal aberrations. The results suggest that stable complex chromosomal translocations represent a long-lived, quantitative, low-background biomarker of densely ionizing radiation for human populations exposed many years ago. (c) 2005 Wiley-Liss, Inc.

Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches.

PubMed

Yugandhar, K; Gromiha, M Michael

2014-09-01

Protein-protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein-protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein-protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein-protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein-protein complexes into high and low affinity groups based on their Kd values. Our results showed a 10-fold cross-validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein-protein interaction networks and human-pathogen interactions based on the strength of interactions. © 2014 Wiley Periodicals, Inc.

Synthesis, characterization and cytotoxic evaluation of inclusion complexes between Riparin A and β-cyclodextrin

NASA Astrophysics Data System (ADS)

Araújo, Éverton José Ferreira de; Silva, Oskar Almeida; Rezende-Júnior, Luís Mário; Sousa, Ian Jhemes Oliveira; Araújo, Danielle Yasmin Moura Lopes de; Carvalho, Rusbene Bruno Fonseca de; Pereira, Sean Telles; Gutierrez, Stanley Juan Chavez; Ferreira, Paulo Michel Pinheiro; Lima, Francisco das Chagas Alves

2017-08-01

This study performed a physicochemical characterization of the inclusion complex generated between Riparin A and β-cyclodextrin (Rip A/β-CD) and compared the cytotoxic potential of the incorporated Rip A upon Artemia salina larvae. Samples were analyzed by phase solubility diagram, dissolution profile, differential scanning calorimetry, X-ray diffraction, infrared spectroscopy, proton nuclear magnetic resonance, scanning electron microscopy and artemicidal action. Riparin A/β-cyclodextrin complexes presented increased water solubility, AL type solubility diagram and Kst constant of 373 L/mol. Thermal analysis demonstrated reduction of the melt peak of complexed Rip A at 116.2 °C. Infrared spectroscopy confirmed generation of inclusion complexes, 1H NMR pointed out the interaction with H-3 of β-CD cavities, alterations in the crystalline natures of Rip A when incorporated within β-CD were observed and inclusion complexes presented higher cytotoxic on A. salina nauplii, with CL50 value of 117.2 (84.9-161.8) μg/mL. So, Rip A was incorporated into β-CDs with high efficiency and water solubility of Rip A was improved. Such solubility was corroborated by cytotoxic evaluation and these outcomes support the improvement of biological properties for complexes between Riparin A/β-cyclodextrin.

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.

Genistein in 1:1 Inclusion Complexes with Ramified Cyclodextrins: Theoretical, Physicochemical and Biological Evaluation

PubMed Central

Danciu, Corina; Soica, Codruta; Oltean, Mircea; Avram, Stefana; Borcan, Florin; Csanyi, Erzsebet; Ambrus, Rita; Zupko, Istvan; Muntean, Delia; Dehelean, Cristina A.; Craina, Marius; Popovici, Ramona A.

2014-01-01

Genistein is one of the most studied phytocompound in the class of isoflavones, presenting a notable estrogenic activity and in vitro and/or in vivo benefits in different types of cancer such as those of the bladder, kidney, lung, pancreatic, skin and endometrial cancer. A big inconvenience for drug development is low water solubility, which can be solved by using hydrophilic cyclodextrins. The aim of this study is to theoretically analyze, based on the interaction energy, the possibility of a complex formation between genistein (Gen) and three different ramified cyclodextrins (CD), using a 1:1 molar ratio Gen:CD. Theoretical data were correlated with a screening of both in vitro and in vivo activity. Proliferation of different human cancer cell lines, antimicrobial activity and angiogenesis behavior was analyzed in order to see if complexation has a beneficial effect for any of the above mentioned activities and if so, which of the three CDs is the most suitable for the incorporation of genistein, and which may lead to future improved pharmaceutical formulations. Results showed antiproliferative activity with different IC50 values for all tested cell lines, remarkable antimicrobial activity on Bacillus subtilis and antiangiogenic activity as revealed by CAM assay. Differences regarding the intensity of the activity for pure and the three Gen complexes were noticed as explained in the text. The data represent a proof that the three CDs can be used for furtherer research towards practical use in the pharmaceutical and medical field. PMID:24473144

The evolution of phenotypic integration: How directional selection reshapes covariation in mice

PubMed Central

Penna, Anna; Melo, Diogo; Bernardi, Sandra; Oyarzabal, Maria Inés; Marroig, Gabriel

2017-01-01

Abstract Variation is the basis for evolution, and understanding how variation can evolve is a central question in biology. In complex phenotypes, covariation plays an even more important role, as genetic associations between traits can bias and alter evolutionary change. Covariation can be shaped by complex interactions between loci, and this genetic architecture can also change during evolution. In this article, we analyzed mouse lines experimentally selected for changes in size to address the question of how multivariate covariation changes under directional selection, as well as to identify the consequences of these changes to evolution. Selected lines showed a clear restructuring of covariation in their cranium and, instead of depleting their size variation, these lines increased their magnitude of integration and the proportion of variation associated with the direction of selection. This result is compatible with recent theoretical works on the evolution of covariation that take the complexities of genetic architecture into account. This result also contradicts the traditional view of the effects of selection on available covariation and suggests a much more complex view of how populations respond to selection. PMID:28685813

Detection and characterization of gene-gene and gene-environment interactions in common human diseases and complex clinical endpoints

EPA Science Inventory

Biological organisms are complex systems that dynamically integrate inputs from a multitude of physiological and environmental factors. Therefore, in addressing questions concerning the etiology of complex health outcomes, it is essential that the systemic nature of biology be ta...

A unique large-scale undergraduate research experience in molecular systems biology for non-mathematics majors.

PubMed

Kappler, Ulrike; Rowland, Susan L; Pedwell, Rhianna K

2017-05-01

Systems biology is frequently taught with an emphasis on mathematical modeling approaches. This focus effectively excludes most biology, biochemistry, and molecular biology students, who are not mathematics majors. The mathematical focus can also present a misleading picture of systems biology, which is a multi-disciplinary pursuit requiring collaboration between biochemists, bioinformaticians, and mathematicians. This article describes an authentic large-scale undergraduate research experience (ALURE) in systems biology that incorporates proteomics, bacterial genomics, and bioinformatics in the one exercise. This project is designed to engage students who have a basic grounding in protein chemistry and metabolism and no mathematical modeling skills. The pedagogy around the research experience is designed to help students attack complex datasets and use their emergent metabolic knowledge to make meaning from large amounts of raw data. On completing the ALURE, participants reported a significant increase in their confidence around analyzing large datasets, while the majority of the cohort reported good or great gains in a variety of skills including "analysing data for patterns" and "conducting database or internet searches." An environmental scan shows that this ALURE is the only undergraduate-level system-biology research project offered on a large-scale in Australia; this speaks to the perceived difficulty of implementing such an opportunity for students. We argue however, that based on the student feedback, allowing undergraduate students to complete a systems-biology project is both feasible and desirable, even if the students are not maths and computing majors. © 2016 by The International Union of Biochemistry and Molecular Biology, 45(3):235-248, 2017. © 2016 The International Union of Biochemistry and Molecular Biology.

Using Simple Manipulatives to Improve Student Comprehension of a Complex Biological Process: Protein Synthesis

ERIC Educational Resources Information Center

Guzman, Karen; Bartlett, John

2012-01-01

Biological systems and living processes involve a complex interplay of biochemicals and macromolecular structures that can be challenging for undergraduate students to comprehend and, thus, misconceptions abound. Protein synthesis, or translation, is an example of a biological process for which students often hold many misconceptions. This article…

7th Annual Systems Biology Symposium: Systems Biology and Engineering

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

Galitski, Timothy P.

2008-04-01

Systems biology recognizes the complex multi-scale organization of biological systems, from molecules to ecosystems. The International Symposium on Systems Biology has been hosted by the Institute for Systems Biology in Seattle, Washington, since 2002. The annual two-day event gathers the most influential researchers transforming biology into an integrative discipline investingating complex systems. Engineering and application of new technology is a central element of systems biology. Genome-scale, or very small-scale, biological questions drive the enigneering of new technologies, which enable new modes of experimentation and computational analysis, leading to new biological insights and questions. Concepts and analytical methods in engineering aremore » now finding direct applications in biology. Therefore, the 2008 Symposium, funded in partnership with the Department of Energy, featured global leaders in "Systems Biology and Engineering."« less

Evaluation of "shotgun" proteomics for identification of biological threat agents in complex environmental matrixes: experimental simulations.

PubMed

Verberkmoes, Nathan C; Hervey, W Judson; Shah, Manesh; Land, Miriam; Hauser, Loren; Larimer, Frank W; Van Berkel, Gary J; Goeringer, Douglas E

2005-02-01

There is currently a great need for rapid detection and positive identification of biological threat agents, as well as microbial species in general, directly from complex environmental samples. This need is most urgent in the area of homeland security, but also extends into medical, environmental, and agricultural sciences. Mass-spectrometry-based analysis is one of the leading technologies in the field with a diversity of different methodologies for biothreat detection. Over the past few years, "shotgun"proteomics has become one method of choice for the rapid analysis of complex protein mixtures by mass spectrometry. Recently, it was demonstrated that this methodology is capable of distinguishing a target species against a large database of background species from a single-component sample or dual-component mixtures with relatively the same concentration. Here, we examine the potential of shotgun proteomics to analyze a target species in a background of four contaminant species. We tested the capability of a common commercial mass-spectrometry-based shotgun proteomics platform for the detection of the target species (Escherichia coli) at four different concentrations and four different time points of analysis. We also tested the effect of database size on positive identification of the four microbes used in this study by testing a small (13-species) database and a large (261-species) database. The results clearly indicated that this technology could easily identify the target species at 20% in the background mixture at a 60, 120, 180, or 240 min analysis time with the small database. The results also indicated that the target species could easily be identified at 20% or 6% but could not be identified at 0.6% or 0.06% in either a 240 min analysis or a 30 h analysis with the small database. The effects of the large database were severe on the target species where detection above the background at any concentration used in this study was impossible, though the three other microbes used in this study were clearly identified above the background when analyzed with the large database. This study points to the potential application of this technology for biological threat agent detection but highlights many areas of needed research before the technology will be useful in real world samples.

Evolution of biological complexity

PubMed Central

Adami, Christoph; Ofria, Charles; Collier, Travis C.

2000-01-01

To make a case for or against a trend in the evolution of complexity in biological evolution, complexity needs to be both rigorously defined and measurable. A recent information-theoretic (but intuitively evident) definition identifies genomic complexity with the amount of information a sequence stores about its environment. We investigate the evolution of genomic complexity in populations of digital organisms and monitor in detail the evolutionary transitions that increase complexity. We show that, because natural selection forces genomes to behave as a natural “Maxwell Demon,” within a fixed environment, genomic complexity is forced to increase. PMID:10781045

Efficient biological conversion of carbon monoxide (CO) to carbon dioxide (CO2) and for utilization in bioplastic production by Ralstonia eutropha through the display of an enzyme complex on the cell surface.

PubMed

Hyeon, Jeong Eun; Kim, Seung Wook; Park, Chulhwan; Han, Sung Ok

2015-06-25

An enzyme complex for biological conversion of CO to CO2 was anchored on the cell surface of the CO2-utilizing Ralstonia eutropha and successfully resulted in a 3.3-fold increase in conversion efficiency. These results suggest that this complexed system may be a promising strategy for CO2 utilization as a biological tool for the production of bioplastics.

COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets

PubMed Central

Lohmann, Ingrid

2012-01-01

In multi-cellular organisms, spatiotemporal activity of cis-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with cis-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (Co-Occurrence Pattern Search), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression. PMID:23272209

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.

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering.

PubMed

Groen, Nathalie; Guvendiren, Murat; Rabitz, Herschel; Welsh, William J; Kohn, Joachim; de Boer, Jan

2016-04-01

The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. In this opinion paper, we postulate that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. Copyright © 2016. Published by Elsevier Ltd.

Photo-damage protective effect of two facial products, containing a unique complex of Dead Sea minerals and Himalayan actives.

PubMed

Wineman, Eitan; Portugal-Cohen, Meital; Soroka, Yoram; Cohen, Dror; Schlippe, Gerrit; Voss, Werner; Brenner, Sarah; Milner, Yoram; Hai, Noam; Ma'or, Zeevi

2012-09-01

Skin appearance is badly affected when exposed to solar UV rays, which encourage physiological and structural cutaneous alterations that eventually lead to skin photo-damage. To test the capability of two facial preparations, extreme day cream (EXD) and extreme night treatment (EXN), containing a unique complex of Dead Sea water and three Himalayan extracts, to antagonize biological effects induced by photo-damage. Pieces of organ cultures of human skin were used as a model to assess the biological effects of UVB irradiation and the protective effect of topical application of two Extreme preparations. Skin pieces were analyzed for mitochondrial activity by MTT assay, for apoptosis by caspase 3 assay, and for cytokine secretion by solid phase ELISA. Human subjects were tested to evaluate the effect of Extreme preparations on skin wrinkle depth using PRIMOS and skin hydration by a corneometer. UVB irradiation induced cell apoptosis in the epidermis of skin organ cultures and increased their pro-inflammatory cytokine, tumor necrosis α (TNFα) secretion. Topical applications of both preparations significantly attenuated all these effects. Furthermore, in human subjects, a reduction in wrinkle depth and an elevation in the intense skin moisture were observed. The observations clearly show that EXD and EXN preparations have protective anti-apoptotic and anti-inflammatory properties that can attenuate biological effects of skin photo-damage. Topical application of the preparations improves skin appearance by reducing its wrinkles depth and increasing its moisturizing impact. © 2012 Wiley Periodicals, Inc.

Electrochemical quantum tunneling for electronic detection and characterization of biological toxins

NASA Astrophysics Data System (ADS)

Gupta, Chaitanya; Walker, Ross M.; Gharpuray, Rishi; Shulaker, Max M.; Zhang, Zhiyong; Javanmard, Mehdi; Davis, Ronald W.; Murmann, Boris; Howe, Roger T.

2012-06-01

This paper introduces a label-free, electronic biomolecular sensing platform for the detection and characterization of trace amounts of biological toxins within a complex background matrix. The mechanism for signal transduction is the electrostatic coupling of molecule bond vibrations to charge transport across an insulated electrode-electrolyte interface. The current resulting from the interface charge flow has long been regarded as an experimental artifact of little interest in the development of traditional charge based biosensors like the ISFET, and has been referred to in the literature as a "leakage current". However, we demonstrate by experimental measurements and theoretical modeling that this current has a component that arises from the rate-limiting transition of a quantum mechanical electronic relaxation event, wherein the electronic tunneling process between a hydrated proton in the electrolyte and the metallic electrode is closely coupled to the bond vibrations of molecular species in the electrolyte. Different strategies to minimize the effect of quantum decoherence in the quantized exchange of energy between the molecular vibrations and electron energy will be discussed, as well as the experimental implications of such strategies. Since the mechanism for the transduction of chemical information is purely electronic and does not require labels or tags or optical transduction, the proposed platform is scalable. Furthermore, it can achieve the chemical specificity typically associated with traditional micro-array or mass spectrometry-based platforms that are used currently to analyze complex biological fluids for trace levels of toxins or pathogen markers.

Automated glycopeptide analysis—review of current state and future directions

PubMed Central

Dallas, David C.; Martin, William F.; Hua, Serenus

2013-01-01

Glycosylation of proteins is involved in immune defense, cell–cell adhesion, cellular recognition and pathogen binding and is one of the most common and complex post-translational modifications. Science is still struggling to assign detailed mechanisms and functions to this form of conjugation. Even the structural analysis of glycoproteins—glycoproteomics—remains in its infancy due to the scarcity of high-throughput analytical platforms capable of determining glycopeptide composition and structure, especially platforms for complex biological mixtures. Glycopeptide composition and structure can be determined with high mass-accuracy mass spectrometry, particularly when combined with chromatographic separation, but the sheer volume of generated data necessitates computational software for interpretation. This review discusses the current state of glycopeptide assignment software—advances made to date and issues that remain to be addressed. The various software and algorithms developed so far provide important insights into glycoproteomics. However, there is currently no freely available software that can analyze spectral data in batch and unambiguously determine glycopeptide compositions for N- and O-linked glycopeptides from relevant biological sources such as human milk and serum. Few programs are capable of aiding in structural determination of the glycan component. To significantly advance the field of glycoproteomics, analytical software and algorithms are required that: (i) solve for both N- and O-linked glycopeptide compositions, structures and glycosites in biological mixtures; (ii) are high-throughput and process data in batches; (iii) can interpret mass spectral data from a variety of sources and (iv) are open source and freely available. PMID:22843980

The State of Software for Evolutionary Biology.

PubMed

Darriba, Diego; Flouri, Tomáš; Stamatakis, Alexandros

2018-05-01

With Next Generation Sequencing data being routinely used, evolutionary biology is transforming into a computational science. Thus, researchers have to rely on a growing number of increasingly complex software. All widely used core tools in the field have grown considerably, in terms of the number of features as well as lines of code and consequently, also with respect to software complexity. A topic that has received little attention is the software engineering quality of widely used core analysis tools. Software developers appear to rarely assess the quality of their code, and this can have potential negative consequences for end-users. To this end, we assessed the code quality of 16 highly cited and compute-intensive tools mainly written in C/C++ (e.g., MrBayes, MAFFT, SweepFinder, etc.) and JAVA (BEAST) from the broader area of evolutionary biology that are being routinely used in current data analysis pipelines. Because, the software engineering quality of the tools we analyzed is rather unsatisfying, we provide a list of best practices for improving the quality of existing tools and list techniques that can be deployed for developing reliable, high quality scientific software from scratch. Finally, we also discuss journal as well as science policy and, more importantly, funding issues that need to be addressed for improving software engineering quality as well as ensuring support for developing new and maintaining existing software. Our intention is to raise the awareness of the community regarding software engineering quality issues and to emphasize the substantial lack of funding for scientific software development.

Proteomic Approaches and Identification of Novel Therapeutic Targets for Alcoholism

PubMed Central

Gorini, Giorgio; Adron Harris, R; Dayne Mayfield, R

2014-01-01

Recent studies have shown that gene regulation is far more complex than previously believed and does not completely explain changes at the protein level. Therefore, the direct study of the proteome, considerably different in both complexity and dynamicity to the genome/transcriptome, has provided unique insights to an increasing number of researchers. During the past decade, extraordinary advances in proteomic techniques have changed the way we can analyze the composition, regulation, and function of protein complexes and pathways underlying altered neurobiological conditions. When combined with complementary approaches, these advances provide the contextual information for decoding large data sets into meaningful biologically adaptive processes. Neuroproteomics offers potential breakthroughs in the field of alcohol research by leading to a deeper understanding of how alcohol globally affects protein structure, function, interactions, and networks. The wealth of information gained from these advances can help pinpoint relevant biomarkers for early diagnosis and improved prognosis of alcoholism and identify future pharmacological targets for the treatment of this addiction. PMID:23900301

Mechanics of metal-catecholate complexes: The roles of coordination state and metal types

PubMed Central

Xu, Zhiping

2013-01-01

There have been growing evidences for the critical roles of metal-coordination complexes in defining structural and mechanical properties of unmineralized biological materials, including hardness, toughness, and abrasion resistance. Their dynamic (e.g. pH-responsive, self-healable, reversible) properties inspire promising applications of synthetic materials following this concept. However, mechanics of these coordination crosslinks, which lays the ground for predictive and rational material design, has not yet been well addressed. Here we present a first-principles study of representative coordination complexes between metals and catechols. The results show that these crosslinks offer stiffness and strength near a covalent bond, which strongly depend on the coordination state and type of metals. This dependence is discussed by analyzing the nature of bonding between metals and catechols. The responsive mechanics of metal-coordination is further mapped from the single-molecule level to a networked material. The results presented here provide fundamental understanding and principles for material selection in metal-coordination-based applications. PMID:24107799

Label-Free Discovery Array Platform for the Characterization of Glycan Binding Proteins and Glycoproteins.

PubMed

Gray, Christopher J; Sánchez-Ruíz, Antonio; Šardzíková, Ivana; Ahmed, Yassir A; Miller, Rebecca L; Reyes Martinez, Juana E; Pallister, Edward; Huang, Kun; Both, Peter; Hartmann, Mirja; Roberts, Hannah N; Šardzík, Robert; Mandal, Santanu; Turnbull, Jerry E; Eyers, Claire E; Flitsch, Sabine L

2017-04-18

The identification of carbohydrate-protein interactions is central to our understanding of the roles of cell-surface carbohydrates (the glycocalyx), fundamental for cell-recognition events. Therefore, there is a need for fast high-throughput biochemical tools to capture the complexity of these biological interactions. Here, we describe a rapid method for qualitative label-free detection of carbohydrate-protein interactions on arrays of simple synthetic glycans, more complex natural glycosaminoglycans (GAG), and lectins/carbohydrate binding proteins using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. The platform can unequivocally identify proteins that are captured from either purified or complex sample mixtures, including biofluids. Identification of proteins bound to the functionalized array is achieved by analyzing either the intact protein mass or, after on-chip proteolytic digestion, the peptide mass fingerprint and/or tandem mass spectrometry of selected peptides, which can yield highly diagnostic sequence information. The platform described here should be a valuable addition to the limited analytical toolbox that is currently available for glycomics.

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.

PubMed

Nielsen, H Bjørn; Almeida, Mathieu; Juncker, Agnieszka Sierakowska; Rasmussen, Simon; Li, Junhua; Sunagawa, Shinichi; Plichta, Damian R; Gautier, Laurent; Pedersen, Anders G; Le Chatelier, Emmanuelle; Pelletier, Eric; Bonde, Ida; Nielsen, Trine; Manichanh, Chaysavanh; Arumugam, Manimozhiyan; Batto, Jean-Michel; Quintanilha Dos Santos, Marcelo B; Blom, Nikolaj; Borruel, Natalia; Burgdorf, Kristoffer S; Boumezbeur, Fouad; Casellas, Francesc; Doré, Joël; Dworzynski, Piotr; Guarner, Francisco; Hansen, Torben; Hildebrand, Falk; Kaas, Rolf S; Kennedy, Sean; Kristiansen, Karsten; Kultima, Jens Roat; Léonard, Pierre; Levenez, Florence; Lund, Ole; Moumen, Bouziane; Le Paslier, Denis; Pons, Nicolas; Pedersen, Oluf; Prifti, Edi; Qin, Junjie; Raes, Jeroen; Sørensen, Søren; Tap, Julien; Tims, Sebastian; Ussery, David W; Yamada, Takuji; Renault, Pierre; Sicheritz-Ponten, Thomas; Bork, Peer; Wang, Jun; Brunak, Søren; Ehrlich, S Dusko

2014-08-01

Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.

Lysine-functionalized nanodiamonds as gene carriers: development of stable colloidal dispersion for in vitro cellular uptake studies and siRNA delivery application

PubMed Central

Alwani, Saniya; Kaur, Randeep; Michel, Deborah; Chitanda, Jackson M; Verrall, Ronald E; Karunakaran, Chithra; Badea, Ildiko

2016-01-01

Purpose Nanodiamonds (NDs) are emerging as an attractive tool for gene therapeutics. To reach their full potential for biological application, NDs should maintain their colloidal stability in biological milieu. This study describes the behavior of lysine-functionalized ND (lys-ND) in various dispersion media, with an aim to limit aggregation and improve the colloidal stability of ND-gene complexes called diamoplexes. Furthermore, cellular and macromolecular interactions of lys-NDs are also analyzed in vitro to establish the understanding of ND-mediated gene transfer in cells. Methods lys-NDs were synthesized earlier through covalent conjugation of lysine amino acid to carboxylated NDs surface generated through re-oxidation in strong oxidizing acids. In this study, dispersions of lys-NDs were prepared in various media, and the degree of sedimentation was monitored for 72 hours. Particle size distributions and zeta potential measurements were performed for a period of 25 days to characterize the physicochemical stability of lys-NDs in the medium. The interaction profile of lys-NDs with fetal bovine serum showed formation of a protein corona, which was evaluated by size and charge distribution measurements. Uptake of lys-NDs in cervical cancer cells was analyzed by scanning transmission X-ray microscopy, flow cytometry, and confocal microscopy. Cellular uptake of diamoplexes (complex of lys-NDs with small interfering RNA) was also analyzed using flow cytometry. Results Aqueous dispersion of lys-NDs showed minimum sedimentation and remained stable over a period of 25 days. Size distributions showed good stability, remaining under 100 nm throughout the testing period. A positive zeta potential of >+20 mV indicated a preservation of surface charges. Size distribution and zeta potential changed for lys-NDs after incubation with blood serum, suggesting an interaction with biomolecules, mainly proteins, and a possible formation of a protein corona. Cellular internalization of lys-NDs was confirmed by various techniques such as confocal microscopy, soft X-ray spectroscopy, and flow cytometry. Conclusion This study establishes that dispersion of lys-NDs in aqueous medium maintains long-term stability and also provides evidence that lysine functionalization enables NDs to interact effectively with the biological system to be used for RNAi therapeutics. PMID:26929623

Unity and disunity in evolutionary sciences: process-based analogies open common research avenues for biology and linguistics.

PubMed

List, Johann-Mattis; Pathmanathan, Jananan Sylvestre; Lopez, Philippe; Bapteste, Eric

2016-08-20

For a long time biologists and linguists have been noticing surprising similarities between the evolution of life forms and languages. Most of the proposed analogies have been rejected. Some, however, have persisted, and some even turned out to be fruitful, inspiring the transfer of methods and models between biology and linguistics up to today. Most proposed analogies were based on a comparison of the research objects rather than the processes that shaped their evolution. Focusing on process-based analogies, however, has the advantage of minimizing the risk of overstating similarities, while at the same time reflecting the common strategy to use processes to explain the evolution of complexity in both fields. We compared important evolutionary processes in biology and linguistics and identified processes specific to only one of the two disciplines as well as processes which seem to be analogous, potentially reflecting core evolutionary processes. These new process-based analogies support novel methodological transfer, expanding the application range of biological methods to the field of historical linguistics. We illustrate this by showing (i) how methods dealing with incomplete lineage sorting offer an introgression-free framework to analyze highly mosaic word distributions across languages; (ii) how sequence similarity networks can be used to identify composite and borrowed words across different languages; (iii) how research on partial homology can inspire new methods and models in both fields; and (iv) how constructive neutral evolution provides an original framework for analyzing convergent evolution in languages resulting from common descent (Sapir's drift). Apart from new analogies between evolutionary processes, we also identified processes which are specific to either biology or linguistics. This shows that general evolution cannot be studied from within one discipline alone. In order to get a full picture of evolution, biologists and linguists need to complement their studies, trying to identify cross-disciplinary and discipline-specific evolutionary processes. The fact that we found many process-based analogies favoring transfer from biology to linguistics further shows that certain biological methods and models have a broader scope than previously recognized. This opens fruitful paths for collaboration between the two disciplines. This article was reviewed by W. Ford Doolittle and Eugene V. Koonin.

Evolution of regulatory networks towards adaptability and stability in a changing environment

NASA Astrophysics Data System (ADS)

Lee, Deok-Sun

2014-11-01

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.

Chemical and biological threat-agent detection using electrophoresis-based lab-on-a-chip devices.

PubMed

Borowsky, Joseph; Collins, Greg E

2007-10-01

The ability to separate complex mixtures of analytes has made capillary electrophoresis (CE) a powerful analytical tool since its modern configuration was first introduced over 25 years ago. The technique found new utility with its application to the microfluidics based lab-on-a-chip platform (i.e., microchip), which resulted in ever smaller footprints, sample volumes, and analysis times. These features, coupled with the technique's potential for portability, have prompted recent interest in the development of novel analyzers for chemical and biological threat agents. This article will comment on three main areas of microchip CE as applied to the separation and detection of threat agents: detection techniques and their corresponding limits of detection, sampling protocol and preparation time, and system portability. These three areas typify the broad utility of lab-on-a-chip for meeting critical, present-day security, in addition to illustrating areas wherein advances are necessary.

A method for validating Rent's rule for technological and biological networks.

PubMed

Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro

2017-07-14

Rent's rule is empirical power law introduced in an effort to describe and optimize the wiring complexity of computer logic graphs. It is known that brain and neuronal networks also obey Rent's rule, which is consistent with the idea that wiring costs play a fundamental role in brain evolution and development. Here we propose a method to validate this power law for a certain range of network partitions. This method is based on the bifurcation phenomenon that appears when the network is subjected to random alterations preserving its degree distribution. It has been tested on a set of VLSI circuits and real networks, including biological and technological ones. We also analyzed the effect of different types of random alterations on the Rentian scaling in order to test the influence of the degree distribution. There are network architectures quite sensitive to these randomization procedures with significant increases in the values of the Rent exponents.

Fluctuation relations between hierarchical kinetically equivalent networks with Arrhenius-type transitions and their roles in systems and structural biology.

PubMed

Deng, De-Ming; Lu, Yi-Ta; Chang, Cheng-Hung

2017-06-01

The legality of using simple kinetic schemes to determine the stochastic properties of a complex system depends on whether the fluctuations generated from hierarchical equivalent schemes are consistent with one another. To analyze this consistency, we perform lumping processes on the stochastic differential equations and the generalized fluctuation-dissipation theorem and apply them to networks with the frequently encountered Arrhenius-type transition rates. The explicit Langevin force derived from those networks enables us to calculate the state fluctuations caused by the intrinsic and extrinsic noises on the free energy surface and deduce their relations between kinetically equivalent networks. In addition to its applicability to wide classes of network related systems, such as those in structural and systems biology, the result sheds light on the fluctuation relations for general physical variables in Keizer's canonical theory.

Testing and Validation of Computational Methods for Mass Spectrometry.

PubMed

Gatto, Laurent; Hansen, Kasper D; Hoopmann, Michael R; Hermjakob, Henning; Kohlbacher, Oliver; Beyer, Andreas

2016-03-04

High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets ( http://compms.org/RefData ) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.

CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis.

PubMed

Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki

2014-09-01

Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.

Lectins from Mycelia of Basidiomycetes

PubMed Central

Nikitina, Valentina E.; Loshchinina, Ekaterina A.; Vetchinkina, Elena P.

2017-01-01

Lectins are proteins of a nonimmunoglobulin nature that are capable of specific recognition of and reversible binding to the carbohydrate moieties of complex carbohydrates, without altering the covalent structure of any of the recognized glycosyl ligands. They have a broad range of biological activities important for the functioning of the cell and the whole organism and, owing to the high specificity of reversible binding to carbohydrates, are valuable tools used widely in biology and medicine. Lectins can be produced by many living organisms, including basidiomycetes. Whereas lectins from the fruit bodies of basidiomycetes have been studied sufficiently well, mycelial lectins remain relatively unexplored. Here, we review and comparatively analyze what is currently known about lectins isolated from the vegetative mycelium of macrobasidiomycetes, including their localization, properties, and carbohydrate specificities. Particular attention is given to the physiological role of mycelial lectins in fungal growth and development. PMID:28640205

Thrombotic Depositions on Right Impeller of Double-Ended Centrifugal Total Artificial Heart In Vivo.

PubMed

Karimov, Jamshid H; Horvath, David J; Okano, Shinji; Goodin, Mark; Sunagawa, Gengo; Byram, Nicole; Moazami, Nader; Golding, Leonard A R; Fukamachi, Kiyotaka

2017-05-01

The development of total artificial heart devices is a complex undertaking that includes chronic biocompatibility assessment of the device. It is considered particularly important to assess whether device design and features can be compatible long term in a biological environment. As part of the development program for the Cleveland Clinic continuous-flow total artificial heart (CFTAH), we evaluated the device for signs of thrombosis and biological material deposition in four animals that had achieved the intended 14-, 30-, or 90-day durations in each respective experiment. Explanted CFTAHs were analyzed for possible clot buildup at "susceptible" areas inside the pump, particularly the right pump impeller. Depositions of various consistency and shapes were observed. We here report our findings, along with macroscopic and microscopic analysis post explant, and provide computational fluid dynamics data with its potential implications for thrombus formation. © 2016 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Daily variations in oligosaccharides of human milk determined by microfluidic chips and mass spectrometry.

PubMed

Niñonuevo, Milady R; Perkins, Patrick D; Francis, Jimi; Lamotte, Latasha M; LoCascio, Riccardo G; Freeman, Samara L; Mills, David A; German, J Bruce; Grimm, Rudolf; Lebrilla, Carlito B

2008-01-23

Human milk is a complex biological fluid that provides not only primary nourishment for infants but also protection against pathogens and influences their metabolic, immunologic, and even cognitive development. The presence of oligosaccharides in remarkable abundance in human milk has been associated to provide diverse biological functions including directing the development of an infant's intestinal microflora and immune system. Recent advances in analytical tools offer invaluable insights in understanding the specific functions and health benefits these biomolecules impart to infants. Oligosaccharides in human milk samples obtained from five different individual donors over the course of a 3 month lactation period were isolated and analyzed using HPLC-Chip/TOF-MS technology. The levels and compositions of oligosaccharides in human milk were investigated from five individual donors. Comparison of HPLC-Chip/TOF-MS oligosaccharides profiles revealed heterogeneity among multiple individuals with no significant variations at different stages of lactation within individual donors.

Detection of Diverse and High Molecular Weight Nesprin-1 and Nesprin-2 Isoforms Using Western Blotting.

PubMed

Carthew, James; Karakesisoglou, Iakowos

2016-01-01

Heavily utilized in cell and molecular biology, western blotting is considered a crucial technique for the detection and quantification of proteins within complex mixtures. In particular, the detection of members of the nesprin (nuclear envelope spectrin repeat protein) family has proven difficult to analyze due to their substantial isoform diversity, molecular weight variation, and the sheer size of both nesprin-1 and nesprin-2 giant protein variants (>800 kDa). Nesprin isoforms contain distinct domain signatures, perform differential cytoskeletal associations, occupy different subcellular compartments, and vary in their tissue expression profiles. This structural and functional variance highlights the need to distinguish between the full range of proteins within the nesprin protein family, allowing for greater understanding of their specific roles in cell biology and disease. Herein, we describe a western blotting protocol modified for the detection of low to high molecular weight (50-1000 kDa) nesprin proteins.

Experiments in Natural and Synthetic Dental Materials: A Mouthful of Experiments

NASA Technical Reports Server (NTRS)

Masi, James V.

1996-01-01

The objectives of these experiments are to show that the area of biomaterials, especially dental materials (natural and synthetic), contain all of the elements of good and bad design, with the caveat that a person's health is directly involved. The students learn the process of designing materials for the complex interactions in the oral cavity, analyze those already used, and suggest possible solutions to the problems involved with present technology. The N.I.O.S.H. Handbook is used extensively by the students and judgement calls are made, even without extensive biology education.

Units of analysis and kinetic structure of behavioral repertoires

PubMed Central

Thompson, Travis; Lubinski, David

1986-01-01

It is suggested that molar streams of behavior are constructed of various arrangements of three elementary constituents (elicited, evoked, and emitted response classes). An eight-cell taxonomy is elaborated as a framework for analyzing and synthesizing complex behavioral repertoires based on these functional units. It is proposed that the local force binding functional units into a smoothly articulated kinetic sequence arises from temporally arranged relative response probability relationships. Behavioral integration is thought to reflect the joint influence of the organism's hierarchy of relative response probabilities, fluctuating biological states, and the arrangement of environmental and behavioral events in time. PMID:16812461

Model for the computation of self-motion in biological systems

NASA Technical Reports Server (NTRS)

Perrone, John A.

1992-01-01

A technique is presented by which direction- and speed-tuned cells, such as those commonly found in the middle temporal region of the primate brain, can be utilized to analyze the patterns of retinal image motion that are generated during observer movement through the environment. The developed model determines heading by finding the peak response in a population of detectors or neurons each tuned to a particular heading direction. It is suggested that a complex interaction of multiple cell networks is required for the solution of the self-motion problem in the primate brain.

[Low-frequency pulsed magnetotherapy combined with electrostimulation of biologically active points in the combined treatment of traumatic mandibular osteomyelitis].

PubMed

Korotkikh, N G; Oreshkin, A V

1999-01-01

The results of treatment are analyzed in 51 patients (35 with exacerbation of chronic traumatic mandibular osteomyelitis and 16 with chronic traumatic mandibular osteomyelitis). Low-intensity pulsed magnetic therapy of the focus in combination with electric stimulation of segmentary bioactive points, synchronized by the patient's pulse, are proposed to be added to the therapeutic complex. Such a modality improved the regional hemodynamics, promoted liquidation of the postoperative edema on days 1-2 after intervention, and sooner than after traditional therapy repaired the energy of the patient's organism.

Chromosome-specific staining to detect genetic rearrangements

DOEpatents

Gray, Joe W.; Pinkel, Daniel; Tkachuk, Douglas; Westbrook, Carol

2013-04-09

Methods and compositions for staining based upon nucleic acid sequence that employ nucleic acid probes are provided. Said methods produce staining patterns that can be tailored for specific cytogenetic analyzes. Said probes are appropriate for in situ hybridization and stain both interphase and metaphase chromosomal material with reliable signals. The nucleic acid probes are typically of a complexity greater than 50 kb, the complexity depending upon the cytogenetic application. Methods and reagents are provided for the detection of genetic rearrangements. Probes and test kits are provided for use in detecting genetic rearrangements, particularly for use in tumor cytogenetics, in the detection of disease related loci, specifically cancer, such as chronic myelogenous leukemia (CML) and for biological dosimetry. Methods and reagents are described for cytogenetic research, for the differentiation of cytogenetically similar but genetically different diseases, and for many prognostic and diagnostic applications.

Method of detecting genetic deletions identified with chromosomal abnormalities

DOEpatents

Gray, Joe W; Pinkel, Daniel; Tkachuk, Douglas

2013-11-26

Methods and compositions for staining based upon nucleic acid sequence that employ nucleic acid probes are provided. Said methods produce staining patterns that can be tailored for specific cytogenetic analyzes. Said probes are appropriate for in situ hybridization and stain both interphase and metaphase chromosomal material with reliable signals. The nucleic acids probes are typically of a complexity greater tha 50 kb, the complexity depending upon the cytogenetic application. Methods and reagents are provided for the detection of genetic rearrangements. Probes and test kits are provided for use in detecting genetic rearrangements, particlularly for use in tumor cytogenetics, in the detection of disease related loci, specifically cancer, such as chronic myelogenous leukemia (CML) and for biological dosimetry. Methods and reagents are described for cytogenetic research, for the differentiation of cytogenetically similar ut genetically different diseases, and for many prognostic and diagnostic applications.

Diorganotin(IV) complexes of biologically potent 4(3H)-quinazolinone derived Schiff bases: synthesis, spectroscopic characterization, DNA interaction studies and antimicrobial activity.

PubMed

Prasad, Kollur Shiva; Kumar, Linganna Shiva; Chandan, Shivamallu; Jayalakshmi, Basvegowda; Revanasiddappa, Hosakere D

2011-10-15

Four Schiff base ligands and their corresponding organotin(IV) complexes have been synthesized and characterized by elemental analyses, IR, (1)H NMR, MS and thermal studies. The Schiff bases are obtained by the condensation of 3-amino-2-methyl-4(3H)-quinazolinone with different substituted aldehydes. The elemental analysis data suggest the stoichiometry to be 1:1 ratio formation. Infrared spectral data agreed with the coordination to the central metal ion through imine nitrogen, lactam oxygen and deprotonated phenolic oxygen atoms. All the synthesized compounds have been evaluated for antimicrobial activity against selected species of microorganisms. In addition, DNA binding/cleavage capacity of the compounds was analyzed by absorption spectroscopy, viscosity measurements and gel electrophoresis methods. Copyright © 2011 Elsevier B.V. All rights reserved.

Entropy production in mesoscopic stochastic thermodynamics: nonequilibrium kinetic cycles driven by chemical potentials, temperatures, and mechanical forces

NASA Astrophysics Data System (ADS)

Qian, Hong; Kjelstrup, Signe; Kolomeisky, Anatoly B.; Bedeaux, Dick

2016-04-01

Nonequilibrium thermodynamics (NET) investigates processes in systems out of global equilibrium. On a mesoscopic level, it provides a statistical dynamic description of various complex phenomena such as chemical reactions, ion transport, diffusion, thermochemical, thermomechanical and mechanochemical fluxes. In the present review, we introduce a mesoscopic stochastic formulation of NET by analyzing entropy production in several simple examples. The fundamental role of nonequilibrium steady-state cycle kinetics is emphasized. The statistical mechanics of Onsager’s reciprocal relations in this context is elucidated. Chemomechanical, thermomechanical, and enzyme-catalyzed thermochemical energy transduction processes are discussed. It is argued that mesoscopic stochastic NET in phase space provides a rigorous mathematical basis of fundamental concepts needed for understanding complex processes in chemistry, physics and biology. This theory is also relevant for nanoscale technological advances.

Communication: Microsecond dynamics of the protein and water affect electron transfer in a bacterial bc1 complex

NASA Astrophysics Data System (ADS)

Martin, Daniel R.; Matyushov, Dmitry V.

2015-04-01

Cross-membrane electron transport between cofactors localized in proteins of mitochondrial respiration and bacterial photosynthesis is the source of all biological energy. The statistics and dynamics of nuclear fluctuations in these protein/membrane/water heterogeneous systems are critical for their energetic efficiency. The results of 13 μs of atomistic molecular dynamics simulations of the membrane-bound bc1 bacterial complex are analyzed here. The reaction is affected by a broad spectrum of nuclear modes, with the slowest dynamics in the range of time-scales ˜0.1-1.6 μs contributing half of the reaction reorganization energy. Two reorganization energies are required to describe protein electron transfer due to dynamical arrest of protein conformations on the observation window. This mechanistic distinction allows significant lowering of activation barriers for reactions in proteins.

UK–South Asian patients’ experiences of and satisfaction toward receiving information about biologics in rheumatoid arthritis

PubMed Central

Kumar, Kanta; Raizada, Sabrina R; Mallen, Christian D; Stack, Rebecca J

2018-01-01

Background Rheumatoid arthritis (RA) causes painful joint inflammation and is incurable, but treatments control RA. Drug regimens are complex, and patients often do not take their medication as expected. Poor medication adherence can lead to poorly controlled disease and worse patient outcomes. Biologics treatments are expensive and require full engagement from patients. We have previously shown that patients from Black ethnic minority backgrounds do not fully engage into treatment plan. This study explored the patients’ experiences in and satisfaction toward receiving information about biologics and future support preferences in South Asian patients with RA. Methods Twenty South Asian patients with RA from Royal Wolverhampton Hospitals NHS Trust and Central Manchester University Hospitals NHS Foundation Trust participated in individual semistructured interviews. Interviews were transcribed and data were analyzed by using thematic analysis approach. Results Four overarching themes describe the patients’ experience in and satisfaction toward receiving information on biologics: 1) current provision of information regarding the “biologics journey” and understanding of RA: in this theme, non-English-speaking patients expressed heightened anxiety about stepping up to biologics; 2) experience and perceptions of biologics: many patients were positive about the biologic experience; however, there were patient-perceived delays in getting on to the biologics; 3) factors influencing willingness to try biologics: in this theme, a number of factors were identified including seeking advice from doctors abroad; and 4) recommendations on the desired information to fully understand the use of biologics: some patients valued group discussions, while others suggested receiving RA and biologic information through a video interaction. Conclusion This novel study provides insight into South Asian RA patients’ experiences in and satisfaction toward receiving information about biologics. South Asian patients with RA reported a range of perceptions about biologics and support preferences, many of which may not be shared with the non-South Asian population. PMID:29670337

Identification of a Conserved Glycan Signature for Microvesicles

PubMed Central

Batista, Bianca S.; Eng, William S.; Pilobello, Kanoelani T.; Hendricks-Muñoz, Karen D.; Mahal, Lara K.

2011-01-01

Microvesicles (exosomes) are important mediators of intercellular communication, playing a role in immune regulation, cancer progression and the spread of infectious agents. The biological functions of these small vesicles are dependent upon their composition, which is regulated by mechanisms that are not well understood. Although numerous proteomic studies of these particles exist, little is known about their glycosylation. Carbohydrates are involved in protein trafficking and cellular recognition. Glycomic analysis may thus provide valuable insights into microvesicle biology. In this study, we analyzed glycosylation patterns of microvesicles derived from a variety of biological sources using lectin microarray technology. Comparison of the microvesicle glycomes with their parent cell membranes revealed both enrichment and depletion of specific glycan epitopes in these particles. These include enrichment in high mannose, polylactosamine, α-2,6 sialic acid, and complex N-linked glycans and exclusion of terminal blood group A and B antigens. The polylactosamine signature derives from distinct glycoprotein cohorts in microvesicles of different origins. Taken together our data point to the emergence of microvesicles from a specific membrane microdomain, implying a role for glycosylation in microvesicle protein sorting. PMID:21859146

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering☆

PubMed Central

Rabitz, Herschel; Welsh, William J.; Kohn, Joachim; de Boer, Jan

2016-01-01

The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. PMID:26876875

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.

The profiling of the metabolites of hirsutine in rat by ultra-high performance liquid chromatography coupled with linear ion trap Orbitrap mass spectrometry: An improved strategy for the systematic screening and identification of metabolites in multi-samples in vivo.

PubMed

Wang, Jianwei; Qi, Peng; Hou, Jinjun; Shen, Yao; Yang, Min; Bi, Qirui; Deng, Yanping; Shi, Xiaojian; Feng, Ruihong; Feng, Zijin; Wu, Wanying; Guo, Dean

2017-02-05

Drug metabolites identification and construction of metabolic profile are meaningful work for the drug discovery and development. The great challenge during this process is the work of the structural clarification of possible metabolites in the complicated biological matrix, which often resulting in a huge amount data sets, especially in multi-samples in vivo. Analyzing these complex data manually is time-consuming and laborious. The object of this study was to develop a practical strategy for screening and identifying of metabolites from multiple biological samples efficiently. Using hirsutine (HTI), an active components of Uncaria rhynchophylla (Gouteng in Chinese) as a model and its plasma, urine, bile, feces and various tissues were analyzed with data processing software (Metwork), data mining tool (Progenesis QI), and HR-MS n data by ultra-high performance liquid chromatography/linear ion trap-Orbitrap mass spectrometry (U-HPLC/LTQ-Orbitrap-MS). A total of 67 metabolites of HTI in rat biological samples were tentatively identified with established library, and to our knowledge most of which were reported for the first time. The possible metabolic pathways were subsequently proposed, hydroxylation, dehydrogenation, oxidation, N-oxidation, hydrolysis, reduction and glucuronide conjugation were mainly involved according to metabolic profile. The result proved application of this improved strategy was efficient, rapid, and reliable for metabolic profiling of components in multiple biological samples and could significantly expand our understanding of metabolic situation of TCM in vivo. Copyright © 2016 Elsevier B.V. All rights reserved.

Discrete structural features among interface residue-level classes.

PubMed

Sowmya, Gopichandran; Ranganathan, Shoba

2015-01-01

Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding. Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes. Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs.

Discrete structural features among interface residue-level classes

PubMed Central

2015-01-01

Background Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding. Results Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes. Conclusions Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs. PMID:26679043

Interactions of platinum metals and their complexes in biological systems.

PubMed Central

LeRoy, A F

1975-01-01

Platinum-metal oxidation catalysts are to be introduced in exhaust systems of many 1975 model-year automobiles in the U.S. to meet Clean Air Act standards. Small quantities of finely divided catalyst have been found issuing from prototype systems; platinum and palladium compounds may be found also. Although platinum exhibits a remarkable resistance to oxidation and chemical attack, it reacts chemically under some conditions producing coordination complex compounds. Palladium reacts more readily than platinum. Some platinum-metal complexes interact with biological systems as bacteriostatic, bacteriocidal, viricidal, and immunosuppressive agents. Workers chronically exposed to platinum complexes often develop asthma-like respiratory distress and skin reactions called platinosis. Platinum complexes used alone and in combination therapy with other drugs have recently emerged as effective agents in cancer chemotherapy. Understanding toxic and favorable interactions of metal species with living organisms requires basic information on quantities and chemical characteristics of complexes at trace concentrations in biological materials. Some basic chemical kinetic and thermodynamic data are presented to characterize the chemical behavior of the complex cis-[Pt(NH3)2Cl2] used therapeutically. A brief discussion of platinum at manogram levels in biological tissue is discussed. PMID:50943

Simultaneous Release and Labeling of O- and N-Glycans Allowing for Rapid Glycomic Analysis by Online LC-UV-ESI-MS/MS.

PubMed

Wang, Chengjian; Lu, Yu; Han, Jianli; Jin, Wanjun; Li, Lingmei; Zhang, Ying; Song, Xuezheng; Huang, Linjuan; Wang, Zhongfu

2018-05-24

Most glycoproteins and biological protein samples undergo both O- and N-glycosylation, making characterization of their structures very complicated and time-consuming. Nevertheless, to fully understand the biological functions of glycosylation, both the glycosylation forms need to be analyzed. Herein we report a versatile, convenient one-pot method in which O- and N-glycans are simultaneously released from glycoproteins and chromogenically labeled in situ and thus available for further characterization. In this procedure, glycoproteins are incubated with 1-phenyl-3-methyl-5-pyrazolone (PMP) in aqueous ammonium hydroxide, making O-glycans released from protein backbones by β-elimination and N-glycans liberated by alkaline hydrolysis. The released glycans are promptly derivatized with PMP in situ by Knoevenagel condensation and Michael addition, with peeling degradation almost completely prevented. The recovered mixture of O- and N-glycans as bis-PMP derivatives features strong ultraviolet (UV) absorbing ability and hydrophobicity, allowing for high-resolution chromatographic separation and high-sensitivity spectrometric detection. Using this technique, O- and N-glycans were simultaneously prepared from some model glycoproteins and complex biological samples, without significant peeling, desialylation, deacetylation, desulfation or other side-reactions, and then comprehensively analyzed by online HILIC-UV-ESI-MS/MS and RP-HPLC-UV-ESI-MS/MS, with which some novel O- and N-glycan structures were first found. This method provides a simple, versatile strategy for high-throughput glycomics analysis.

Self-organized pattern dynamics of somitogenesis model in embryos

NASA Astrophysics Data System (ADS)

Guan, Linan; Shen, Jianwei

2018-09-01

Somitogenesis, the sequential formation of a periodic pattern along the anteroposterior axis of vertebrate embryos, is one of the most obvious examples of the segmental patterning processes that take place during embryogenesis and also one of the major unresolved events in developmental biology. In this paper, we investigate the effect of diffusion on pattern formation use a modified two dimensional model which can be used to explain somitogenesis during embryonic development. This model is suitable for exploring a design space of somitogenesis and can explain many aspects of somitogenesis that previous models cannot. In the present paper, by analyzing the local linear stability of the equation, we acquired the conditions of Hopf bifurcation and Turing bifurcation. In addition, the amplitude equation near the Turing bifurcation point is obtained by using the methods of multi-scale expansion and symmetry analysis. By analyzing the stability of the amplitude equation, we know that there are various complex phenomena, including Spot pattern, mixture of spot-stripe patterns and labyrinthine. Finally, numerical simulation are given to verify the correctness of our theoretical results. Somitogenesis occupies an important position in the process of biological development, and as a pattern process can be used to investigate many aspects of embryogenesis. Therefore, our study helps greatly to cell differentiation, gene expression and embryonic development. What is more, it is of great significance for the diagnosis and treatment of human diseases to study the related knowledge of model biology.

Towards Engineering Biological Systems in a Broader Context.

PubMed

Venturelli, Ophelia S; Egbert, Robert G; Arkin, Adam P

2016-02-27

Significant advances have been made in synthetic biology to program information processing capabilities in cells. While these designs can function predictably in controlled laboratory environments, the reliability of these devices in complex, temporally changing environments has not yet been characterized. As human society faces global challenges in agriculture, human health and energy, synthetic biology should develop predictive design principles for biological systems operating in complex environments. Natural biological systems have evolved mechanisms to overcome innumerable and diverse environmental challenges. Evolutionary design rules should be extracted and adapted to engineer stable and predictable ecological function. We highlight examples of natural biological responses spanning the cellular, population and microbial community levels that show promise in synthetic biology contexts. We argue that synthetic circuits embedded in host organisms or designed ecologies informed by suitable measurement of biotic and abiotic environmental parameters could be used as engineering substrates to achieve target functions in complex environments. Successful implementation of these methods will broaden the context in which synthetic biological systems can be applied to solve important problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Network biology: Describing biological systems by complex networks. Comment on "Network science of biological systems at different scales: A review" by M. Gosak et al.

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2018-03-01

I enjoyed reading Gosak et al. review on analysing biological systems from network science perspective [1]. Network science, first started within Physics community, is now a mature multidisciplinary field of science with many applications ranging from Ecology to biology, medicine, social sciences, engineering and computer science. Gosak et al. discussed how biological systems can be modelled and described by complex network theory which is an important application of network science. Although there has been considerable progress in network biology over the past two decades, this is just the beginning and network science has a great deal to offer to biology and medical sciences.

Exponential evolution: implications for intelligent extraterrestrial life.

PubMed

Russell, D A

1983-01-01

Some measures of biologic complexity, including maximal levels of brain development, are exponential functions of time through intervals of 10(6) to 10(9) yrs. Biological interactions apparently stimulate evolution but physical conditions determine the time required to achieve a given level of complexity. Trends in brain evolution suggest that other organisms could attain human levels within approximately 10(7) yrs. The number (N) and longevity (L) terms in appropriate modifications of the Drake Equation, together with trends in the evolution of biological complexity on Earth, could provide rough estimates of the prevalence of life forms at specified levels of complexity within the Galaxy. If life occurs throughout the cosmos, exponential evolutionary processes imply that higher intelligence will soon (10(9) yrs) become more prevalent than it now is. Changes in the physical universe become less rapid as time increases from the Big Bang. Changes in biological complexity may be most rapid at such later times. This lends a unique and symmetrical importance to early and late universal times.

Integrating genome-wide association study summaries and element-gene interaction datasets identified multiple associations between elements and complex diseases.

PubMed

He, Awen; Wang, Wenyu; Prakash, N Tejo; Tinkov, Alexey A; Skalny, Anatoly V; Wen, Yan; Hao, Jingcan; Guo, Xiong; Zhang, Feng

2018-03-01

Chemical elements are closely related to human health. Extensive genomic profile data of complex diseases offer us a good opportunity to systemically investigate the relationships between elements and complex diseases/traits. In this study, we applied gene set enrichment analysis (GSEA) approach to detect the associations between elements and complex diseases/traits though integrating element-gene interaction datasets and genome-wide association study (GWAS) data of complex diseases/traits. To illustrate the performance of GSEA, the element-gene interaction datasets of 24 elements were extracted from the comparative toxicogenomics database (CTD). GWAS summary datasets of 24 complex diseases or traits were downloaded from the dbGaP or GEFOS websites. We observed significant associations between 7 elements and 13 complex diseases or traits (all false discovery rate (FDR) < 0.05), including reported relationships such as aluminum vs. Alzheimer's disease (FDR = 0.042), calcium vs. bone mineral density (FDR = 0.031), magnesium vs. systemic lupus erythematosus (FDR = 0.012) as well as novel associations, such as nickel vs. hypertriglyceridemia (FDR = 0.002) and bipolar disorder (FDR = 0.027). Our study results are consistent with previous biological studies, supporting the good performance of GSEA. Our analyzing results based on GSEA framework provide novel clues for discovering causal relationships between elements and complex diseases. © 2017 WILEY PERIODICALS, INC.

Analyzing developmental processes on an individual level using nonstationary time series modeling.

PubMed

Molenaar, Peter C M; Sinclair, Katerina O; Rovine, Michael J; Ram, Nilam; Corneal, Sherry E

2009-01-01

Individuals change over time, often in complex ways. Generally, studies of change over time have combined individuals into groups for analysis, which is inappropriate in most, if not all, studies of development. The authors explain how to identify appropriate levels of analysis (individual vs. group) and demonstrate how to estimate changes in developmental processes over time using a multivariate nonstationary time series model. They apply this model to describe the changing relationships between a biological son and father and a stepson and stepfather at the individual level. The authors also explain how to use an extended Kalman filter with iteration and smoothing estimator to capture how dynamics change over time. Finally, they suggest further applications of the multivariate nonstationary time series model and detail the next steps in the development of statistical models used to analyze individual-level data.

Application of clustering methods: Regularized Markov clustering (R-MCL) for analyzing dengue virus similarity

NASA Astrophysics Data System (ADS)

Lestari, D.; Raharjo, D.; Bustamam, A.; Abdillah, B.; Widhianto, W.

2017-07-01

Dengue virus consists of 10 different constituent proteins and are classified into 4 major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and then we analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. Protein interactions that are connected in a tissue, form a complex protein that serves as a specific biological process unit. The analysis of result shows the R-MCL clustering produces clusters of dengue virus family based on the similarity role of their constituent protein, regardless of serotypes.

Synthesis, structural elucidation, biological, antioxidant and nuclease activities of some 5-Fluorouracil-amino acid mixed ligand complexes

NASA Astrophysics Data System (ADS)

Shobana, Sutha; Subramaniam, Perumal; Mitu, Liviu; Dharmaraja, Jeyaprakash; Arvind Narayan, Sundaram

2015-01-01

Some biologically active mixed ligand complexes (1-9) have been synthesized from 5-Fluorouracil (5-FU; A) and amino acids (B) such as glycine (gly), L-alanine (ala) and L-valine (val) with Ni(II), Cu(II) and Zn(II) ions. The synthesized mixed ligand complexes (1-9) were characterized by various physico-chemical, spectral, thermal and morphological studies. 5-Fluorouracil and its mixed ligand complexes have been tested for their in vitro biological activities against some pathogenic bacterial and fungal species by the agar well diffusion method. The in vitro antioxidant activities of 5-Fluorouracil and its complexes have also been investigated by using the DPPH assay method. The results demonstrate that Cu(II) mixed ligand complexes (4-6) exhibit potent biological as well as antioxidant activities compared to 5-Fluorouracil and Ni(II) (1-3) and Zn(II) (7-9) mixed ligand complexes. Further, the cleaving activities of CT DNA under aerobic conditions show moderate activity with the synthesized Cu(II) and Ni(II) mixed ligand complexes (1-6) while no activity is seen with Zn(II) complexes (7-9). Binding studies of CT DNA with these complexes show a decrease in intensity of the charge transfer band to the extent of 5-15% along with a minor red shift. The free energy change values (Δ‡G) calculated from intrinsic binding constants indicate that the interaction between mixed ligand complex and DNA is spontaneous.

In vitro digestive stability of complexes between gliadin and synthetic blocking peptides.

PubMed

Hoffmann, Karolina; Carlsson, Nils-Gunnar; Alminger, Marie; Chen, Tingsu; Wold, Agnes; Olsson, Olof; Sandberg, Ann-Sofie

2011-05-01

Celiac disease is caused by an inappropriate immune response to incompletely digested gluten proteins. We investigated whether synthetic peptides with high affinity to wheat gliadin could be selected with a phage display technique and whether complexes between such peptides and gliadin could sustain gastric and pancreatic digestion. Two synthetic peptides, P61 and P64, were selected because of their high affinity to immobilized gliadin. They were allowed to form complexes with gliadin, whereafter the complexes were subjected to in vitro digestion with gastric and pancreatic enzymes. The digestion products were analyzed with Western blot and RP HPLC. The results showed that both peptides formed stable complexes with intact gliadin and that complexes between gliadin and peptide P64 partly resisted gastrointestinal digestion. The two peptides reduced the binding of serum anti-gliadin IgA antibodies by 12%, and 11.5%, respectively, and the binding of anti-gliadin antibodies of the IgG isotype by 13% and 10%. Thus peptides produced by a phage display technique could interact stably with gliadin partly masking epitopes for antibody binding. A combination of peptides of this kind may be used to block gliadin-immune system interactions. Copyright © 2011 International Union of Biochemistry and Molecular Biology, Inc.

Mono- and Dinuclear Phosphorescent Rhenium(I) Complexes: Impact of Subcellular Localization on Anticancer Mechanisms.

PubMed

Ye, Rui-Rong; Tan, Cai-Ping; Chen, Mu-He; Hao, Liang; Ji, Liang-Nian; Mao, Zong-Wan

2016-06-01

Elucidation of relationship among chemical structure, cellular uptake, localization, and biological activity of anticancer metal complexes is important for the understanding of their mechanisms of action. Organometallic rhenium(I) tricarbonyl compounds have emerged as potential multifunctional anticancer drug candidates that can integrate therapeutic and imaging capabilities in a single molecule. Herein, two mononuclear phosphorescent rhenium(I) complexes (Re1 and Re2), along with their corresponding dinuclear complexes (Re3 and Re4), were designed and synthesized as potent anticancer agents. The subcellular accumulation of Re1-Re4 was conveniently analyzed by confocal microscopy in situ in live cells by utilizing their intrinsic phosphorescence. We found that increased lipophilicity of the bidentate ligands could enhance their cellular uptake, leading to improved anticancer efficacy. The dinuclear complexes were more potent than the mononuclear counterparts. The molecular anticancer mechanisms of action evoked by Re3 and Re4 were explored in detail. Re3 with a lower lipophilicity localizes to lysosomes and induces caspase-independent apoptosis, whereas Re4 with higher lipophilicity specially accumulates in mitochondria and induces caspase-independent paraptosis in cancer cells. Our study demonstrates that subcellular localization is crucial for the anticancer mechanisms of these phosphorescent rhenium(I) complexes. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

How can we improve problem solving in undergraduate biology? Applying lessons from 30 years of physics education research.

PubMed

Hoskinson, A-M; Caballero, M D; Knight, J K

2013-06-01

If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research.

Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics

PubMed Central

Lacy, Robert C.; Miller, Philip S.; Nyhus, Philip J.; Pollak, J. P.; Raboy, Becky E.; Zeigler, Sara L.

2013-01-01

Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological – physical – human systems. We describe a “metamodel” approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples – one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics – to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation. PMID:24349567

Donor Polymorphisms in Genes Related to B-Cell Biology Associated With Antibody-Mediated Rejection After Heart Transplantation.

PubMed

Marrón-Liñares, Grecia M; Núñez, Lucía; Crespo-Leiro, María G; Álvarez-López, Eloy; Barge-Caballero, Eduardo; Barge-Caballero, Gonzalo; Couto-Mallón, David; Pradas-Irun, Concepción; Muñiz, Javier; Tan, Carmela; Rodríguez, E Rene; Vázquez-Rodríguez, José Manuel; Hermida-Prieto, Manuel

2018-04-25

Heart transplantation (HT) is a well-established lifesaving treatment for endstage cardiac failure. Antibody-mediated rejection (AMR) represents one of the main problems after HT because of its diagnostic complexity and the poor evidence for supporting treatments. Complement cascade and B-cells play a key role in AMR and contribute to graft damage. This study explored the importance of variants in genes related to complement pathway and B-cell biology in HT and AMR in donors and in donor-recipient pairs.Methods and Results:Genetic variants in 112 genes (51 complement and 61 B-cell biology genes) were analyzed on next-generation sequencing in 28 donor-recipient pairs, 14 recipients with and 14 recipients without AMR. Statistical analysis was performed with SNPStats, R, and EPIDAT3.1. We identified one single nucleotide polymorphism (SNP) in donors in genes related to B-cell biology,interleukin-4 receptor subunitα (p.Ile75Val-IL4Rα), which correlated with the development of AMR. Moreover, in the analysis of recipient-donor genotype discrepancies, we identified another SNP, in this case inadenosine deaminase(ADA; p.Val178(p=)), which was related to B-cell biology, associated with the absence of AMR. Donor polymorphisms and recipient-donor discrepancies in genes related to the biology of B-cells, could have an important role in the development of AMR. In contrast, no variants in donor or in donor-recipient pairs in complement pathways seem to have an impact on AMR.

Biological design in science classrooms

PubMed Central

Scott, Eugenie C.; Matzke, Nicholas J.

2007-01-01

Although evolutionary biology is replete with explanations for complex biological structures, scientists concerned about evolution education have been forced to confront “intelligent design” (ID), which rejects a natural origin for biological complexity. The content of ID is a subset of the claims made by the older “creation science” movement. Both creationist views contend that highly complex biological adaptations and even organisms categorically cannot result from natural causes but require a supernatural creative agent. Historically, ID arose from efforts to produce a form of creationism that would be less vulnerable to legal challenges and that would not overtly rely upon biblical literalism. Scientists do not use ID to explain nature, but because it has support from outside the scientific community, ID is nonetheless contributing substantially to a long-standing assault on the integrity of science education. PMID:17494747

Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit.

PubMed

Eriksson, O; Brinne, B; Zhou, Y; Björkegren, J; Tegnér, J

2009-03-01

Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein-protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a 'tearing-and-zooming' approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between state-variables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits. [Includes supplementary material].

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces.

PubMed

Bordner, Andrew J; Gorin, Andrey A

2008-05-12

Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB). We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section). Our method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.

From the Director: The Joy of Science, the Courage of Research

MedlinePlus

... Dr. Zerhouni , one that combines an appreciation of biological complexity with the fearless search for scientific knowledge. ... techniques for greater understanding of the complexity of biological systems. The one thing that has driven my ...

Cross-Linking and Mass Spectrometry Methodologies to Facilitate Structural Biology: Finding a Path through the Maze

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

Merkley, Eric D.; Cort, John R.; Adkins, Joshua N.

2013-09-01

Multiprotein complexes, rather than individual proteins, make up a large part of the biological macromolecular machinery of a cell. Understanding the structure and organization of these complexes is critical to understanding cellular function. Chemical cross-linking coupled with mass spectrometry is emerging as a complementary technique to traditional structural biology methods and can provide low-resolution structural information for a multitude of purposes, such as distance constraints in computational modeling of protein complexes. In this review, we discuss the experimental considerations for successful application of chemical cross-linking-mass spectrometry in biological studies and highlight three examples of such studies from the recent literature.more » These examples (as well as many others) illustrate the utility of a chemical cross-linking-mass spectrometry approach in facilitating structural analysis of large and challenging complexes.« less

Implementation of Complexity Analyzing Based on Additional Effect

NASA Astrophysics Data System (ADS)

Zhang, Peng; Li, Na; Liang, Yanhong; Liu, Fang

According to the Complexity Theory, there is complexity in the system when the functional requirement is not be satisfied. There are several study performances for Complexity Theory based on Axiomatic Design. However, they focus on reducing the complexity in their study and no one focus on method of analyzing the complexity in the system. Therefore, this paper put forth a method of analyzing the complexity which is sought to make up the deficiency of the researches. In order to discussing the method of analyzing the complexity based on additional effect, this paper put forth two concepts which are ideal effect and additional effect. The method of analyzing complexity based on additional effect combines Complexity Theory with Theory of Inventive Problem Solving (TRIZ). It is helpful for designers to analyze the complexity by using additional effect. A case study shows the application of the process.

The Evolution of COP9 Signalosome in Unicellular and Multicellular Organisms.

PubMed

Barth, Emanuel; Hübler, Ron; Baniahmad, Aria; Marz, Manja

2016-05-02

The COP9 signalosome (CSN) is a highly conserved protein complex, recently being crystallized for human. In mammals and plants the COP9 complex consists of nine subunits, CSN 1-8 and CSNAP. The CSN regulates the activity of culling ring E3 ubiquitin and plays central roles in pleiotropy, cell cycle, and defense of pathogens. Despite the interesting and essential functions, a thorough analysis of the CSN subunits in evolutionary comparative perspective is missing. Here we compared 61 eukaryotic genomes including plants, animals, and yeasts genomes and show that the most conserved subunits of eukaryotes among the nine subunits are CSN2 and CSN5. This may indicate a strong evolutionary selection for these two subunits. Despite the strong conservation of the protein sequence, the genomic structures of the intron/exon boundaries indicate no conservation at genomic level. This suggests that the gene structure is exposed to a much less selection compared with the protein sequence. We also show the conservation of important active domains, such as PCI (proteasome lid-CSN-initiation factor) and MPN (MPR1/PAD1 amino-terminal). We identified novel exons and alternative splicing variants for all CSN subunits. This indicates another level of complexity of the CSN. Notably, most COP9-subunits were identified in all multicellular and unicellular eukaryotic organisms analyzed, but not in prokaryotes or archaeas. Thus, genes encoding CSN subunits present in all analyzed eukaryotes indicate the invention of the signalosome at the root of eukaryotes. The identification of alternative splice variants indicates possible "mini-complexes" or COP9 complexes with independent subunits containing potentially novel and not yet identified functions. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Rhodium complexes as therapeutic agents.

PubMed

Ma, Dik-Lung; Wang, Modi; Mao, Zhifeng; Yang, Chao; Ng, Chan-Tat; Leung, Chung-Hang

2016-02-21

The landscape of inorganic medicinal chemistry has been dominated by the investigation of platinum, and to a lesser extent ruthenium, complexes over the past few decades. Recently, complexes based on other metal centers such as rhodium have attracted attention due to their tunable chemical and biological properties as well as distinct mechanisms of action. This perspective highlights recent examples of rhodium complexes that show diverse biological activities against various targets, including enzymes and protein-protein interactions.

Physicochemical and biological properties of oxovanadium(IV), cobalt(II) and nickel(II) complexes with oxydiacetate anions.

PubMed

Wyrzykowski, Dariusz; Kloska, Anna; Pranczk, Joanna; Szczepańska, Aneta; Tesmar, Aleksandra; Jacewicz, Dagmara; Pilarski, Bogusław; Chmurzyński, Lech

2015-03-01

The potentiometric and conductometric titration methods have been used to characterize the stability of series of VO(IV)-, Co(II)- and Ni(II)-oxydiacetato complexes in DMSO-water solutions containing 0-50 % (v/v) DMSO. The influence of DMSO as a co-solvent on the stability of the complexes as well as the oxydiacetic acid was evaluated. Furthermore, the reactivity of the complexes towards superoxide free radicals was assessed by employing the nitro blue tetrazolium (NBT) assay. The biological properties of the complexes were investigated in relation to their cytoprotective activity against the oxidative damage generated exogenously by using hydrogen peroxide in the Human Dermal Fibroblasts adult (HDFa) cell line as well as to their antimicrobial activity against the bacteria (Bacillus subtilis, Escherichia coli, Enterococcus faecalis, Pseudomonas aeruginosa, Staphylococcus aureus, Staphylococcus epidermidis). The relationship between physicochemical and biological properties of the complexes was discussed.

Using synthetic biology to make cells tomorrow's test tubes.

PubMed

Garcia, Hernan G; Brewster, Robert C; Phillips, Rob

2016-04-18

The main tenet of physical biology is that biological phenomena can be subject to the same quantitative and predictive understanding that physics has afforded in the context of inanimate matter. However, the inherent complexity of many of these biological processes often leads to the derivation of complex theoretical descriptions containing a plethora of unknown parameters. Such complex descriptions pose a conceptual challenge to the establishment of a solid basis for predictive biology. In this article, we present various exciting examples of how synthetic biology can be used to simplify biological systems and distill these phenomena down to their essential features as a means to enable their theoretical description. Here, synthetic biology goes beyond previous efforts to engineer nature and becomes a tool to bend nature to understand it. We discuss various recent and classic experiments featuring applications of this synthetic approach to the elucidation of problems ranging from bacteriophage infection, to transcriptional regulation in bacteria and in developing embryos, to evolution. In all of these examples, synthetic biology provides the opportunity to turn cells into the equivalent of a test tube, where biological phenomena can be reconstituted and our theoretical understanding put to test with the same ease that these same phenomena can be studied in the in vitro setting.

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

PubMed

Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

2014-01-27

The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W(k)(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation).

Systems biology of eukaryotic superorganisms and the holobiont concept.

PubMed

Kutschera, Ulrich

2018-06-14

The founders of modern biology (Jean Lamarck, Charles Darwin, August Weismann etc.) were organismic life scientists who attempted to understand the morphology and evolution of living beings as a whole (i.e., the phenotype). However, with the emergence of the study of animal and plant physiology in the nineteenth century, this "holistic view" of the living world changed and was ultimately replaced by a reductionistic perspective. Here, I summarize the history of systems biology, i.e., the modern approach to understand living beings as integrative organisms, from genotype to phenotype. It is documented that the physiologists Claude Bernard and Julius Sachs, who studied humans and plants, respectively, were early pioneers of this discipline, which was formally founded 50 years ago. In 1968, two influential monographs, authored by Ludwig von Bertalanffy and Mihajlo D. Mesarović, were published, wherein a "systems theory of biology" was outlined. Definitions of systems biology are presented with reference to metabolic or cell signaling networks, analyzed via genomics, proteomics, and other methods, combined with computer simulations/mathematical modeling. Then, key insights of this discipline with respect to epiphytic microbes (Methylobacterium sp.) and simple bacteria (Mycoplasma sp.) are described. The principles of homeostasis, molecular systems energetics, gnotobiology, and holobionts (i.e., complexities of host-microbiota interactions) are outlined, and the significance of systems biology for evolutionary theories is addressed. Based on the microbe-Homo sapiens-symbiosis, it is concluded that human biology and health should be interpreted in light of a view of the biomedical sciences that is based on the holobiont concept.

Understanding Randomness and its Impact on Student Learning: Lessons Learned from Building the Biology Concept Inventory (BCI)

PubMed Central

Garvin-Doxas, Kathy

2008-01-01

While researching student assumptions for the development of the Biology Concept Inventory (BCI; http://bioliteracy.net), we found that a wide class of student difficulties in molecular and evolutionary biology appears to be based on deep-seated, and often unaddressed, misconceptions about random processes. Data were based on more than 500 open-ended (primarily) college student responses, submitted online and analyzed through our Ed's Tools system, together with 28 thematic and think-aloud interviews with students, and the responses of students in introductory and advanced courses to questions on the BCI. Students believe that random processes are inefficient, whereas biological systems are very efficient. They are therefore quick to propose their own rational explanations for various processes, from diffusion to evolution. These rational explanations almost always make recourse to a driver, e.g., natural selection in evolution or concentration gradients in molecular biology, with the process taking place only when the driver is present, and ceasing when the driver is absent. For example, most students believe that diffusion only takes place when there is a concentration gradient, and that the mutational processes that change organisms occur only in response to natural selection pressures. An understanding that random processes take place all the time and can give rise to complex and often counterintuitive behaviors is almost totally absent. Even students who have had advanced or college physics, and can discuss diffusion correctly in that context, cannot make the transfer to biological processes, and passing through multiple conventional biology courses appears to have little effect on their underlying beliefs. PMID:18519614

Learning contextual gene set interaction networks of cancer with condition specificity

PubMed Central

2013-01-01

Background Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. Results In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations. Conclusions The analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes. PMID:23418942

A cell-based systems biology assessment of human blood to monitor immune responses after influenza vaccination.

PubMed

Hoek, Kristen L; Samir, Parimal; Howard, Leigh M; Niu, Xinnan; Prasad, Nripesh; Galassie, Allison; Liu, Qi; Allos, Tara M; Floyd, Kyle A; Guo, Yan; Shyr, Yu; Levy, Shawn E; Joyce, Sebastian; Edwards, Kathryn M; Link, Andrew J

2015-01-01

Systems biology is an approach to comprehensively study complex interactions within a biological system. Most published systems vaccinology studies have utilized whole blood or peripheral blood mononuclear cells (PBMC) to monitor the immune response after vaccination. Because human blood is comprised of multiple hematopoietic cell types, the potential for masking responses of under-represented cell populations is increased when analyzing whole blood or PBMC. To investigate the contribution of individual cell types to the immune response after vaccination, we established a rapid and efficient method to purify human T and B cells, natural killer (NK) cells, myeloid dendritic cells (mDC), monocytes, and neutrophils from fresh venous blood. Purified cells were fractionated and processed in a single day. RNA-Seq and quantitative shotgun proteomics were performed to determine expression profiles for each cell type prior to and after inactivated seasonal influenza vaccination. Our results show that transcriptomic and proteomic profiles generated from purified immune cells differ significantly from PBMC. Differential expression analysis for each immune cell type also shows unique transcriptomic and proteomic expression profiles as well as changing biological networks at early time points after vaccination. This cell type-specific information provides a more comprehensive approach to monitor vaccine responses.

Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses.

PubMed

Tian, Ye; Wang, Sean S; Zhang, Zhen; Rodriguez, Olga C; Petricoin, Emanuel; Shih, Ie-Ming; Chan, Daniel; Avantaggiati, Maria; Yu, Guoqiang; Ye, Shaozhen; Clarke, Robert; Wang, Chao; Zhang, Bai; Wang, Yue; Albanese, Chris

2014-01-01

Ever growing "omics" data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.

Multiway modeling and analysis in stem cell systems biology

PubMed Central

2008-01-01

Background Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells. Results We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate. Conclusion Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models. PMID:18625054

Protein-ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes.

PubMed

Raschka, Sebastian; Wolf, Alex J; Bemister-Buffington, Joseph; Kuhn, Leslie A

2018-04-01

Understanding how proteins encode ligand specificity is fascinating and similar in importance to deciphering the genetic code. For protein-ligand recognition, the combination of an almost infinite variety of interfacial shapes and patterns of chemical groups makes the problem especially challenging. Here we analyze data across non-homologous proteins in complex with small biological ligands to address observations made in our inhibitor discovery projects: that proteins favor donating H-bonds to ligands and avoid using groups with both H-bond donor and acceptor capacity. The resulting clear and significant chemical group matching preferences elucidate the code for protein-native ligand binding, similar to the dominant patterns found in nucleic acid base-pairing. On average, 90% of the keto and carboxylate oxygens occurring in the biological ligands formed direct H-bonds to the protein. A two-fold preference was found for protein atoms to act as H-bond donors and ligand atoms to act as acceptors, and 76% of all intermolecular H-bonds involved an amine donor. Together, the tight chemical and geometric constraints associated with satisfying donor groups generate a hydrogen-bonding lock that can be matched only by ligands bearing the right acceptor-rich key. Measuring an index of H-bond preference based on the observed chemical trends proved sufficient to predict other protein-ligand complexes and can be used to guide molecular design. The resulting Hbind and Protein Recognition Index software packages are being made available for rigorously defining intermolecular H-bonds and measuring the extent to which H-bonding patterns in a given complex match the preference key.

Protein-ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes

NASA Astrophysics Data System (ADS)

Raschka, Sebastian; Wolf, Alex J.; Bemister-Buffington, Joseph; Kuhn, Leslie A.

2018-02-01

Understanding how proteins encode ligand specificity is fascinating and similar in importance to deciphering the genetic code. For protein-ligand recognition, the combination of an almost infinite variety of interfacial shapes and patterns of chemical groups makes the problem especially challenging. Here we analyze data across non-homologous proteins in complex with small biological ligands to address observations made in our inhibitor discovery projects: that proteins favor donating H-bonds to ligands and avoid using groups with both H-bond donor and acceptor capacity. The resulting clear and significant chemical group matching preferences elucidate the code for protein-native ligand binding, similar to the dominant patterns found in nucleic acid base-pairing. On average, 90% of the keto and carboxylate oxygens occurring in the biological ligands formed direct H-bonds to the protein. A two-fold preference was found for protein atoms to act as H-bond donors and ligand atoms to act as acceptors, and 76% of all intermolecular H-bonds involved an amine donor. Together, the tight chemical and geometric constraints associated with satisfying donor groups generate a hydrogen-bonding lock that can be matched only by ligands bearing the right acceptor-rich key. Measuring an index of H-bond preference based on the observed chemical trends proved sufficient to predict other protein-ligand complexes and can be used to guide molecular design. The resulting Hbind and Protein Recognition Index software packages are being made available for rigorously defining intermolecular H-bonds and measuring the extent to which H-bonding patterns in a given complex match the preference key.

Dissecting social cell biology and tumors using Drosophila genetics.

PubMed

Pastor-Pareja, José Carlos; Xu, Tian

2013-01-01

Cancer was seen for a long time as a strictly cell-autonomous process in which oncogenes and tumor-suppressor mutations drive clonal cell expansions. Research in the past decade, however, paints a more integrative picture of communication and interplay between neighboring cells in tissues. It is increasingly clear as well that tumors, far from being homogenous lumps of cells, consist of different cell types that function together as complex tissue-level communities. The repertoire of interactive cell behaviors and the quantity of cellular players involved call for a social cell biology that investigates these interactions. Research into this social cell biology is critical for understanding development of normal and tumoral tissues. Such complex social cell biology interactions can be parsed in Drosophila. Techniques in Drosophila for analysis of gene function and clonal behavior allow us to generate tumors and dissect their complex interactive biology with cellular resolution. Here, we review recent Drosophila research aimed at understanding tissue-level biology and social cell interactions in tumors, highlighting the principles these studies reveal.

Self-assembly of polyelectrolyte surfactant complexes using large scale MD simulation

NASA Astrophysics Data System (ADS)

Goswami, Monojoy; Sumpter, Bobby

2014-03-01

Polyelectrolytes (PE) and surfactants are known to form interesting structures with varied properties in aqueous solutions. The morphological details of the PE-surfactant complexes depend on a combination of polymer backbone, electrostatic interactions and hydrophobic interactions. We study the self-assembly of cationic PE and anionic surfactants complexes in dilute condition. The importance of such complexes of PE with oppositely charged surfactants can be found in biological systems, such as immobilization of enzymes in polyelectrolyte complexes or nonspecific association of DNA with protein. Many useful properties of PE surfactant complexes come from the highly ordered structures of surfactant self-assembly inside the PE aggregate which has applications in industry. We do large scale molecular dynamics simulation using LAMMPS to understand the structure and dynamics of PE-surfactant systems. Our investigation shows highly ordered pearl-necklace structures that have been observed experimentally in biological systems. We investigate many different properties of PE-surfactant complexation for different parameter ranges that are useful for pharmaceutical, engineering and biological applications.

A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

PubMed

Mehranfar, Adele; Ghadiri, Nasser; Kouhsar, Morteza; Golshani, Ashkan

2017-09-01

Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Complexity: the organizing principle at the interface of biological (dis)order.

PubMed

Bhat, Ramray; Pally, Dharma

2017-07-01

The term complexity means several things to biologists.When qualifying morphological phenotype, on the one hand, it is used to signify the sheer complicatedness of living systems, especially as a result of the multicomponent aspect of biological form. On the other hand, it has been used to represent the intricate nature of the connections between constituents that make up form: a more process-based explanation. In the context of evolutionary arguments, complexity has been defined, in a quantifiable fashion, as the amount of information, an informatic template such as a sequence of nucleotides or amino acids stores about its environment. In this perspective, we begin with a brief review of the history of complexity theory. We then introduce a developmental and an evolutionary understanding of what it means for biological systems to be complex.We propose that the complexity of living systems can be understood through two interdependent structural properties: multiscalarity of interconstituent mechanisms and excitability of the biological materials. The answer to whether a system becomes more or less complex over time depends on the potential for its constituents to interact in novel ways and combinations to give rise to new structures and functions, as well as on the evolution of excitable properties that would facilitate the exploration of interconstituent organization in the context of their microenvironments and macroenvironments.

Biological tracer method

DOEpatents

Strong-Gunderson, Janet M.; Palumbo, Anthony V.

1998-01-01

The present invention is a biological tracer method for characterizing the movement of a material through a medium, comprising the steps of: introducing a biological tracer comprising a microorganism having ice nucleating activity into a medium; collecting at least one sample of the medium from a point removed from the introduction point; and analyzing the sample for the presence of the biological tracer. The present invention is also a method for using a biological tracer as a label for material identification by introducing a biological tracer having ice nucleating activity into a material, collecting a sample of a portion of the labelled material and analyzing the sample for the presence of the biological tracer.

Biological tracer method

DOEpatents

Strong-Gunderson, J.M.; Palumbo, A.V.

1998-09-15

The present invention is a biological tracer method for characterizing the movement of a material through a medium, comprising the steps of: introducing a biological tracer comprising a microorganism having ice nucleating activity into a medium; collecting at least one sample of the medium from a point removed from the introduction point; and analyzing the sample for the presence of the biological tracer. The present invention is also a method for using a biological tracer as a label for material identification by introducing a biological tracer having ice nucleating activity into a material, collecting a sample of a portion of the labelled material and analyzing the sample for the presence of the biological tracer. 2 figs.

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

PubMed

Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

Disentangling life: Darwin, selectionism, and the postgenomic return of the environment.

PubMed

Meloni, Maurizio

2017-04-01

In this paper, I analyze the disruptive impact of Darwinian selectionism for the century-long tradition in which the environment had a direct causative role in shaping an organism's traits. In the case of humans, the surrounding environment often determined not only the physical, but also the mental and moral features of individuals and whole populations. With its apparatus of indirect effects, random variations, and a much less harmonious view of nature and adaptation, Darwinian selectionism severed the deep imbrication of organism and milieu posited by these traditional environmentalist models. This move had radical implications well beyond strictly biological debates. In my essay, I discuss the problematization of the moral idiom of environmentalism by William James and August Weismann who adopted a selectionist view of the development of mental faculties. These debates show the complex moral discourse associated with the environmentalist-selectionist dilemma. They also well illustrate how the moral reverberations of selectionism went well beyond the stereotyped associations with biological fatalism or passivity of the organism. Rereading them today may be helpful as a genealogical guide to the complex ethical quandaries unfolding in the current postgenomic scenario in which a revival of new environmentalist themes is taking place. Copyright © 2017 Elsevier Ltd. All rights reserved.

Biomolecular signatures of diabetic wound healing by structural mass spectrometry

PubMed Central

Hines, Kelly M.; Ashfaq, Samir; Davidson, Jeffrey M.; Opalenik, Susan R.; Wikswo, John P.; McLean, John A.

2013-01-01

Wound fluid is a complex biological sample containing byproducts associated with the wound repair process. Contemporary techniques, such as immunoblotting and enzyme immunoassays, require extensive sample manipulation and do not permit the simultaneous analysis of multiple classes of biomolecular species. Structural mass spectrometry, implemented as ion mobility-mass spectrometry (IM-MS), comprises two sequential, gas-phase dispersion techniques well suited for the study of complex biological samples due to its ability to separate and simultaneously analyze multiple classes of biomolecules. As a model of diabetic wound healing, polyvinyl alcohol (PVA) sponges were inserted subcutaneously into non-diabetic (control) and streptozotocin-induced diabetic rats to elicit a granulation tissue response and to collect acute wound fluid. Sponges were harvested at days 2 or 5 to capture different stages of the early wound healing process. Utilizing IM-MS, statistical analysis, and targeted ultra-performance liquid chromatography (UPLC) analysis, biomolecular signatures of diabetic wound healing have been identified. The protein S100-A8 was highly enriched in the wound fluids collected from day 2 diabetic rats. Lysophosphatidylcholine (20:4) and cholic acid also contributed significantly to the differences between diabetic and control groups. This report provides a generalized workflow for wound fluid analysis demonstrated with a diabetic rat model. PMID:23452326

A Portable Analyzer for Pouch-Actuated, Immunoassay Cassettes

PubMed Central

Qiu, Xianbo; Liu, Changchun; Mauk, Michael G.; Hart, Robert W.; Chen, Dafeng; Qiu, Jing; Kientz, Terry; Fiene, Jonathan; Bau, Haim H.

2011-01-01

A portable, small footprint, light, general purpose analyzer (processor) to control the flow in immunoassay cassettes and to facilitate the detection of test results is described. The durable analyzer accepts disposable cassettes that contain pouches and reaction chambers for various unit operations such as hydration of dry reagents, stirring, and incubation. The analyzer includes individually controlled, linear actuators to compress the pouches in the cassette, which facilitates the pumping and mixing of sample and reagents, and to close diaphragm-based valves for flow control. The same types of actuators are used to compress pouches and actuate valves. The analyzer also houses a compact OEM scanner/reader to excite fluorescence and detect emission from labels. The analyzer is hydraulically isolated from the cassette, reducing the possibility of cross-contamination. The analyzer facilitates programmable, automated execution of a sequence of operations such as pumping and valving in a timely fashion, reducing the level of expertise required from the operator and the possibility for errors. The analyzer’s design is modular and expandable to accommodate cassettes of various complexities and additional functionalities. In this paper, the utility of the analyzer has been demonstrated with the execution of a simple, consecutive, lateral flow assay of a model biological system and the test results were detected with up converting phosphor labels that are excited at infrared frequencies and emit in the visible spectrum. PMID:22125359

Screening the efficient biological prospects of triazole allied mixed ligand metal complexes

NASA Astrophysics Data System (ADS)

Utthra, Ponnukalai Ponya; Kumaravel, Ganesan; Raman, Natarajan

2017-12-01

Triazole appended mixed ligand complexes (1-8) of the general formula [ML (bpy/phen)2]Cl2, where M = Cu(II), Co(II), Ni(II) and Zn(II), L = triazole appended Schiff base (E)sbnd N-(4-nitrobenzylidene)-1H-1,2,4-triazol-3-amine and bpy/phen = 2,2‧-bipyridine/1,10-phenanthroline, have been synthesized. The design and synthesis of this elaborate ligand has been performed with the aim of increasing stability and conjugation of 1,2,4 triazole, whose Schiff base derivatives are known as biologically active compounds thereby exploring their DNA binding affinity and other biological applications. The compounds have been comprehensively characterized by elemental analysis, spectroscopic methods (IR, UV-Vis, EPR, 1H and 13C NMR spectroscopy), ESI mass spectrometry and magnetic susceptibility measurements. The complexes were found to exhibit octahedral geometry. The complexes 1-8 were subjected to DNA binding techniques evaluated using UV-Vis absorption, CV, CD, Fluorescence spectroscopy and hydrodynamic measurements. Complex 5 showed a Kb value of 3.9 × 105 M-1. The DNA damaging efficacy for the complexes was observed to be high compared to the ligand. The antimicrobial screening of the compounds against bacterial and fungal strains indicates that the complexes possess excellent antimicrobial activity than the ligand. The overall biological activity of the complexes with phen as a co-ligand possessed superior potential than the ligand.

Optimizing Nutrient Uptake in Biological Transport Networks

NASA Astrophysics Data System (ADS)

Ronellenfitsch, Henrik; Katifori, Eleni

2013-03-01

Many biological systems employ complex networks of vascular tubes to facilitate transport of solute nutrients, examples include the vascular system of plants (phloem), some fungi, and the slime-mold Physarum. It is believed that such networks are optimized through evolution for carrying out their designated task. We propose a set of hydrodynamic governing equations for solute transport in a complex network, and obtain the optimal network architecture for various classes of optimizing functionals. We finally discuss the topological properties and statistical mechanics of the resulting complex networks, and examine correspondence of the obtained networks to those found in actual biological systems.

Cloning and characterization of Sdga gene encoding alpha-subunit of heterotrimeric guanosine 5'-triphosphate-binding protein complex in Scoparia dulcis.

PubMed

Shite, Masato; Yamamura, Yoshimi; Hayashi, Toshimitsu; Kurosaki, Fumiya

2008-11-01

A homology-based cloning strategy yielded Sdga, a cDNA clone presumably encoding alpha-subunit of heterotrimeric guanosine 5'-triphosphate-binding protein complex, from leaf tissues of Scoparia dulcis. Phylogenetic tree analysis of G-protein alpha-subunits from various biological sources suggested that, unlike in animal cells, classification of Galpha-proteins into specific subfamilies could not be applicable to the proteins from higher plants. Restriction digests of genomic DNA of S. dulcis showed a single hybridized signal in Southern blot analysis, suggesting that Sdga is a sole gene encoding Galpha-subunit in this plant. The expression level of Sdga appeared to be maintained at almost constant level after exposure of the leaves to methyl jasmonate as analyzed by reverse-transcription polymerase chain reaction. These results suggest that Sdga plays roles in methyl jasmonate-induced responses of S. dulcis without a notable change in the transcriptional level.

Abiogenic synthesis of nucleotides on the surface of small space bodies with high energy particles

NASA Astrophysics Data System (ADS)

Simakov, M. B.; Kuzicheva, E. A.; Antropov, A. E.; Dodonova, N. Ya

Abiotic formation of such complex biochemical compounds as nucleotides and oligopeptides on the surface of interstellar and interplanetary dust particles (IDP) by cosmic radiation was examined. In order to study the formation of organic compounds on IDPs, solid films prepared from nucleososide and inorganic phosphate were irradiated with high energy protons. Irradiated products were analyzed with HPLC. The natural nucleotides were detected. The main products were 5' AMP (3.2%) and 2'3' cAMP (2.7%). The results were compared with others experiments on the action of ultraviolet radiation with different wavelengths, γ-radiation and heat on solid mixtures of biologically significant compounds. The experiment on abiogenic synthesis of nucleotides on board of space satellite "BION-11" was compared also. The present results suggest that a considerable amount of complex biochemical compounds formed in extraterrestrial environments could have been supplied to the primitive earth before the origin of life.

Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal

PubMed Central

Gao, Jianjiong; Aksoy, Bülent Arman; Dogrusoz, Ugur; Dresdner, Gideon; Gross, Benjamin; Sumer, S. Onur; Sun, Yichao; Jacobsen, Anders; Sinha, Rileen; Larsson, Erik; Cerami, Ethan; Sander, Chris; Schultz, Nikolaus

2014-01-01

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics. PMID:23550210

Genetic fallout in biocultural landscapes: molecular imperialism and the cultural politics of (not) seeing transgenes in Mexico.

PubMed

Christophe Bonneuil; Foyer, Jean; Wynne, Brian

2014-12-01

This article explores the trajectory of the global controversy over the introgression (or not) of transgenes from genetically modified maize into Mexican indigenous maize landraces. While a plurality of knowledge-making processes were deployed to render transgenes visible or invisible, we analyze how a particular in vitro based DNA-centered knowledge came to marginalize other forms of knowledge, thus obscuring other bio-cultural dimensions key to the understanding of gene flow and maize diversity. We show that dominant molecular norms of proof and standards of detection, which co-developed with the world of industrial monocropping and gene patenting, discarded and externalized non-compliant actors (i.e. complex maize genomes, human dimensions of gene flow). Operating in the name of high science, they hence obscured the complex biological and cultural processes that maintain crop diversity and enacted a cultural-political domination over the world of Mexican landraces and indigenous communities.

Formulation, optimization and evaluation of curcumin-β-cyclodextrin-loaded sponge for effective drug delivery in thermal burns chemotherapy.

PubMed

Kaur, Navdeep; Garg, Tarun; Goyal, Amit K; Rath, Goutam

2016-09-01

The present study was designed to determine the role of curcumin-β-cyclodextrin-loaded sponge on burn wound healing in rats. Curcumin-β-cyclodextrin complex was prepared by the solvent evaporation encapsulation method. Molecular inclusion complex of curcumin-β-cyclodextrin was incorporated into gelatin sponge. The developed sponge was characterized for drug entrapment, drug release and morphology. The biological activity of optimized formulation was determined on burn wounds which were made on rats. The burn wound healing efficacy was analyzed through physical and histological changes observed at the wound sites. There was a significant decrease in rate of wound contraction in experimental groups then the control group. Curcumin-β-cyclodextrin-loaded sponge treated wound was found to heal in rate comparable to marketed formulation with no sign of adverse consequence. The result clearly substantiates the beneficial effects of curcumin-β-cyclodextrin-loaded sponge in the acceleration of wound healing.

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

Zhang, Xing; Ibrahim, Yehia M.; Chen, Tsung-Chi

We report the first evaluation of a platform coupling a high speed field asymmetric ion mobility spectrometry microchip (µFAIMS) with drift tube ion mobility and mass spectrometry (IMS-MS). The µFAIMS/IMS-MS platform was used to analyze biological samples and simultaneously acquire multidimensional information of detected features from the measured FAIMS compensation fields and IMS drift times, while also obtaining accurate ion masses. These separations thereby increase the overall separation power, resulting increased information content, and provide more complete characterization of more complex samples. The separation conditions were optimized for sensitivity and resolving power by the selection of gas compositions and pressuresmore » in the FAIMS and IMS separation stages. The resulting performance provided three dimensional separations, benefitting both broad complex mixture studies and targeted analyses by e.g. improving isomeric separations and allowing detection of species obscured by “chemical noise” and other interfering peaks.« less

Ion specific correlations in bulk and at biointerfaces.

PubMed

Kalcher, I; Horinek, D; Netz, R R; Dzubiella, J

2009-10-21

Ion specific effects are ubiquitous in any complex colloidal or biological fluid in bulk or at interfaces. The molecular origins of these 'Hofmeister effects' are not well understood and their theoretical description poses a formidable challenge to the modeling and simulation community. On the basis of the combination of atomistically resolved molecular dynamics (MD) computer simulations and statistical mechanics approaches, we present a few selected examples of specific electrolyte effects in bulk, at simple neutral and charged interfaces, and on a short α-helical peptide. The structural complexity in these strongly Coulomb-correlated systems is highlighted and analyzed in the light of available experimental data. While in general the comparison of MD simulations to experiments often lacks quantitative agreement, mostly because molecular force fields and coarse-graining procedures remain to be optimized, the consensus as regards trends provides important insights into microscopic hydration and binding mechanisms.

Schiff bases in medicinal chemistry: a patent review (2010-2015).

PubMed

Hameed, Abdul; Al-Rashida, Mariya; Uroos, Maliha; Abid Ali, Syed; Khan, Khalid Mohammed

2017-01-01

Schiff bases are synthetically accessible and structurally diverse compounds, typically obtained by facile condensation between an aldehyde, or a ketone with primary amines. Schiff bases contain an azomethine (-C = N-) linkage that stitches together two or more biologically active aromatic/heterocyclic scaffolds to form various molecular hybrids with interesting biological properties. Schiff bases are versatile metal complexing agents and have been known to coordinate all metals to form stable metal complexes with vast therapeutic applications. Areas covered: This review aims to provide a comprehensive overview of the various patented therapeutic applications of Schiff bases and their metal complexes from 2010 to 2015. Expert opinion: Schiff bases are a popular class of compounds with interesting biological properties. Schiff bases are also versatile metal complexing ligands and have been used to coordinate almost all d-block metals as well as lanthanides. Therapeutically, Schiff bases and their metal complexes have been reported to exhibit a wide range of biological activities such as antibacterial including antimycobacterial, antifungal, antiviral, antimalarial, antiinflammatory, antioxidant, pesticidal, cytotoxic, enzyme inhibitory, and anticancer including DNA damage.

Separation and screening of short-chain chlorinated paraffins in environmental samples using comprehensive two-dimensional gas chromatography with micro electron capture detection.

PubMed

Xia, Dan; Gao, Lirong; Zhu, Shuai; Zheng, Minghui

2014-11-01

Short-chain chlorinated paraffins (SCCPs) are highly complex technical mixtures with thousands of isomers and numerous homologs. They are classified as priority candidate persistent organic pollutants under the Stockholm Convention for their persistence, bioaccumulation, and toxicity. Analyzing SCCPs is challenging because of the complexity of the mixtures. Chromatograms of SCCPs acquired using one-dimensional (1D) gas chromatography (GC) contain a large characteristic "peak" with a broad and unresolved profile. Comprehensive two-dimensional GC (GC×GC) shows excellent potential for separating complex mixtures. In this study, GC×GC coupled with micro electron capture detection (μECD) was used to separate and screen SCCPs. The chromatographic parameters, including the GC column types, oven temperature program, and modulation period, were systematically optimized. The SCCP congeners were separated into groups using a DM-1 column connected to a BPX-50 column. The SCCP congeners in technical mixtures were separated according to the number of chlorine substituents for a given carbon chain length and according to the number of carbon atoms plus chlorine atoms for different carbon chain lengths. A fish tissue sample was analyzed to illustrate the feasibility of the GC×GC-μECD method in analyzing biological samples. Over 1,500 compounds were identified in the fish extract, significantly more than were identified using 1D GC. The detection limits for five selected SCCP congeners were between 1 and 5 pg/L using the GC×GC method, and these were significantly lower than those achieved using 1D GC. This method is a good choice for analysis of SCCPs in environmental samples, exhibiting good separation and good sensitivity.

How Can We Improve Problem Solving in Undergraduate Biology? Applying Lessons from 30 Years of Physics Education Research

PubMed Central

Hoskinson, A.-M.; Caballero, M. D.; Knight, J. K.

2013-01-01

If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research. PMID:23737623

The multi-replication protein A (RPA) system--a new perspective.

PubMed

Sakaguchi, Kengo; Ishibashi, Toyotaka; Uchiyama, Yukinobu; Iwabata, Kazuki

2009-02-01

Replication protein A (RPA) complex has been shown, using both in vivo and in vitro approaches, to be required for most aspects of eukaryotic DNA metabolism: replication, repair, telomere maintenance and homologous recombination. Here, we review recent data concerning the function and biological importance of the multi-RPA complex. There are distinct complexes of RPA found in the biological kingdoms, although for a long time only one type of RPA complex was believed to be present in eukaryotes. Each complex probably serves a different role. In higher plants, three distinct large and medium subunits are present, but only one species of the smallest subunit. Each of these protein subunits forms stable complexes with their respective partners. They are paralogs as complex. Humans possess two paralogs and one analog of RPA. The multi-RPA system can be regarded as universal in eukaryotes. Among eukaryotic kingdoms, paralogs, orthologs, analogs and heterologs of many DNA synthesis-related factors, including RPA, are ubiquitous. Convergent evolution seems to be ubiquitous in these processes. Using recent findings, we review the composition and biological functions of RPA complexes.

Financial implications of ventral hernia repair: a hospital cost analysis.

PubMed

Reynolds, Drew; Davenport, Daniel L; Korosec, Ryan L; Roth, J Scott

2013-01-01

Complicated ventral hernias are often referred to tertiary care centers. Hospital costs associated with these repairs include direct costs (mesh materials, supplies, and nonsurgeon labor costs) and indirect costs (facility fees, equipment depreciation, and unallocated labor). Operative supplies represent a significant component of direct costs, especially in an era of proprietary synthetic meshes and biologic grafts. We aim to evaluate the cost-effectiveness of complex abdominal wall hernia repair at a tertiary care referral facility. Cost data on all consecutive open ventral hernia repairs (CPT codes 49560, 49561, 49565, and 49566) performed between 1 July 2008 and 31 May 2011 were analyzed. Cases were analyzed based upon hospital status (inpatient vs. outpatient) and whether the hernia repair was a primary or secondary procedure. We examined median net revenue, direct costs, contribution margin, indirect costs, and net profit/loss. Among primary hernia repairs, cost data were further analyzed based upon mesh utilization (no mesh, synthetic, or biologic). Four-hundred and fifteen patients underwent ventral hernia repair (353 inpatients and 62 outpatients); 173 inpatients underwent ventral hernia repair as the primary procedure; 180 inpatients underwent hernia repair as a secondary procedure. Median net revenue ($17,310 vs. 10,360, p < 0.001) and net losses (3,430 vs. 1,700, p < 0.025) were significantly greater for those who underwent hernia repair as a secondary procedure. Among inpatients undergoing ventral hernia repair as the primary procedure, 46 were repaired without mesh; 79 were repaired with synthetic mesh and 48 with biologic mesh. Median direct costs for cases performed without mesh were $5,432; median direct costs for those using synthetic and biologic mesh were $7,590 and 16,970, respectively (p < .01). Median net losses for repairs without mesh were $500. Median net profit of $60 was observed for synthetic mesh-based repairs. The median contribution margin for cases utilizing biologic mesh was -$4,560, and the median net financial loss was $8,370. Outpatient ventral hernia repairs, with and without synthetic mesh, resulted in median net losses of $1,560 and 230, respectively. Ventral hernia repair is associated with overall financial losses. Inpatient synthetic mesh repairs are essentially budget neutral. Outpatient and inpatient repairs without mesh result in net financial losses. Inpatient biologic mesh repairs result in a negative contribution margin and striking net financial losses. Cost-effective strategies for managing ventral hernias in a tertiary care environment need to be developed in light of the financial implications of this patient population.

The State of Software for Evolutionary Biology

PubMed Central

Darriba, Diego; Flouri, Tomáš; Stamatakis, Alexandros

2018-01-01

Abstract With Next Generation Sequencing data being routinely used, evolutionary biology is transforming into a computational science. Thus, researchers have to rely on a growing number of increasingly complex software. All widely used core tools in the field have grown considerably, in terms of the number of features as well as lines of code and consequently, also with respect to software complexity. A topic that has received little attention is the software engineering quality of widely used core analysis tools. Software developers appear to rarely assess the quality of their code, and this can have potential negative consequences for end-users. To this end, we assessed the code quality of 16 highly cited and compute-intensive tools mainly written in C/C++ (e.g., MrBayes, MAFFT, SweepFinder, etc.) and JAVA (BEAST) from the broader area of evolutionary biology that are being routinely used in current data analysis pipelines. Because, the software engineering quality of the tools we analyzed is rather unsatisfying, we provide a list of best practices for improving the quality of existing tools and list techniques that can be deployed for developing reliable, high quality scientific software from scratch. Finally, we also discuss journal as well as science policy and, more importantly, funding issues that need to be addressed for improving software engineering quality as well as ensuring support for developing new and maintaining existing software. Our intention is to raise the awareness of the community regarding software engineering quality issues and to emphasize the substantial lack of funding for scientific software development. PMID:29385525

Evaluating Microbial Indicators of Environmental Condition in Oregon Rivers

NASA Astrophysics Data System (ADS)

Pennington, Alan T.; Harding, Anna K.; Hendricks, Charles W.; Campbell, Heidi M. K.

2001-12-01

Traditional bacterial indicators used in public health to assess water quality and the Biolog® system were evaluated to compare their response to biological, chemical, and physical habitat indicators of stream condition both within the state of Oregon and among ecoregion aggregates (Coast Range, Willamette Valley, Cascades, and eastern Oregon). Forty-three randomly selected Oregon river sites were sampled during the summer in 1997 and 1998. The public health indicators included heterotrophic plate counts (HPC), total coliforms (TC), fecal coliforms (FC) and Escherichia coli (EC). Statewide, HPC correlated strongly with physical habitat (elevation, riparian complexity, % canopy presence, and indices of agriculture, pavement, road, pasture, and total disturbance) and chemistry (pH, dissolved O2, specific conductance, acid-neutralizing capacity, dissolved organic carbon, total N, total P, SiO2, and SO4). FC and EC were significantly correlated generally with the river chemistry indicators. TC bacteria significantly correlated with riparian complexity, road disturbance, dissolved O2, and SiO2 and FC. Analyzing the sites by ecoregion, eastern Oregon was characterized by high HPC, FC, EC, nutrient loads, and indices of human disturbance, whereas the Cascades ecoregion had correspondingly low counts of these indicators. The Coast Range and Willamette Valley presented inconsistent indicator patterns that are more difficult to characterize. Attempts to distinguish between ecoregions with the Biolog system were not successful, nor did a statistical pattern emerge between the first five principle components and the other environmental indicators. Our research suggests that some traditional public health microbial indicators may be useful in measuring the environmental condition of lotic systems.

Dendritic Cells in the Context of Human Tumors: Biology and Experimental Tools.

PubMed

Volovitz, Ilan; Melzer, Susanne; Amar, Sarah; Bocsi, József; Bloch, Merav; Efroni, Sol; Ram, Zvi; Tárnok, Attila

2016-01-01

Dendritic cells (DC) are the most potent and versatile antigen-presenting cells (APC) in the immune system. DC have an exceptional ability to comprehend the immune context of a captured antigen based on molecular signals identified from its vicinity. The analyzed information is then conveyed to other immune effector cells. Such capability enables DC to play a pivotal role in mediating either an immunogenic response or immune tolerance towards an acquired antigen. This review summarizes current knowledge on DC in the context of human tumors. It covers the basics of human DC biology, elaborating on the different markers, morphology and function of the different subsets of human DC. Human blood-borne DC are comprised of at least three subsets consisting of one plasmacytoid DC (pDC) and two to three myeloid DC (mDC) subsets. Some tissues have unique DC. Each subset has a different phenotype and function and may induce pro-tumoral or anti-tumoral effects. The review also discusses two methods fundamental to the research of DC on the single-cell level: multicolor flow cytometry (FCM) and image-based cytometry (IC). These methods, along with new genomics and proteomics tools, can provide high-resolution information on specific DC subsets and on immune and tumor cells with which they interact. The different layers of collected biological data may then be integrated using Immune-Cytomics modeling approaches. Such novel integrated approaches may help unravel the complex network of cellular interactions that DC carry out within tumors, and may help harness this complex immunological information into the development of more effective treatments for cancer.

Genomic survey, expression profile and co-expression network analysis of OsWD40 family in rice

PubMed Central

2012-01-01

Background WD40 proteins represent a large family in eukaryotes, which have been involved in a broad spectrum of crucial functions. Systematic characterization and co-expression analysis of OsWD40 genes enable us to understand the networks of the WD40 proteins and their biological processes and gene functions in rice. Results In this study, we identify and analyze 200 potential OsWD40 genes in rice, describing their gene structures, genome localizations, and evolutionary relationship of each member. Expression profiles covering the whole life cycle in rice has revealed that transcripts of OsWD40 were accumulated differentially during vegetative and reproductive development and preferentially up or down-regulated in different tissues. Under phytohormone treatments, 25 OsWD40 genes were differentially expressed with treatments of one or more of the phytohormone NAA, KT, or GA3 in rice seedlings. We also used a combined analysis of expression correlation and Gene Ontology annotation to infer the biological role of the OsWD40 genes in rice. The results suggested that OsWD40 genes may perform their diverse functions by complex network, thus were predictive for understanding their biological pathways. The analysis also revealed that OsWD40 genes might interact with each other to take part in metabolic pathways, suggesting a more complex feedback network. Conclusions All of these analyses suggest that the functions of OsWD40 genes are diversified, which provide useful references for selecting candidate genes for further functional studies. PMID:22429805

Evaluation of colorimetric assays for analyzing reductively methylated proteins: Biases and mechanistic insights.

PubMed

Brady, Pamlea N; Macnaughtan, Megan A

2015-12-15

Colorimetric protein assays, such as the Coomassie blue G-250 dye-binding (Bradford) and bicinchoninic acid (BCA) assays, are commonly used to quantify protein concentration. The accuracy of these assays depends on the amino acid composition. Because of the extensive use of reductive methylation in the study of proteins and the importance of biological methylation, it is necessary to evaluate the impact of lysyl methylation on the Bradford and BCA assays. Unmodified and reductively methylated proteins were analyzed using the absorbance at 280 nm to standardize the concentrations. Using model compounds, we demonstrate that the dimethylation of lysyl ε-amines does not affect the proteins' molar extinction coefficients at 280 nm. For the Bradford assay, the responses (absorbance per unit concentration) of the unmodified and reductively methylated proteins were similar, with a slight decrease in the response upon methylation. For the BCA assay, the responses of the reductively methylated proteins were consistently higher, overestimating the concentrations of the methylated proteins. The enhanced color formation in the BCA assay may be due to the lower acid dissociation constants of the lysyl ε-dimethylamines compared with the unmodified ε-amine, favoring Cu(II) binding in biuret-like complexes. The implications for the analysis of biologically methylated samples are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Evaluation of Colorimetric Assays for Analyzing Reductively Methylated Proteins: Biases and Mechanistic Insights

PubMed Central

Brady, Pamlea N.; Macnaughtan, Megan A.

2015-01-01

Colorimetric protein assays, such as the Coomassie blue G-250 dye-binding (Bradford) and bicinchoninic acid (BCA) assays, are commonly used to quantify protein concentration. The accuracy of these assays depends on the amino acid composition. Because of the extensive use of reductive methylation in the study of proteins and the importance of biological methylation, it is necessary to evaluate the impact of lysyl methylation on the Bradford and BCA assays. Unmodified and reductively methylated proteins were analyzed using the absorbance at 280 nm to standardize the concentrations. Using model compounds, we demonstrate that the dimethylation of lysyl ε-amines does not affect the proteins’ molar extinction coefficients at 280 nm. For the Bradford assay, the response (absorbance per unit concentration) of the unmodified and reductively methylated proteins were similar with a slight decrease in the response upon methylation. For the BCA assay, the responses of the reductively methylated proteins were consistently higher, overestimating the concentrations of the methylated proteins. The enhanced color-formation in the BCA assay may be due to the lower acid dissociation constants of the lysyl ε-dimethylamines, compared to the unmodified ε-amine, favoring Cu(II) binding in biuret-like complexes. The implications for the analysis of biologically methylated samples are discussed. PMID:26342307

Synthetic biology: programming cells for biomedical applications.

PubMed

Hörner, Maximilian; Reischmann, Nadine; Weber, Wilfried

2012-01-01

The emerging field of synthetic biology is a novel biological discipline at the interface between traditional biology, chemistry, and engineering sciences. Synthetic biology aims at the rational design of complex synthetic biological devices and systems with desired properties by combining compatible, modular biological parts in a systematic manner. While the first engineered systems were mainly proof-of-principle studies to demonstrate the power of the modular engineering approach of synthetic biology, subsequent systems focus on applications in the health, environmental, and energy sectors. This review describes recent approaches for biomedical applications that were developed along the synthetic biology design hierarchy, at the level of individual parts, of devices, and of complex multicellular systems. It describes how synthetic biological parts can be used for the synthesis of drug-delivery tools, how synthetic biological devices can facilitate the discovery of novel drugs, and how multicellular synthetic ecosystems can give insight into population dynamics of parasites and hosts. These examples demonstrate how this new discipline could contribute to novel solutions in the biopharmaceutical industry.

Using Stereoscopy to Teach Complex Biological Concepts

ERIC Educational Resources Information Center

Ferdig, Richard; Blank, James; Kratcoski, Annette; Clements, Robert

2015-01-01

Used effectively, stereoscopic three-dimensional (3D) technologies can engage students with complex disciplinary content as they are presented with informative representations of abstract concepts. In addition, preliminary evidence suggests that stereoscopy may enhance learning and retention in some educational settings. Biological concepts…

Rapid ionic liquid-based ultrasound assisted dual magnetic microextraction to preconcentrate and separate cadmium-4-(2-thiazolylazo)-resorcinol complex from environmental and biological samples.

PubMed

Khan, Sumaira; Kazi, Tasneem Gul; Soylak, Mustafa

2014-04-05

A rapid and innovative microextraction technique named as, ionic liquid-based ultrasound-assisted dual magnetic microextraction (IL-UA-DMME) was developed for the preconcentration and extraction of trace cadmium from environmental and biological samples, prior to analyzed by flame atomic absorption spectrometry (FAAS). The proposed method has many obvious advantages, including evading the use of organic solvents and achieved high extraction yields by the combination of dispersive liquid-liquid microextraction (DLLME) and magnetic mediated-solid phase extraction (MM-SPE). In this approach ionic liquid (IL) 1-butyl-3-methylimidazolium hexafluorophosphate [C4mim][PF6] play an important role to extract the cadmium-4-(2-thiazolylazo)-resorcinol (Cd-TAR) complex from acid digested sample solutions and ultrasonic irradiation was applied to assist emulsification. After then, dispersed small amount of Fe3O4 magnetic nanoparticles (MNPs) in sample solutions to salvaged the IL and complete phase separation was attained. Some analytical parameters that influencing the efficiency of proposed (IL-UA-DMME) method, such as pH, volume of IL, ligand concentration, ultra-sonication time, amount of Fe3O4 MNPs, sample volume and matrix effect were optimized. Limit of detection (LOD) and enrichment factor (EF) of the method under optimal experimental conditions were found to be 0.40μgL(-1) and 100, respectively. The relative standard deviation (RSD) of 50μgL(-1) Cd was 4.29%. The validity and accuracy of proposed method, was assessed to analyzed certified reference materials of fortified lake water TMDA-54.4, SPS-WW2 waste water, spinach leaves 1570a and also checked by standard addition method. The obtained values showed good agreement with the certified values and sufficiently high recovery were found in the range of 98.1-101% for Cd. The proposed method was facile, rapid and successfully applied for the determination of Cd in environmental and different biological samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Study on molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM, HOMO-LUMO energies and docking studies of 5-fluorouracil, a substance used to treat cancer.

PubMed

Almeida, Michell O; Barros, Daiane A S; Araujo, Sheila C; Faria, Sergio H D M; Maltarollo, Vinicius G; Honorio, Kathia M

2017-09-05

Cancer cells can expand to other parts of body through blood system and nodes from a mechanism known as metastasis. Due to the large annual growth of cancer cases, various biological targets have been studied and related to this disorder. A very interesting target related to cancer is human epidermal growth factor receptor 2 (HER2). In this study, we analyzed the main intermolecular interactions between a drug used in the cancer treatment (5-fluorouracil) and HER2. Molecular modeling methods were also employed to assess the molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM and HOMO-LUMO energies of 5-FU. From the docking simulations it was possible to analyze the interactions that occur between some residues in the binding site of HER2 and 5-FU. To validate the choice of basis set that was used in the NBO and QTAIM analyses, theoretical calculations were performed to obtain FT-IR and UV/Vis spectra, and the theoretical results are consistent with the experimental data, showing that the basis set chosen is suitable. For the maximum λ from the theoretical calculation (254.89nm) of UV/Vis, the electronic transition from HOMO to LUMO occurs at 4.89eV. From NBO analyses, we observed interactions between Asp863 and 5-FU, i.e. the orbitals with high transfer of electrons are LP O 15 (donor NBO) and BD* (π) N 1 -H 10 (acceptor NBO), being that the value of this interaction is 7.72kcal/mol. Results from QTAIM indicate one main intermolecular H bond, which is necessary to stabilize the complex formed between the ligands and the biological target. Therefore, this study allowed a careful evaluation on the main structural, spectroscopic and electronic properties involved in the interaction between 5-FU and HER2, an important biological complex related to the cancer treatment. Copyright © 2017 Elsevier B.V. All rights reserved.

Origin and Evolutionary Alteration of the Mitochondrial Import System in Eukaryotic Lineages.

PubMed

Fukasawa, Yoshinori; Oda, Toshiyuki; Tomii, Kentaro; Imai, Kenichiro

2017-07-01

Protein transport systems are fundamentally important for maintaining mitochondrial function. Nevertheless, mitochondrial protein translocases such as the kinetoplastid ATOM complex have recently been shown to vary in eukaryotic lineages. Various evolutionary hypotheses have been formulated to explain this diversity. To resolve any contradiction, estimating the primitive state and clarifying changes from that state are necessary. Here, we present more likely primitive models of mitochondrial translocases, specifically the translocase of the outer membrane (TOM) and translocase of the inner membrane (TIM) complexes, using scrutinized phylogenetic profiles. We then analyzed the translocases' evolution in eukaryotic lineages. Based on those results, we propose a novel evolutionary scenario for diversification of the mitochondrial transport system. Our results indicate that presequence transport machinery was mostly established in the last eukaryotic common ancestor, and that primitive translocases already had a pathway for transporting presequence-containing proteins. Moreover, secondary changes including convergent and migrational gains of a presequence receptor in TOM and TIM complexes, respectively, likely resulted from constrained evolution. The nature of a targeting signal can constrain alteration to the protein transport complex. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

The evolution of phenotypic integration: How directional selection reshapes covariation in mice.

PubMed

Penna, Anna; Melo, Diogo; Bernardi, Sandra; Oyarzabal, Maria Inés; Marroig, Gabriel

2017-10-01

Variation is the basis for evolution, and understanding how variation can evolve is a central question in biology. In complex phenotypes, covariation plays an even more important role, as genetic associations between traits can bias and alter evolutionary change. Covariation can be shaped by complex interactions between loci, and this genetic architecture can also change during evolution. In this article, we analyzed mouse lines experimentally selected for changes in size to address the question of how multivariate covariation changes under directional selection, as well as to identify the consequences of these changes to evolution. Selected lines showed a clear restructuring of covariation in their cranium and, instead of depleting their size variation, these lines increased their magnitude of integration and the proportion of variation associated with the direction of selection. This result is compatible with recent theoretical works on the evolution of covariation that take the complexities of genetic architecture into account. This result also contradicts the traditional view of the effects of selection on available covariation and suggests a much more complex view of how populations respond to selection. © 2017 The Author(s). Evolution published by Wiley Periodicals, Inc. on behalf of The Society for the Study of Evolution.

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.

Synthesis, spectroscopic characterization and in vitro cytotoxicities of new organometallic palladium complexes with biologically active β-diketones; Biological evaluation probing of the interaction mechanism with DNA/Protein and molecular docking

NASA Astrophysics Data System (ADS)

Karami, Kazem; Rafiee, Mina; Lighvan, Zohreh Mehri; Zakariazadeh, Mostafa; Faal, Ali Yeganeh; Esmaeili, Seyed-Alireza; Momtazi-Borojeni, Amir Abbas

2018-02-01

[Pd{(C,N)sbnd C6H4CH (CH3)NH}(CUR)] (3) and [Pd2{(C,N)sbnd C6H4CH(CH3)NH2}2(μ-N3CS2)] (4) [cur = 1,7-bis(4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dion] novel organometallic complexes with biologically active ligands have been prepared and characterized via elemental analysis, multinuclear spectroscopic techniques (1H, and 13C NMR and IR) and their biological activities, including antitumoral activity and DNA-protein interactions have been investigated. Fluorescence spectroscopy used to study the interaction of the complexes with BSA have shown the affinity of the complexes for these proteins with relatively high binding constant values and the changed secondary structure of BSA in the presence of the complexes. In the meantime, spectroscopy and competitive titration have been applied to investigate the interaction of complexes with Warfarin and Ibuprofen site markers for sites I and II, respectively, with BSA. The results have suggested that the locations of complexes 3 and 4 are sites II and I, respectively. UV-Vis spectroscopy, emission titration and helix melting methods have been used to study the interaction of these complexes with CT-DNA, indicating that complexes are bound to CT-DNA by intercalation binding mode. In addition, good cytotoxic activity against MCF-7 (human breast cancer) and JURKAT (human leukemia) cell line has been shown by both complexes whereas low cytotoxicity was exerted on normal peripheral blood mononuclear cells.

Immunoaffinity capillary electrophoresis as a powerful strategy for the quantification of low-abundance biomarkers, drugs, and metabolites in biological matrices

PubMed Central

Guzman, Norberto A.; Blanc, Timothy; Phillips, Terry M.

2009-01-01

In the last few years, there has been a greater appreciation by the scientific community of how separation science has contributed to the advancement of biomedical research. Despite past contributions in facilitating several biomedical breakthroughs, separation sciences still urgently need the development of improved methods for the separation and detection of biological and chemical substances. In particular, the challenging task of quantifying small molecules and biomolecules, found in low abundance in complex matrices (e.g., serum), is a particular area in need of new high-efficiency techniques. The tandem or on-line coupling of highly selective antibody capture agents with the high-resolving power of CE is being recognized as a powerful analytical tool for the enrichment and quantification of ultra-low abundance analytes in complex matrices. This development will have a significant impact on the identification and characterization of many putative biomarkers and on biomedical research in general. Immunoaffinity CE (IACE) technology is rapidly emerging as the most promising method for the analysis of low-abundance biomarkers; its power comes from a three-step procedure: (i) bioselective adsorption and (ii) subsequent recovery of compounds from an immobilized affinity ligand followed by (iii) separation of the enriched compounds. This technology is highly suited to automation and can be engineered to as a multiplex instrument capable of routinely performing hundreds of assays per day. Furthermore, a significant enhancement in sensitivity can be achieved for the purified and enriched affinity targeted analytes. Thus, a compound that exists in a complex biological matrix at a concentration far below its LOD is easily brought to well within its range of quantification. The present review summarizes several applications of IACE, as well as a chronological description of the improvements made in the fabrication of the analyte concentrator-microreactor device leading to the development of a multidimensional biomarker analyzer. PMID:18646282

Statistical Inference for Big Data Problems in Molecular Biophysics

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

Ramanathan, Arvind; Savol, Andrej; Burger, Virginia

2012-01-01

We highlight the role of statistical inference techniques in providing biological insights from analyzing long time-scale molecular simulation data. Technologi- cal and algorithmic improvements in computation have brought molecular simu- lations to the forefront of techniques applied to investigating the basis of living systems. While these longer simulations, increasingly complex reaching petabyte scales presently, promise a detailed view into microscopic behavior, teasing out the important information has now become a true challenge on its own. Mining this data for important patterns is critical to automating therapeutic intervention discovery, improving protein design, and fundamentally understanding the mech- anistic basis of cellularmore » homeostasis.« less

Multimodal biophotonic workstation for live cell analysis.

PubMed

Esseling, Michael; Kemper, Björn; Antkowiak, Maciej; Stevenson, David J; Chaudet, Lionel; Neil, Mark A A; French, Paul W; von Bally, Gert; Dholakia, Kishan; Denz, Cornelia

2012-01-01

A reliable description and quantification of the complex physiology and reactions of living cells requires a multimodal analysis with various measurement techniques. We have investigated the integration of different techniques into a biophotonic workstation that can provide biological researchers with these capabilities. The combination of a micromanipulation tool with three different imaging principles is accomplished in a single inverted microscope which makes the results from all the techniques directly comparable. Chinese Hamster Ovary (CHO) cells were manipulated by optical tweezers while the feedback was directly analyzed by fluorescence lifetime imaging, digital holographic microscopy and dynamic phase-contrast microscopy. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The CH/π hydrogen bond: Implication in chemistry

NASA Astrophysics Data System (ADS)

Nishio, M.

2012-06-01

The CH/π hydrogen bond is the weakest extreme of hydrogen bonds that occurs between a soft acid CH and a soft base π-system. Implication in chemistry of the CH/π hydrogen bond includes issues of conformation, crystal packing, and specificity in host/guest complexes. The result obtained by analyzing the Cambridge Structural Database is reviewed. The peculiar axial preference of isopropyl group in α-phellandrene and folded conformation of levopimaric acid have been explained in terms of the CH/π hydrogen bond, by high-level ab initio MO calculations. Implication of the CH/π hydrogen bond in structural biology is also discussed, briefly.

[Phenomenology of craving: from differentiation to adequate therapy].

PubMed

Mendelevich, V D

2010-01-01

The author analyzes a phenomenon of addiction from the psychological/psychiatric position and differentiates it from psychopathological disorders, including parabulia, hyperbulia, paraphylia, commonly used for the definition of drive disorders. It has been concluded that addition is a specific complex of clinical symptoms which is not similar to other drive disorders. To avoid diagnostic and therapeutic errors, the author suggests to revise definitions by assigning the biological sense to the conception of addiction within psychoactive substance dependence and sexual addiction, some forms of eating dependence and to use the definition of paraaddictive drives in cases of over-valued drives (gambling, Internet dependence, fanaticism etc).

Peptide Logic Circuits Based on Chemoenzymatic Ligation for Programmable Cell Apoptosis.

PubMed

Li, Yong; Sun, Sujuan; Fan, Lin; Hu, Shanfang; Huang, Yan; Zhang, Ke; Nie, Zhou; Yao, Shouzhou

2017-11-20

A novel and versatile peptide-based bio-logic system capable of regulating cell function is developed using sortase A (SrtA), a peptide ligation enzyme, as a generic processor. By modular peptide design, we demonstrate that mammalian cells apoptosis can be programmed by peptide-based logic operations, including binary and combination gates (AND, INHIBIT, OR, and AND-INHIBIT), and a complex sequential logic circuit (multi-input keypad lock). Moreover, a proof-of-concept peptide regulatory circuit was developed to analyze the expression profile of cell-secreted protein biomarkers and trigger cancer-cell-specific apoptosis. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Preparation of the low molecular weight serum proteome for mass spectrometry analysis.

PubMed

Waybright, Timothy J; Chan, King C; Veenstra, Timothy D; Xiao, Zhen

2013-01-01

The discovery of viable biomarkers or indicators of disease states is complicated by the inherent complexity of the chosen biological specimen. Every sample, whether it is serum, plasma, urine, tissue, cells, or a host of others, contains thousands of large and small components, each interacting in multiple ways. The need to concentrate on a group of these components to narrow the focus on a potential biomarker candidate becomes, out of necessity, a priority, especially in the search for immune-related low molecular weight serum biomarkers. One such method in the field of proteomics is to divide the sample proteome into groups based on the size of the protein, analyze each group, and mine the data for statistically significant items. This chapter details a portion of this method, concentrating on a method for fractionating and analyzing the low molecular weight proteome of human serum.

[Recent advances in analysis of petroleum geological samples by comprehensive two-dimensional gas chromatography].

PubMed

Gao, Xuanbo; Chang, Zhenyang; Dai, Wei; Tong, Ting; Zhang, Wanfeng; He, Sheng; Zhu, Shukui

2014-10-01

Abundant geochemical information can be acquired by analyzing the chemical compositions of petroleum geological samples. The information obtained from the analysis provides scientifical evidences for petroleum exploration. However, these samples are complicated and can be easily influenced by physical (e. g. evaporation, emulsification, natural dispersion, dissolution and sorption), chemical (photodegradation) and biological (mainly microbial degradation) weathering processes. Therefore, it is very difficult to analyze the petroleum geological samples and they cannot be effectively separated by traditional gas chromatography/mass spectrometry. A newly developed separation technique, comprehensive two-dimensional gas chromatography (GC x GC), has unique advantages in complex sample analysis, and recently it has been applied to petroleum geological samples. This article mainly reviews the research progres- ses in the last five years, the main problems and the future research about GC x GC applied in the area of petroleum geology.

Informing Biological Design by Integration of Systems and Synthetic Biology

PubMed Central

Smolke, Christina D.; Silver, Pamela A.

2011-01-01

Synthetic biology aims to make the engineering of biology faster and more predictable. In contrast, systems biology focuses on the interaction of myriad components and how these give rise to the dynamic and complex behavior of biological systems. Here, we examine the synergies between these two fields. PMID:21414477

Spectroscopic, thermal, quantum chemical calculations and in vitro biological studies of titanium/zirconium(IV) complexes of mono-and disubstituted aryldithiocarbonates

NASA Astrophysics Data System (ADS)

Andotra, Savit; Kumar, Sandeep; Kour, Mandeep; Kalgotra, Nidhi; Vikas; Chayawan; Pandey, Sushil K.

2018-03-01

Aryldithiocarbonates of titanium(IV) and zirconium(IV) corresponding to [(ROCS2)2MCl2] (R = o-, m- or p-CH3C6H4 and 4-Cl-3-CH3C6H3; M = Ti or Zr) have been isolated by the reaction of sodium salt of dithiocarbonates with titanium or zirconium tetrachloride in 1:2 M ratio in CHCl3. Donor stabilized addition complexes of titanium/zirconium with aryldithiocarbonates were also successfully isolated in chloroform. These have been characterized by elemental analyses, IR, mass, TGA/DTA, SEM and heteronuclear NMR (1H, 13C and 31P) spectroscopic studies. The antimicrobial test of these complexes has also been conducted against the bacteria Klebsiella pneumonia and Enterococcus faciolus and fungus Fusarium oxysporium, which indicate potential antimicrobial activity. In addition, the antioxidant activities of the complexes were also investigated through their scavenging effect on DPPH radicals. Density Functional Theory (DFT) calculations have been carried out to investigate geometry parameters of the complexes using B3LYP method and LANL2DZ basis set. HOMO (Highest Occupied Molecular Orbital), LUMO (Lowest Unoccupied Molecular Orbital) energies are analyzed. Based on analytical and theoretical results, a hexacoordinate geometry is concluded around the Ti or Zr atom.

Statistical Determinants of Selective Ionic Complexation: Ions in Solvent, Transport Proteins, and Other “Hosts”

PubMed Central

Bostick, David L.; Brooks, Charles L.

2009-01-01

To provide utility in understanding the molecular evolution of ion-selective biomembrane channels/transporters, globular proteins, and ionophoric compounds, as well as in guiding their modification and design, we present a statistical mechanical basis for deconstructing the impact of the coordination structure and chemistry of selective multidentate ionic complexes. The deconstruction augments familiar ideas in liquid structure theory to realize the ionic complex as an open ion-ligated system acting under the influence of an “external field” provided by the host (or surrounding medium). Using considerations derived from this basis, we show that selective complexation arises from exploitation of a particular ion's coordination preferences. These preferences derive from a balance of interactions much like that which dictates the Hofmeister effect. By analyzing the coordination-state space of small family IA and VIIA ions in simulated fluid media, we derive domains of coordinated states that confer selectivity for a given ion upon isolating and constraining particular attributes (order parameters) of a complex comprised of a given type of ligand. We demonstrate that such domains may be used to rationalize the ion-coordinated environments provided by selective ionophores and biological ion channels/transporters of known structure, and that they can serve as a means toward deriving rational design principles for ion-selective hosts. PMID:19486671

The Cerebro-oculo-facio-skeletal Syndrome Point Mutation F231L in the ERCC1 DNA Repair Protein Causes Dissociation of the ERCC1-XPF Complex.

PubMed

Faridounnia, Maryam; Wienk, Hans; Kovačič, Lidija; Folkers, Gert E; Jaspers, Nicolaas G J; Kaptein, Robert; Hoeijmakers, Jan H J; Boelens, Rolf

2015-08-14

The ERCC1-XPF heterodimer, a structure-specific DNA endonuclease, is best known for its function in the nucleotide excision repair (NER) pathway. The ERCC1 point mutation F231L, located at the hydrophobic interaction interface of ERCC1 (excision repair cross-complementation group 1) and XPF (xeroderma pigmentosum complementation group F), leads to severe NER pathway deficiencies. Here, we analyze biophysical properties and report the NMR structure of the complex of the C-terminal tandem helix-hairpin-helix domains of ERCC1-XPF that contains this mutation. The structures of wild type and the F231L mutant are very similar. The F231L mutation results in only a small disturbance of the ERCC1-XPF interface, where, in contrast to Phe(231), Leu(231) lacks interactions stabilizing the ERCC1-XPF complex. One of the two anchor points is severely distorted, and this results in a more dynamic complex, causing reduced stability and an increased dissociation rate of the mutant complex as compared with wild type. These data provide a biophysical explanation for the severe NER deficiencies caused by this mutation. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Representations of the Nature of Scientific Knowledge in Turkish Biology Textbooks

ERIC Educational Resources Information Center

Irez, Serhat

2016-01-01

Considering the impact of textbooks on learning, this study set out to assess representations of the nature of scientific knowledge in Turkish 9th grade biology textbooks. To this end, the ten most commonly used 9th grade biology textbooks were analyzed. A qualitative research approach was utilized and the textbooks were analyzed using…

Synthesis, spectroscopic characterization, electrochemistry and biological evaluation of some binuclear transition metal complexes of bicompartmental ONO donor ligands containing benzo[b]thiophene moiety

NASA Astrophysics Data System (ADS)

Mahendra Raj, K.; Vivekanand, B.; Nagesh, G. Y.; Mruthyunjayaswamy, B. H. M.

2014-02-01

A series of new binucleating Cu(II), Co(II), Ni(II) and Zn(II) complexes of bicompartmental ligands with ONO donor were synthesized. The ligands were obtained by the condensation of 3-chloro-6-substituted benzo[b]thiophene-2-carbohydrazides and 4,6-diacetylresorcinol. The synthesized ligands and their complexes were characterized by elemental analysis and various spectroscopic techniques. Elemental analysis, IR, 1H NMR, ESI-mass, UV-Visible, TG-DTA, magnetic measurements, molar conductance and powder-XRD data has been used to elucidate their structures. The bonding sites are the oxygen atom of amide carbonyl, azomethine nitrogen and phenolic oxygen for ligands 1 and 2. The binuclear nature of the complexes was confirmed by ESR spectral data. TG-DTA studies for some complexes showed the presence of coordinated water molecules and the final product is the metal oxide. All the complexes were investigated for their electrochemical activity, only the Cu(II) complexes showed the redox property. Cu(II) complexes were square planar, whereas Co(II), Ni(II) and Zn(II) complexes were octahedral. Powder-XRD pattern have been studied in order to test the degree of crystallinity of the complexes and unit cell calculations were made. In order to evaluate the effect of antimicrobial activity of metal ions upon chelation, both the ligands and their metal complexes were screened for their antibacterial and antifungal activities by minimum inhibitory concentration (MIC) method. The results showed that the metal complexes were found to be more active than free ligands. The DNA cleaving capacities of all the complexes were analyzed by agarose gel electrophoresis method against supercoiled plasmid DNA. Among the compounds tested for antioxidant capacity, ligand 1 displayed excellent activity than its metal complexes.

Electrochemical studies of DNA interaction and antimicrobial activities of MnII, FeIII, CoII and NiII Schiff base tetraazamacrocyclic complexes

NASA Astrophysics Data System (ADS)

Kumar, Anuj; Vashistha, Vinod Kumar; Tevatia, Prashant; Singh, Randhir

2017-04-01

Tetraazamacrocyclic complexes of MnII, FeIII, CoII and NiII have been synthesized by template method. These tetraazamacrocycles have been analyzed with various techniques like molar conductance, IR, UV-vis, mass spectral and cyclic voltammetric studies. On the basis of all these studies, octahedral geometry has been assigned to these tetraazamacrocyclic complexes. The DNA binding properties of these macrocyclic complexes have been investigated by electronic absorption spectra, fluorescence spectra, cyclic voltammetric and differential pulse voltammetric studies. The cyclic voltammetric data showed that ipc and ipa were effectively decreased in the presence of calf thymus DNA, which is a strong evidence for the interaction of these macrocyclic complexes with the calf thymus DNA (ct-DNA). The heterogeneous electron transfer rate constant found in the order: KCoII > KNiII > KMnII which indicates that CoII macrocyclic complex has formed a strong intercalated intermediate. The Stern-Volmer quenching constant (KSV) and voltammetric binding constant were found in the order KSV(CoII) > KSV(NiII) > KSV(MnII) and K+(CoII) > K+(NiII) > K+(MnII) which shows that CoII macrocyclic complex exhibits the high interaction affinity towards ct-DNA by the intercalation binding. Biological studies of the macrocyclic complexes compared with the standard drug like Gentamycin, have shown antibacterial activities against E. coli, P. aeruginosa, B. cereus, S. aureus and antifungal activity against C. albicans.

Translational research in infectious disease: current paradigms and challenges ahead

PubMed Central

Fontana, Judith M.; Alexander, Elizabeth; Salvatore, Mirella

2012-01-01

In recent years, the biomedical community has witnessed a rapid scientific and technological evolution following the development and refinement of high-throughput methodologies. Concurrently and consequentially, the scientific perspective has changed from the reductionist approach of meticulously analyzing the fine details of a single component of biology, to the “holistic” approach of broadmindedly examining the globally interacting elements of biological systems. The emergence of this new way of thinking has brought about a scientific revolution in which genomics, proteomics, metabolomics and other “omics” have become the predominant tools by which large amounts of data are amassed, analyzed and applied to complex questions of biology that were previously unsolvable. This enormous transformation of basic science research and the ensuing plethora of promising data, especially in the realm of human health and disease, have unfortunately not been followed by a parallel increase in the clinical application of this information. On the contrary, the number of new potential drugs in development has been steadily decreasing, suggesting the existence of roadblocks that prevent the translation of promising research into medically relevant therapeutic or diagnostic application. In this paper we will review, in a non-inclusive fashion, several recent scientific advancements in the field of translational research, with a specific focus on how they relate to infectious disease. We will also present a current picture of the limitations and challenges that exist for translational research, as well as ways that have been proposed by the National Institutes of Health to improve the state of this field. PMID:22633095

Evidence of pervasive biologically functional secondary structures within the genomes of eukaryotic single-stranded DNA viruses.

PubMed

Muhire, Brejnev Muhizi; Golden, Michael; Murrell, Ben; Lefeuvre, Pierre; Lett, Jean-Michel; Gray, Alistair; Poon, Art Y F; Ngandu, Nobubelo Kwanele; Semegni, Yves; Tanov, Emil Pavlov; Monjane, Adérito Luis; Harkins, Gordon William; Varsani, Arvind; Shepherd, Dionne Natalie; Martin, Darren Patrick

2014-02-01

Single-stranded DNA (ssDNA) viruses have genomes that are potentially capable of forming complex secondary structures through Watson-Crick base pairing between their constituent nucleotides. A few of the structural elements formed by such base pairings are, in fact, known to have important functions during the replication of many ssDNA viruses. Unknown, however, are (i) whether numerous additional ssDNA virus genomic structural elements predicted to exist by computational DNA folding methods actually exist and (ii) whether those structures that do exist have any biological relevance. We therefore computationally inferred lists of the most evolutionarily conserved structures within a diverse selection of animal- and plant-infecting ssDNA viruses drawn from the families Circoviridae, Anelloviridae, Parvoviridae, Nanoviridae, and Geminiviridae and analyzed these for evidence of natural selection favoring the maintenance of these structures. While we find evidence that is consistent with purifying selection being stronger at nucleotide sites that are predicted to be base paired than at sites predicted to be unpaired, we also find strong associations between sites that are predicted to pair with one another and site pairs that are apparently coevolving in a complementary fashion. Collectively, these results indicate that natural selection actively preserves much of the pervasive secondary structure that is evident within eukaryote-infecting ssDNA virus genomes and, therefore, that much of this structure is biologically functional. Lastly, we provide examples of various highly conserved but completely uncharacterized structural elements that likely have important functions within some of the ssDNA virus genomes analyzed here.

Evidence of Pervasive Biologically Functional Secondary Structures within the Genomes of Eukaryotic Single-Stranded DNA Viruses

PubMed Central

Muhire, Brejnev Muhizi; Golden, Michael; Murrell, Ben; Lefeuvre, Pierre; Lett, Jean-Michel; Gray, Alistair; Poon, Art Y. F.; Ngandu, Nobubelo Kwanele; Semegni, Yves; Tanov, Emil Pavlov; Monjane, Adérito Luis; Harkins, Gordon William; Varsani, Arvind; Shepherd, Dionne Natalie

2014-01-01

Single-stranded DNA (ssDNA) viruses have genomes that are potentially capable of forming complex secondary structures through Watson-Crick base pairing between their constituent nucleotides. A few of the structural elements formed by such base pairings are, in fact, known to have important functions during the replication of many ssDNA viruses. Unknown, however, are (i) whether numerous additional ssDNA virus genomic structural elements predicted to exist by computational DNA folding methods actually exist and (ii) whether those structures that do exist have any biological relevance. We therefore computationally inferred lists of the most evolutionarily conserved structures within a diverse selection of animal- and plant-infecting ssDNA viruses drawn from the families Circoviridae, Anelloviridae, Parvoviridae, Nanoviridae, and Geminiviridae and analyzed these for evidence of natural selection favoring the maintenance of these structures. While we find evidence that is consistent with purifying selection being stronger at nucleotide sites that are predicted to be base paired than at sites predicted to be unpaired, we also find strong associations between sites that are predicted to pair with one another and site pairs that are apparently coevolving in a complementary fashion. Collectively, these results indicate that natural selection actively preserves much of the pervasive secondary structure that is evident within eukaryote-infecting ssDNA virus genomes and, therefore, that much of this structure is biologically functional. Lastly, we provide examples of various highly conserved but completely uncharacterized structural elements that likely have important functions within some of the ssDNA virus genomes analyzed here. PMID:24284329

Nonproliferation Test and Evaluation Complex - NPTEC

ScienceCinema

None

2018-01-16

The Nonproliferation Test and Evaluation Complex, or NPTEC, is the world's largest facility for open air testing of hazardous toxic materials and biological simulants. NPTEC is used for testing, experimentation, and training for technologies that require the release of toxic chemicals or biological simulants into the environment.

Entropy for the Complexity of Physiological Signal Dynamics.

PubMed

Zhang, Xiaohua Douglas

2017-01-01

Recently, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of biological dynamics. Portable noninvasive medical devices are crucial to capture individual characteristics of biological dynamics. The wearable noninvasive medical devices and the analysis/management of related digital medical data will revolutionize the management and treatment of diseases, subsequently resulting in the establishment of a new healthcare system. One of the key features that can be extracted from the data obtained by wearable noninvasive medical device is the complexity of physiological signals, which can be represented by entropy of biological dynamics contained in the physiological signals measured by these continuous monitoring medical devices. Thus, in this chapter I present the major concepts of entropy that are commonly used to measure the complexity of biological dynamics. The concepts include Shannon entropy, Kolmogorov entropy, Renyi entropy, approximate entropy, sample entropy, and multiscale entropy. I also demonstrate an example of using entropy for the complexity of glucose dynamics.

Charge-transfer interaction of drug quinidine with quinol, picric acid and DDQ: Spectroscopic characterization and biological activity studies towards understanding the drug-receptor mechanism.

PubMed

Eldaroti, Hala H; Gadir, Suad A; Refat, Moamen S; Adam, Abdel Majid A

2014-04-01

Investigation of charge-transfer (CT) complexes of drugs has been recognized as an important phenomenon in understanding of the drug-receptor binding mechanism. Structural, thermal, morphological and biological behavior of CT complexes formed between drug quinidine (Qui) as a donor and quinol (QL), picric acid (PA) or dichlorodicyanobenzoquinone (DDQ) as acceptors were reported. The newly synthesized CT complexes have been spectroscopically characterized via elemental analysis; infrared (IR), Raman, 1 H NMR and electronic absorption spectroscopy; powder X-ray diffraction (PXRD); thermogravimetric (TG) analysis and scanning electron microscopy (SEM). It was found that the obtained complexes are nanoscale, semi-crystalline particles, thermally stable and spontaneous. The molecular composition of the obtained complexes was determined using spectrophotometric titration method and was found to be 1:1 ratios (donor:acceptor). Finally, the biological activities of the obtained CT complexes were tested for their antibacterial activities. The results obtained herein are satisfactory for estimation of drug Qui in the pharmaceutical form.

Industrial systems biology and its impact on synthetic biology of yeast cell factories.

PubMed

Fletcher, Eugene; Krivoruchko, Anastasia; Nielsen, Jens

2016-06-01

Engineering industrial cell factories to effectively yield a desired product while dealing with industrially relevant stresses is usually the most challenging step in the development of industrial production of chemicals using microbial fermentation processes. Using synthetic biology tools, microbial cell factories such as Saccharomyces cerevisiae can be engineered to express synthetic pathways for the production of fuels, biopharmaceuticals, fragrances, and food flavors. However, directing fluxes through these synthetic pathways towards the desired product can be demanding due to complex regulation or poor gene expression. Systems biology, which applies computational tools and mathematical modeling to understand complex biological networks, can be used to guide synthetic biology design. Here, we present our perspective on how systems biology can impact synthetic biology towards the goal of developing improved yeast cell factories. Biotechnol. Bioeng. 2016;113: 1164-1170. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

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.

Simulating complex intracellular processes using object-oriented computational modelling.

PubMed

Johnson, Colin G; Goldman, Jacki P; Gullick, William J

2004-11-01

The aim of this paper is to give an overview of computer modelling and simulation in cellular biology, in particular as applied to complex biochemical processes within the cell. This is illustrated by the use of the techniques of object-oriented modelling, where the computer is used to construct abstractions of objects in the domain being modelled, and these objects then interact within the computer to simulate the system and allow emergent properties to be observed. The paper also discusses the role of computer simulation in understanding complexity in biological systems, and the kinds of information which can be obtained about biology via simulation.

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools.

PubMed

Price, Jeffrey H; Goodacre, Angela; Hahn, Klaus; Hodgson, Louis; Hunter, Edward A; Krajewski, Stanislaw; Murphy, Robert F; Rabinovich, Andrew; Reed, John C; Heynen, Susanne

2002-01-01

Cellular behavior is complex. Successfully understanding systems at ever-increasing complexity is fundamental to advances in modern science and unraveling the functional details of cellular behavior is no exception. We present a collection of prospectives to provide a glimpse of the techniques that will aid in collecting, managing and utilizing information on complex cellular processes via molecular imaging tools. These include: 1) visualizing intracellular protein activity with fluorescent markers, 2) high throughput (and automated) imaging of multilabeled cells in statistically significant numbers, and 3) machine intelligence to analyze subcellular image localization and pattern. Although not addressed here, the importance of combining cell-image-based information with detailed molecular structure and ligand-receptor binding models cannot be overlooked. Advanced molecular imaging techniques have the potential to impact cellular diagnostics for cancer screening, clinical correlations of tissue molecular patterns for cancer biology, and cellular molecular interactions for accelerating drug discovery. The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry. Copyright 2002 Wiley-Liss, Inc.

Correlative nanoscale imaging of actin filaments and their complexes

NASA Astrophysics Data System (ADS)

Sharma, Shivani; Zhu, Huanqi; Grintsevich, Elena E.; Reisler, Emil; Gimzewski, James K.

2013-06-01

Actin remodeling is an area of interest in biology in which correlative microscopy can bring a new way to analyze protein complexes at the nanoscale. Advances in EM, X-ray diffraction, fluorescence, and single molecule techniques have provided a wealth of information about the modulation of the F-actin structure and its regulation by actin binding proteins (ABPs). Yet, there are technological limitations of these approaches to achieving quantitative molecular level information on the structural and biophysical changes resulting from ABPs interaction with F-actin. Fundamental questions about the actin structure and dynamics and how these determine the function of ABPs remain unanswered. Specifically, how local and long-range structural and conformational changes result in ABPs induced remodeling of F-actin needs to be addressed at the single filament level. Advanced, sensitive and accurate experimental tools for detailed understanding of ABP-actin interactions are much needed. This article discusses the current understanding of nanoscale structural and mechanical modulation of F-actin by ABPs at the single filament level using several correlative microscopic techniques, focusing mainly on results obtained by Atomic Force Microscopy (AFM) analysis of ABP-actin complexes.

Chemistry and biological activity of platinum amidine complexes.

PubMed

Michelin, Rino A; Sgarbossa, Paolo; Sbovata, Silvia Mazzega; Gandin, Valentina; Marzano, Cristina; Bertani, Roberta

2011-07-04

Platinum amidine complexes represent a new class of potential antitumor drugs that contain the imino moiety HN=C(sp(2)) bonded to the platinum center. They can be related to the iminoether derivatives, which were recently shown to be the first Pt(II) compounds with a trans configuration endowed with anticancer activity. The chemical and biological properties of platinum amidine complexes, and more generally of platinum imino derivatives, can be rationally modified through suitable synthetic procedures with the aim of improving their cytotoxicity and antitumor activity. The addition of protic nucleophiles to nitriles coordinated to platinum in various oxidation states can offer a wide variety of complexes with chemical, structural, and physical properties specifically tuned for a more efficacious biological response. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

On the integration of protein-protein interaction networks with gene expression and 3D structural data: What can be gained?

NASA Astrophysics Data System (ADS)

Bertolazzi, Paola; Bock, Mary Ellen; Guerra, Concettina; Paci, Paola; Santoni, Daniele

2014-06-01

The biological role of proteins has been analyzed from different perspectives, initially by considering proteins as isolated biological entities, then as cooperating entities that perform their function by interacting with other molecules. There are other dimensions that are important for the complete understanding of the biological processes: time and location. However a protein is rarely annotated with temporal and spatial information. Experimental Protein-Proteins Interaction (PPI) data are static; furthermore they generally do not include transient interactions which are a considerable fraction of the interactome of many organisms. One way to incorporate temporal and condition information is to use other sources of information, such as gene expression data and 3D structural data. Here we review work done to understand the insight that can be gained by enriching PPI data with gene expression and 3D structural data. In particular, we address the following questions: Can the dynamics of a single protein or of an interaction be accurately derived from these data? Can the assembly-disassembly of protein complexes be traced over time? What type of topological changes occur in a PPI network architecture over time?

Photoinduced catalytic synthesis of biologically important metabolites from formaldehyde and ammonia under plausible "prebiotic" conditions

NASA Astrophysics Data System (ADS)

Delidovich, I. V.; Taran, O. P.; Simonov, A. N.; Matvienko, L. G.; Parmon, V. N.

2011-08-01

The article analyzes new and previously reported data on several catalytic and photochemical processes yielding biologically important molecules. UV-irradiation of formaldehyde aqueous solution yields acetaldehyde, glyoxal, glycolaldehyde and glyceraldehyde, which can serve as precursors of more complex biochemically relevant compounds. Photolysis of aqueous solution of acetaldehyde and ammonium nitrate results in formation of alanine and pyruvic acid. Dehydration of glyceraldehyde catalyzed by zeolite HZSM-5-17 yields pyruvaldehyde. Monosaccharides are formed in the course of the phosphate-catalyzed aldol condensation reactions of glycolaldehyde, glyceraldehyde and formaldehyde. The possibility of the direct synthesis of tetroses, keto- and aldo-pentoses from pure formaldehyde due to the combination of the photochemical production of glycolahyde and phosphate-catalyzed carbohydrate chain growth is demonstrated. Erythrulose and 3-pentulose are the main products of such combined synthesis with selectivity up to 10%. Biologically relevant aldotetroses, aldo- and ketopentoses are more resistant to the photochemical destruction owing to the stabilization in hemiacetal cyclic forms. They are formed as products of isomerization of erythrulose and 3-pentulose. The conjugation of the concerned reactions results in a plausible route to the formation of sugars, amino and organic acids from formaldehyde and ammonia under presumed 'prebiotic' conditions.

Modeling biology with HDL languages: a first step toward a genetic design automation tool inspired from microelectronics.

PubMed

Gendrault, Yves; Madec, Morgan; Lallement, Christophe; Haiech, Jacques

2014-04-01

Nowadays, synthetic biology is a hot research topic. Each day, progresses are made to improve the complexity of artificial biological functions in order to tend to complex biodevices and biosystems. Up to now, these systems are handmade by bioengineers, which require strong technical skills and leads to nonreusable development. Besides, scientific fields that share the same design approach, such as microelectronics, have already overcome several issues and designers succeed in building extremely complex systems with many evolved functions. On the other hand, in systems engineering and more specifically in microelectronics, the development of the domain has been promoted by both the improvement of technological processes and electronic design automation tools. The work presented in this paper paves the way for the adaptation of microelectronics design tools to synthetic biology. Considering the similarities and differences between the synthetic biology and microelectronics, the milestones of this adaptation are described. The first one concerns the modeling of biological mechanisms. To do so, a new formalism is proposed, based on an extension of the generalized Kirchhoff laws to biology. This way, a description of all biological mechanisms can be made with languages widely used in microelectronics. Our approach is therefore successfully validated on specific examples drawn from the literature.

Studies on the synthesis, characterization, human serum albumin binding and biological activity of single chain surfactant-cobalt(III) complexes.

PubMed

Vignesh, G; Sugumar, K; Arunachalam, S; Vignesh, S; Arthur James, R; Arun, R; Premkumar, K

2016-03-01

The interaction of surfactant-cobalt(III) complexes [Co(bpy)(dien)TA](ClO4)3 · 3H2O (1) and [Co(dien)(phen)TA](ClO4)3 · 4H2O (2), where bpy = 2,2'-bipyridine, dien = diethylenetriamine, phen = 1,10-phenanthroline and TA = tetradecylamine with human serum albumin (HSA) under physiological conditions was analyzed using steady state, synchronous, 3D fluorescence, UV/visabsorption and circular dichroism spectroscopic techniques. The results show that these complexes cause the fluorescence quenching of HSA through a static mechanism. The binding constant (Kb ) and number of binding-sites (n) were obtained at different temperatures. The corresponding thermodynamic parameters (∆G°, ∆H° and ∆S°) and Ea were also obtained. According to Förster's non-radiation energy transfer theory, the binding distance (r) between the complexes and HSA were calculated. The results of synchronous and 3D fluorescence spectroscopy indicate that the binding process has changed considerably the polarity around the fluorophores, along with changes in the conformation of the protein. The antimicrobial and anticancer activities of the complexes were tested and the results show that the complexes have good activities against pathogenic microorganisms and cancer cells. Copyright © 2015 John Wiley & Sons, Ltd.

Quantum, characterization and spectroscopic studies on Cu(II), Pd(II) and Pt(II) complexes of 1-(benzo[d]thiazol-2-yl)-3-phenylthiourea and its biological application as antimicrobial and antioxidant

NASA Astrophysics Data System (ADS)

Jambi, M. S.

2017-09-01

Divalent platinum, palladium and copper chelates of H2PhT have been isolated and identified. Their structures have been elucidated by partial elemental analyses, magnetic susceptibilities and spectroscopic estimations and additionally mass spectra. The FTIR and 1H NMR studies illustrated that H2PhT performs as mono-negative bi-dentate in Cu(II) and Pd(II) complexes while it behaves as neutral bi-dentate in both Pt(II) complexes. Both magnetic moments and spectral studies suggests a tetrahedral coordination geometry for [Cu(HPhT)(H2O)Cl] complex, a square planar geometry for both [Pd(HPhT)2] and [Pt(H2PhT)2Cl2] complexes and octahedral geometry for [Pt(H2PhT)2Cl2] complex. The molecular modeling are drawn and demonstrated both bond lengths and angles, chemical reactivity, MEP, NLO, Mulliken atomic charges, and binding energy (kcal/mol) for the investigated compounds. Theoretical infrared intensities and 1H NMR of H2PhT was computed utilizing DFT technique. An examination of the experimental and hypothetical spectra can be extremely valuable in making right assignments and analyzing the main chemical shift. DNA bioassay, antibacterial and antifungal activities of the investigated compounds have been determined.

Cyclodextrin-insulin complex encapsulated polymethacrylic acid based nanoparticles for oral insulin delivery.

PubMed

Sajeesh, S; Sharma, Chandra P

2006-11-15

Present investigation was aimed at developing an oral insulin delivery system based on hydroxypropyl beta cyclodextrin-insulin (HPbetaCD-I) complex encapsulated polymethacrylic acid-chitosan-polyether (polyethylene glycol-polypropylene glycol copolymer) (PMCP) nanoparticles. Nanoparticles were prepared by the free radical polymerization of methacrylic acid in presence of chitosan and polyether in a solvent/surfactant free medium. Dynamic light scattering (DLS) experiment was conducted with particles dispersed in phosphate buffer (pH 7.4) and size distribution curve was observed in the range of 500-800 nm. HPbetaCD was used to prepare non-covalent inclusion complex with insulin and complex was analyzed by Fourier transform infrared (FTIR) and fluorescence spectroscopic studies. HPbetaCD complexed insulin was encapsulated into PMCP nanoparticles by diffusion filling method and their in vitro release profile was evaluated at acidic/alkaline pH. PMCP nanoparticles displayed good insulin encapsulation efficiency and release profile was largely dependent on the pH of the medium. Enzyme linked immunosorbent assay (ELISA) study demonstrated that insulin encapsulated inside the particles was biologically active. Trypsin inhibitory effect of PMCP nanoparticles was evaluated using N-alpha-benzoyl-L-arginine ethyl ester (BAEE) and casein as substrates. Mucoadhesive studies of PMCP nanoparticles were conducted using freshly excised rat intestinal mucosa and the particles were found fairly adhesive. From the preliminary studies, cyclodextrin complexed insulin encapsulated mucoadhesive nanoparticles appear to be a good candidate for oral insulin delivery.

Absorption spectroscopic studies of Np(IV) complexes

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

Reed, D. T.

2004-01-01

The complexation of neptunium (IV) with selected inorganic and organic ligands was studied as part of an investigation to establish key subsurface interactions between neptunium and biological systems. The prevalence of reducing environments in most subsurface migation scenarios, which are in many cases induced by biological activity, has increased the role and importance of Np(IV) as a key subsurface neptunium oxidation state. The biodegradation of larger organics that often coexist with actinides in the subsurface leads to the formation of many organic acids as transient products that, by complexation, play a key role in defining the fate and speciation ofmore » neptunium in biologically active systems. These often compete with inorganic complexes e.g. hydrolysis and phosphate. Herein we report the results of a series of complexation studies based on new band formation of the characteristic 960 nm band for Np(IV). Formation constants for Np(IV) complexes with phosphate, hydrolysis, succinate, acetohydroxamic acid, and acetate were determined. These results show the 960 nm absorption band to be very amenable to these types of complexation studies.« less

Biological roles of glycans

PubMed Central

Varki, Ajit

2017-01-01

Abstract Simple and complex carbohydrates (glycans) have long been known to play major metabolic, structural and physical roles in biological systems. Targeted microbial binding to host glycans has also been studied for decades. But such biological roles can only explain some of the remarkable complexity and organismal diversity of glycans in nature. Reviewing the subject about two decades ago, one could find very few clear-cut instances of glycan-recognition-specific biological roles of glycans that were of intrinsic value to the organism expressing them. In striking contrast there is now a profusion of examples, such that this updated review cannot be comprehensive. Instead, a historical overview is presented, broad principles outlined and a few examples cited, representing diverse types of roles, mediated by various glycan classes, in different evolutionary lineages. What remains unchanged is the fact that while all theories regarding biological roles of glycans are supported by compelling evidence, exceptions to each can be found. In retrospect, this is not surprising. Complex and diverse glycans appear to be ubiquitous to all cells in nature, and essential to all life forms. Thus, >3 billion years of evolution consistently generated organisms that use these molecules for many key biological roles, even while sometimes coopting them for minor functions. In this respect, glycans are no different from other major macromolecular building blocks of life (nucleic acids, proteins and lipids), simply more rapidly evolving and complex. It is time for the diverse functional roles of glycans to be fully incorporated into the mainstream of biological sciences. PMID:27558841

Profiling Bioactivity of the ToxCast Chemical Library Using BioMAP Primary Human Cell Systems

EPA Science Inventory

The complexity of human biology has made prediction of health effects as a consequence of exposure to environmental chemicals especially challenging. Complex cell systems, such as the Biologically Multiplexed Activity Profiling (BioMAP) primary, human, cell-based disease models, ...

Ask not what physics can do for biology--ask what biology can do for physics.

PubMed

Frauenfelder, Hans

2014-10-08

Stan Ulam, the famous mathematician, said once to Hans Frauenfelder: 'Ask not what Physics can do for biology, ask what biology can do for physics'. The interaction between biologists and physicists is a two-way street. Biology reveals the secrets of complex systems, physics provides the physical tools and the theoretical concepts to understand the complexity. The perspective gives a personal view of the path to some of the physical concepts that are relevant for biology and physics (Frauenfelder et al 1999 Rev. Mod. Phys. 71 S419-S442). Schrödinger's book (Schrödinger 1944 What is Life? (Cambridge: Cambridge University Press)), loved by physicists and hated by eminent biologists (Dronamraju 1999 Genetics 153 1071-6), still shows how a great physicist looked at biology well before the first protein structure was known.

The Quantitative Methods Boot Camp: Teaching Quantitative Thinking and Computing Skills to Graduate Students in the Life Sciences

PubMed Central

Stefan, Melanie I.; Gutlerner, Johanna L.; Born, Richard T.; Springer, Michael

2015-01-01

The past decade has seen a rapid increase in the ability of biologists to collect large amounts of data. It is therefore vital that research biologists acquire the necessary skills during their training to visualize, analyze, and interpret such data. To begin to meet this need, we have developed a “boot camp” in quantitative methods for biology graduate students at Harvard Medical School. The goal of this short, intensive course is to enable students to use computational tools to visualize and analyze data, to strengthen their computational thinking skills, and to simulate and thus extend their intuition about the behavior of complex biological systems. The boot camp teaches basic programming using biological examples from statistics, image processing, and data analysis. This integrative approach to teaching programming and quantitative reasoning motivates students’ engagement by demonstrating the relevance of these skills to their work in life science laboratories. Students also have the opportunity to analyze their own data or explore a topic of interest in more detail. The class is taught with a mixture of short lectures, Socratic discussion, and in-class exercises. Students spend approximately 40% of their class time working through both short and long problems. A high instructor-to-student ratio allows students to get assistance or additional challenges when needed, thus enhancing the experience for students at all levels of mastery. Data collected from end-of-course surveys from the last five offerings of the course (between 2012 and 2014) show that students report high learning gains and feel that the course prepares them for solving quantitative and computational problems they will encounter in their research. We outline our course here which, together with the course materials freely available online under a Creative Commons License, should help to facilitate similar efforts by others. PMID:25880064

The quantitative methods boot camp: teaching quantitative thinking and computing skills to graduate students in the life sciences.

PubMed

Stefan, Melanie I; Gutlerner, Johanna L; Born, Richard T; Springer, Michael

2015-04-01

The past decade has seen a rapid increase in the ability of biologists to collect large amounts of data. It is therefore vital that research biologists acquire the necessary skills during their training to visualize, analyze, and interpret such data. To begin to meet this need, we have developed a "boot camp" in quantitative methods for biology graduate students at Harvard Medical School. The goal of this short, intensive course is to enable students to use computational tools to visualize and analyze data, to strengthen their computational thinking skills, and to simulate and thus extend their intuition about the behavior of complex biological systems. The boot camp teaches basic programming using biological examples from statistics, image processing, and data analysis. This integrative approach to teaching programming and quantitative reasoning motivates students' engagement by demonstrating the relevance of these skills to their work in life science laboratories. Students also have the opportunity to analyze their own data or explore a topic of interest in more detail. The class is taught with a mixture of short lectures, Socratic discussion, and in-class exercises. Students spend approximately 40% of their class time working through both short and long problems. A high instructor-to-student ratio allows students to get assistance or additional challenges when needed, thus enhancing the experience for students at all levels of mastery. Data collected from end-of-course surveys from the last five offerings of the course (between 2012 and 2014) show that students report high learning gains and feel that the course prepares them for solving quantitative and computational problems they will encounter in their research. We outline our course here which, together with the course materials freely available online under a Creative Commons License, should help to facilitate similar efforts by others.

PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization.

PubMed

Dogrusoz, U; Erson, E Z; Giral, E; Demir, E; Babur, O; Cetintas, A; Colak, R

2006-02-01

Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.

Influence of Using Challenging Tasks in Biology Classrooms on Students' Cognitive Knowledge Structure: An Empirical Video Study

ERIC Educational Resources Information Center

Nawani, Jigna; Rixius, Julia; Neuhaus, Birgit J.

2016-01-01

Empirical analysis of secondary biology classrooms revealed that, on average, 68% of teaching time in Germany revolved around processing tasks. Quality of instruction can thus be assessed by analyzing the quality of tasks used in classroom discourse. This quasi-experimental study analyzed how teachers used tasks in 38 videotaped biology lessons…

Electrochemical Biosensors - Sensor Principles and Architectures

PubMed Central

Grieshaber, Dorothee; MacKenzie, Robert; Vörös, Janos; Reimhult, Erik

2008-01-01

Quantification of biological or biochemical processes are of utmost importance for medical, biological and biotechnological applications. However, converting the biological information to an easily processed electronic signal is challenging due to the complexity of connecting an electronic device directly to a biological environment. Electrochemical biosensors provide an attractive means to analyze the content of a biological sample due to the direct conversion of a biological event to an electronic signal. Over the past decades several sensing concepts and related devices have been developed. In this review, the most common traditional techniques, such as cyclic voltammetry, chronoamperometry, chronopotentiometry, impedance spectroscopy, and various field-effect transistor based methods are presented along with selected promising novel approaches, such as nanowire or magnetic nanoparticle-based biosensing. Additional measurement techniques, which have been shown useful in combination with electrochemical detection, are also summarized, such as the electrochemical versions of surface plasmon resonance, optical waveguide lightmode spectroscopy, ellipsometry, quartz crystal microbalance, and scanning probe microscopy. The signal transduction and the general performance of electrochemical sensors are often determined by the surface architectures that connect the sensing element to the biological sample at the nanometer scale. The most common surface modification techniques, the various electrochemical transduction mechanisms, and the choice of the recognition receptor molecules all influence the ultimate sensitivity of the sensor. New nanotechnology-based approaches, such as the use of engineered ion-channels in lipid bilayers, the encapsulation of enzymes into vesicles, polymersomes, or polyelectrolyte capsules provide additional possibilities for signal amplification. In particular, this review highlights the importance of the precise control over the delicate interplay between surface nano-architectures, surface functionalization and the chosen sensor transducer principle, as well as the usefulness of complementary characterization tools to interpret and to optimize the sensor response. PMID:27879772

A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks

PubMed Central

Khammash, Mustafa

2014-01-01

Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed. PMID:24968191

The effects of morphological irregularity on the mechanical behavior of interdigitated biological sutures under tension.

PubMed

Liu, Lei; Jiang, Yunyao; Boyce, Mary; Ortiz, Christine; Baur, Jeffery; Song, Juha; Li, Yaning

2017-06-14

Irregular interdigitated morphology is prevalent in biological sutures in nature. Suture complexity index has long been recognized as the most important morphological parameter to govern the mechanical properties of biological sutures. However, the suture complexity index alone does not reflect all aspects of suture morphology. The goal of this investigation was to determine that besides suture complexity index, whether the degree of morphological irregularity of biological sutures has influences on the mechanical properties, and if there is any, how to quantify these influences. To explore these issues, theoretical and finite element (FE) suture models with the same suture complexity index but different levels of morphological irregularity were developed. The quasi-static stiffness, strength for damage initiation and post-failure process of irregular sutures were studied. It was shown that for the same suture complexity index, when the level of morphological irregularity increases, the overall strain to failure will increase while tensile stiffness is retained; also, the total energy to fracture increases with a sacrifice in strength to damage initiation. These results reveal that morphological irregularity is another important independent parameter to govern and balance the mechanical properties of biological sutures. Therefore, from the mechanics point of view, the prevalence of irregular suture morphology in nature is a merit, not a defect. Copyright © 2017 Elsevier Ltd. All rights reserved.

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis

DOE PAGES

Sakhanenko, Nikita A.; Kunert-Graf, James; Galas, David J.

2017-10-13

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. Here, we present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discretemore » variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis—that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. Finally, we illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.« less

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis

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

Sakhanenko, Nikita A.; Kunert-Graf, James; Galas, David J.

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. Here, we present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discretemore » variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis—that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. Finally, we illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.« less

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis.

PubMed

Sakhanenko, Nikita A; Kunert-Graf, James; Galas, David J

2017-12-01

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. We present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discrete variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis-that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. We illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.

A mutation screening of oncogenes, tumor suppressor gene TP53 and nuclear encoded mitochondrial complex I genes in oncocytic thyroid tumors.

PubMed

Evangelisti, Cecilia; de Biase, Dario; Kurelac, Ivana; Ceccarelli, Claudio; Prokisch, Holger; Meitinger, Thomas; Caria, Paola; Vanni, Roberta; Romeo, Giovanni; Tallini, Giovanni; Gasparre, Giuseppe; Bonora, Elena

2015-03-21

Thyroid neoplasias with oncocytic features represent a specific phenotype in non-medullary thyroid cancer, reflecting the unique biological phenomenon of mitochondrial hyperplasia in the cytoplasm. Oncocytic thyroid cells are characterized by a prominent eosinophilia (or oxyphilia) caused by mitochondrial abundance. Although disruptive mutations in the mitochondrial DNA (mtDNA) are the most significant hallmark of such tumors, oncocytomas may be envisioned as heterogeneous neoplasms, characterized by multiple nuclear and mitochondrial gene lesions. We investigated the nuclear mutational profile of oncocytic tumors to pinpoint the mutations that may trigger the early oncogenic hit. Total DNA was extracted from paraffin-embedded tissues from 45 biopsies of oncocytic tumors. High-resolution melting was used for mutation screening of mitochondrial complex I subunits genes. Specific nuclear rearrangements were investigated by RT-PCR (RET/PTC) or on isolated nuclei by interphase FISH (PAX8/PPARγ). Recurrent point mutations were analyzed by direct sequencing. In our oncocytic tumor samples, we identified rare TP53 mutations. The series of analyzed cases did not include poorly- or undifferentiated thyroid carcinomas, and none of the TP53 mutated cases had significant mitotic activity or high-grade features. Thus, the presence of disruptive TP53 mutations was completely unexpected. In addition, novel mutations in nuclear-encoded complex I genes were identified. These findings suggest that nuclear genetic lesions altering the bioenergetics competence of thyroid cells may give rise to an aberrant mitochondria-centered compensatory mechanism and ultimately to the oncocytic phenotype.

Analyzing Data for Systems Biology: Working at the Intersection of Thermodynamics and Data Analytics

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

Cannon, William R.; Baxter, Douglas J.

2012-08-15

Many challenges in systems biology have to do with analyzing data within the framework of molecular phenomena and cellular pathways. How does this relate to thermodynamics that we know govern the behavior of molecules? Making progress in relating data analysis to thermodynamics is essential in systems biology if we are to build predictive models that enable the field of synthetic biology. This report discusses work at the crossroads of thermodynamics and data analysis, and demonstrates that statistical mechanical free energy is a multinomial log likelihood. Applications to systems biology are presented.

Rapid Profiling of Bovine and Human Milk Gangliosides by Matrix-Assisted Laser Desorption/Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

PubMed Central

Lee, Hyeyoung; An, Hyun Joo; Lerno, Larry A.; German, J. Bruce; Lebrilla, Carlito B.

2010-01-01

Gangliosides are anionic glycosphingolipids widely distributed in vertebrate tissues and fluids. Their structural and quantitative expression patterns depend on phylogeny and are distinct down to the species level. In milk, gangliosides are exclusively associated with the milk fat globule membrane. They may participate in diverse biological processes but more specifically to host-pathogen interactions. However, due to the molecular complexities, the analysis needs extensive sample preparation, chromatographic separation, and even chemical reaction, which makes the process very complex and time-consuming. Here, we describe a rapid profiling method for bovine and human milk gangliosides employing matrix-assisted desorption/ionization (MALDI) Fourier transform ion cyclotron resonance (FTICR) mass spectrometry (MS). Prior to the analyses of biological samples, milk ganglioside standards GM3 and GD3 fractions were first analyzed in order to validate this method. High mass accuracy and high resolution obtained from MALDI FTICR MS allow for the confident assignment of chain length and degree of unsaturation of the ceramide. For the structural elucidation, tandem mass spectrometry (MS/MS), specifically as collision-induced dissociation (CID) and infrared multiphoton dissociation (IRMPD) were employed. Complex ganglioside mixtures from bovine and human milk were further analyzed with this method. The samples were prepared by two consecutive chloroform/methanol extraction and solid phase extraction. We observed a number of differences between bovine milk and human milk. The common gangliosides in bovine and human milk are NeuAc-NeuAc-Hex-Hex-Cer (GD3) and NeuAc-Hex-Hex-Cer (GM3); whereas, the ion intensities of ganglioside species are different between two milk samples. Kendrick mass defect plot yields grouping of ganglioside peaks according to their structural similarities. Gangliosides were further probed by tandem MS to confirm the compositional and structural assignments. We found that only in human milk gangliosides was the ceramide carbon always even numbered, which is consistent with the notion that differences in the oligosaccharide and the ceramide moieties confer to their physiological distinctions. PMID:21860602

Petri net modelling of biological networks.

PubMed

Chaouiya, Claudine

2007-07-01

Mathematical modelling is increasingly used to get insights into the functioning of complex biological networks. In this context, Petri nets (PNs) have recently emerged as a promising tool among the various methods employed for the modelling and analysis of molecular networks. PNs come with a series of extensions, which allow different abstraction levels, from purely qualitative to more complex quantitative models. Noteworthily, each of these models preserves the underlying graph, which depicts the interactions between the biological components. This article intends to present the basics of the approach and to foster the potential role PNs could play in the development of the computational systems biology.

Anion-exchange high-performance liquid chromatography of water-soluble chromium (VI) and chromium (III) complexes in biological materials.

PubMed

Suzuki, Y

1987-04-10

A high-performance anion-exchange liquid chromatograph coupled to visible-range (370 nm) and UV (280 nm) detectors and an atomic-absorption spectrometer allowed the rapid determination of CrVI and/or complexes of CrIII in rat plasma, erythrocyte lysate and liver supernatant treated with CrVI or CrIII in vitro. CrVI in the eluates was determined using both the visible-range detector and atomic-absorption spectrometer (AAS). The detection limits of CrVI in standard solutions using these methods were 2 and 5 ng (signal-to-noise ratio = 2), respectively. Separations of the biological components and of CrIII complexes were monitored by UV and AAS analyses, respectively. Time-related decreases of CrVI accompanied by increases in CrIII complexes were observed, indicating the reduction of CrVI by some of the biological components. The reduction rates were considerably higher in the liver supernatant and erythrocyte lysate than in the plasma. These results indicate that the anion-exchange high-performance liquid chromatographic system is useful for simultaneous determination of CrVI and CrIII complexes in biological materials.

Technetium-99m and rhenium-188 complexes with one and two pendant bisphosphonate groups for imaging arterial calcification.

PubMed

Bordoloi, Jayanta Kumar; Berry, David; Khan, Irfan Ullah; Sunassee, Kavitha; de Rosales, Rafael Torres Martin; Shanahan, Catherine; Blower, Philip J

2015-03-21

The first (99m)Tc and (188)Re complexes containing two pendant bisphosphonate groups have been synthesised, based on the mononuclear M(v) nitride core with two dithiocarbamate ligands each with a pendant bisphosphonate. The structural identity of the (99)Tc and stable rhenium analogues as uncharged, mononuclear nitridobis(dithiocarbamate) complexes was determined by electrospray mass spectrometry. The (99m)Tc complex showed greater affinity for synthetic and biological hydroxyapatite, and greater stability in biological media, than the well-known but poorly-characterised and inhomogeneous bone imaging agent (99m)Tc-MDP. It gave excellent SPECT images of both bone calcification (mice and rats) and vascular calcification (rat model), but the improved stability and the availability of two pendant bisphosphonate groups conferred no dramatic advantage in imaging over the conventional (99m)Tc-MDP agent in which the bisphosphonate group is bound directly to Tc. The (188)Re complex also showed preferential uptake in bone. These tracers and the biological model of vascular calcification offer the opportunity to study the biological interpretation and clinical potential of radionuclide imaging of vascular calcification and to deliver radionuclide therapy to bone metastases.

Quantitative computational models of molecular self-assembly in systems biology

PubMed Central

Thomas, Marcus; Schwartz, Russell

2017-01-01

Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally. PMID:28535149

Quantitative computational models of molecular self-assembly in systems biology.

PubMed

Thomas, Marcus; Schwartz, Russell

2017-05-23

Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally.

A probabilistic framework for identifying biosignatures using Pathway Complexity

NASA Astrophysics Data System (ADS)

Marshall, Stuart M.; Murray, Alastair R. G.; Cronin, Leroy

2017-11-01

One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configurations in biology can easily be distinguished from non-living material assemblies. Many complex artefacts, from ordinary bioproducts to human tools, though they are not living things, are ultimately produced by biological processes-whether those processes occur at the scale of cells or societies, they are the consequences of living systems. While these objects are not living, they cannot randomly form, as they are the product of a biological organism and hence are either technological or cultural biosignatures. A generalized approach that aims to evaluate complex objects as possible biosignatures could be useful to explore the cosmos for new life forms. However, it is not obvious how it might be possible to create such a self-contained approach. This would require us to prove rigorously that a given artefact is too complex to have formed by chance. In this paper, we present a new type of complexity measure, which we call `Pathway Complexity', that allows us not only to threshold the abiotic-biotic divide, but also to demonstrate a probabilistic approach based on object abundance and complexity which can be used to unambiguously assign complex objects as biosignatures. We hope that this approach will not only open up the search for biosignatures beyond the Earth, but also allow us to explore the Earth for new types of biology, and to determine when a complex chemical system discovered in the laboratory could be considered alive. This article is part of the themed issue 'Reconceptualizing the origins of life'.

Student Performance along Axes of Scenario Novelty and Complexity in Introductory Biology: Lessons from a Unique Factorial Approach to Assessment.

PubMed

Deane-Coe, Kirsten K; Sarvary, Mark A; Owens, Thomas G

2017-01-01

In an undergraduate introductory biology laboratory course, we used a summative assessment to directly test the learning objective that students will be able to apply course material to increasingly novel and complex situations. Using a factorial framework, we developed multiple true-false questions to fall along axes of novelty and complexity, which resulted in four categories of questions: familiar content and low complexity (category A); novel content and low complexity (category B); familiar content and high complexity (category C); and novel content and high complexity (category D). On average, students scored more than 70% on all questions, indicating that the course largely met this learning objective. However, students scored highest on questions in category A, likely because they were most similar to course content, and lowest on questions in categories C and D. While we anticipated students would score equally on questions for which either novelty or complexity was altered (but not both), we observed that student scores in category C were lower than in category B. Furthermore, students performed equally poorly on all questions for which complexity was higher (categories C and D), even those containing familiar content, suggesting that application of course material to increasingly complex situations is particularly challenging to students. © 2017 K. K. Deane-Coe et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Somatic evolution of head and neck cancer - biological robustness and latent vulnerability.

PubMed

Masuda, Muneyuki; Toh, Satoshi; Wakasaki, Takahiro; Suzui, Masumi; Joe, Andrew K

2013-02-01

Despite recent advancements in multidisciplinary treatments, the overall survival and quality of life of patients with advanced head and neck squamous cell carcinoma (HNSCC) have not improved significantly over the past decade. Molecular targeted therapies, which have been addressed and advanced by the concept of "oncogene addiction", have demonstrated only limited successes so far. To explore a novel clue for clinically effective targeted therapies, we analyzed the molecular circuitry of HNSCC through the lens that HNSCC is an evolving system. In the trajectory of this somatic evolution, HNSCC acquires biological robustness under a variety of selective pressures including genetic, epigenetic, micro-environmental and metabolic stressors, which well explains the major mechanism of "escaping from oncogene addiction". On the other hand, this systemic view appears to instruct us approaches to target latent vulnerability of HNSCC that is masked behind the plasticity and evolvability of this complex adaptive system. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

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.

In vivo degeneration and the fate of inorganic nanoparticles.

PubMed

Feliu, Neus; Docter, Dominic; Heine, Markus; Del Pino, Pablo; Ashraf, Sumaira; Kolosnjaj-Tabi, Jelena; Macchiarini, Paolo; Nielsen, Peter; Alloyeau, Damien; Gazeau, Florence; Stauber, Roland H; Parak, Wolfgang J

2016-05-03

What happens to inorganic nanoparticles (NPs), such as plasmonic gold or silver, superparamagnetic iron oxide, or fluorescent quantum dot NPs after they have been administrated to a living being? This review discusses the integrity, biodistribution, and fate of NPs after in vivo administration. The hybrid nature of the NPs is described, conceptually divided into the inorganic core, the engineered surface coating comprising of the ligand shell and optionally also bio-conjugates, and the corona of adsorbed biological molecules. Empirical evidence shows that all of these three compounds may degrade individually in vivo and can drastically modify the life cycle and biodistribution of the whole heterostructure. Thus, the NPs may be decomposed into different parts, whose biodistribution and fate would need to be analyzed individually. Multiple labeling and quantification strategies for such a purpose will be discussed. All reviewed data indicate that NPs in vivo should no longer be considered as homogeneous entities, but should be seen as inorganic/organic/biological nano-hybrids with complex and intricately linked distribution and degradation pathways.

Reverse Genetics and High Throughput Sequencing Methodologies for Plant Functional Genomics

PubMed Central

Ben-Amar, Anis; Daldoul, Samia; Reustle, Götz M.; Krczal, Gabriele; Mliki, Ahmed

2016-01-01

In the post-genomic era, increasingly sophisticated genetic tools are being developed with the long-term goal of understanding how the coordinated activity of genes gives rise to a complex organism. With the advent of the next generation sequencing associated with effective computational approaches, wide variety of plant species have been fully sequenced giving a wealth of data sequence information on structure and organization of plant genomes. Since thousands of gene sequences are already known, recently developed functional genomics approaches provide powerful tools to analyze plant gene functions through various gene manipulation technologies. Integration of different omics platforms along with gene annotation and computational analysis may elucidate a complete view in a system biology level. Extensive investigations on reverse genetics methodologies were deployed for assigning biological function to a specific gene or gene product. We provide here an updated overview of these high throughout strategies highlighting recent advances in the knowledge of functional genomics in plants. PMID:28217003

Current trends in quantitative proteomics - an update.

PubMed

Li, H; Han, J; Pan, J; Liu, T; Parker, C E; Borchers, C H

2017-05-01

Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Cross species analysis of microarray expression data

PubMed Central

Lu, Yong; Huggins, Peter; Bar-Joseph, Ziv

2009-01-01

Motivation: Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied. Results: In this review we discuss the computational and technical challenges associated with these studies, the approaches that have been developed to address these challenges and the advantages of cross-species analysis of microarray data. We show how successful application of these methods lead to insights that cannot be obtained when analyzing data from a single species. We also highlight current open problems and discuss possible ways to address them. Contact: zivbj@cs.cmu.edu PMID:19357096

From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota.

PubMed

Bauer, Eugen; Thiele, Ines

2018-01-01

An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.

Theoretical Calculation of the Uv-Vis Spectral Band Locations of Pahs with Unknown Syntheses Procedures and Prospective Carcinogenic Activity

NASA Astrophysics Data System (ADS)

Ona-Ruales, Jorge Oswaldo; Ruiz-Morales, Yosadara

2017-06-01

Annellation Theory and ZINDO/S semiempirical calculations have been used for the calculation of the locations of maximum absorbance (LMA) of the Ultraviolet-Visible (UV-Vis) of 31 C_{34}H_{16} PAHs (molecular mass 424 Da) with unknown protocols of synthesis. The presence of benzo[a]pyrene bay-like regions and dibenzo[a,l]pyrene fjord-like regions in several of the structures that could be linked to an enhancement of the biological behavior and carcinogenic activity stresses the importance of C_{34}H_{16} PAHs in fields like molecular biology and cancer research. In addition, the occurrence of large PAHs in oil asphaltenes exemplifies the importance of these calculations for the characterization of complex systems. The C_{34}H_{16} PAH group is the largest molecular mass group of organic compounds analyzed so far following the Annellation Theory and ZINDO/S methodology. Future analysis using the same approach will provide evidence regarding the LMA of other high molecular mass PAHs.

Explorative solid-phase extraction (E-SPE) for accelerated microbial natural product discovery, dereplication, and purification.

PubMed

Månsson, Maria; Phipps, Richard K; Gram, Lone; Munro, Murray H G; Larsen, Thomas O; Nielsen, Kristian F

2010-06-25

Microbial natural products (NP) cover a high chemical diversity, and in consequence extracts from microorganisms are often complex to analyze and purify. A distribution analysis of calculated pK(a) values from the 34390 records in Antibase2008 revealed that within pH 2-11, 44% of all included compounds had an acidic functionality, 17% a basic functionality, and 9% both. This showed a great potential for using ion-exchange chromatography as an integral part of the separation procedure, orthogonal to the classic reversed-phase strategy. Thus, we investigated the use of an "explorative solid-phase extraction" (E-SPE) protocol using SAX, Oasis MAX, SCX, and LH-20 columns for targeted exploitation of chemical functionalities. E-SPE provides a minimum of fractions (15) for chemical and biological analyses and implicates development into a preparative scale methodology. Overall, this allows fast extract prioritization, easier dereplication, mapping of biological activities, and formulation of a purification strategy.

Transmission of biology and culture among post-contact Native Americans on the western Great Plains.

PubMed

Lycett, Stephen J; von Cramon-Taubadel, Noreen

2016-08-12

The transmission of genes and culture between human populations has major implications for understanding potential correlations between history, biological, and cultural variation. Understanding such dynamics in 19th century, post-contact Native Americans on the western Great Plains is especially challenging given passage of time, complexity of known dynamics, and difficulties of determining genetic patterns in historical populations for whom, even today, genetic data for their descendants are rare. Here, biometric data collected under the direction of Franz Boas from communities penecontemporaneous with the classic bison-hunting societies, were used as a proxy for genetic variation and analyzed together with cultural data. We show that both gene flow and "culture flow" among populations on the High Plains were mediated by geography, fitting a model of isolation-by-distance. Moreover, demographic and cultural exchange among these communities largely overrode the visible signal of the prior millennia of cultural and genetic histories of these populations.

Specificity of Intramembrane Protein–Lipid Interactions

PubMed Central

Contreras, Francesc-Xabier; Ernst, Andreas Max; Wieland, Felix; Brügger, Britta

2011-01-01

Our concept of biological membranes has markedly changed, from the fluid mosaic model to the current model that lipids and proteins have the ability to separate into microdomains, differing in their protein and lipid compositions. Since the breakthrough in crystallizing membrane proteins, the most powerful method to define lipid-binding sites on proteins has been X-ray and electron crystallography. More recently, chemical biology approaches have been developed to analyze protein–lipid interactions. Such methods have the advantage of providing highly specific cellular probes. With the advent of novel tools to study functions of individual lipid species in membranes together with structural analysis and simulations at the atomistic resolution, a growing number of specific protein–lipid complexes are defined and their functions explored. In the present article, we discuss the various modes of intramembrane protein–lipid interactions in cellular membranes, including examples for both annular and nonannular bound lipids. Furthermore, we will discuss possible functional roles of such specific protein–lipid interactions as well as roles of lipids as chaperones in protein folding and transport. PMID:21536707

Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model

NASA Astrophysics Data System (ADS)

Ehler, Martin; Rajapakse, Vinodh; Zeeberg, Barry; Brooks, Brian; Brown, Jacob; Czaja, Wojciech; Bonner, Robert F.

The gene networks underlying closure of the optic fissure during vertebrate eye development are poorly understood. We used a novel clustering method based on Laplacian Eigenmaps, a nonlinear dimension reduction method, to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental stages (days 10.5 to 12.5) of optic fissure closure. Our new method provided greater biological specificity than classical clustering algorithms in terms of identifying more biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery rates. This new methodology builds on the advantages of LCM to isolate pure phenotypic populations within complex tissues and allows improved ability to identify critical gene products expressed at lower copy number. The combination of LCM of embryonic organs, gene expression microarrays, and extracting spatial and temporal co-variations appear to be a powerful approach to understanding the gene regulatory networks that specify mammalian organogenesis.

Gene chips and arrays revealed: a primer on their power and their uses.

PubMed

Watson, S J; Akil, H

1999-03-01

This article provides an overview and general explanation of the rapidly developing area of gene chips and expression array technology. These are methods targeted at allowing the simultaneous study of thousands of genes or messenger RNAs under various physiological and pathological states. Their technical basis grows from the Human Genome Project. Both methods place DNA strands on glass computer chips (or microscope slides). Expression arrays start with complementary DNA (cDNA) clones derived from the EST data base, whereas Gene Chips synthesize oligonucleotides directly on the chip itself. Both are analyzed using image analysis systems, are capable of reading values from two different individuals at any one site, and can yield quantitative data for thousands of genes or mRNAs per slide. These methods promise to revolutionize molecular biology, cell biology, neuroscience and psychiatry. It is likely that this technology will radically open up our ability to study the actions and structure of the multiple genes involved in the complex genetics of brain disorders.

Bacterial RNA Biology on a Genome Scale.

PubMed

Hör, Jens; Gorski, Stanislaw A; Vogel, Jörg

2018-06-07

Bacteria are an exceedingly diverse group of organisms whose molecular exploration is experiencing a renaissance. While the classical view of bacterial gene expression was relatively simple, the emerging view is more complex, encompassing extensive post-transcriptional control involving riboswitches, RNA thermometers, and regulatory small RNAs (sRNAs) associated with the RNA-binding proteins CsrA, Hfq, and ProQ, as well as CRISPR/Cas systems that are programmed by RNAs. Moreover, increasing interest in members of the human microbiota and environmental microbial communities has highlighted the importance of understudied bacterial species with largely unknown transcriptome structures and RNA-based control mechanisms. Collectively, this creates a need for global RNA biology approaches that can rapidly and comprehensively analyze the RNA composition of a bacterium of interest. We review such approaches with a focus on RNA-seq as a versatile tool to investigate the different layers of gene expression in which RNA is made, processed, regulated, modified, translated, and turned over. Copyright © 2017 Elsevier Inc. All rights reserved.

FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

PubMed

Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

2014-10-01

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

Recurrence plot for parameters analysing of internal combustion engine

NASA Astrophysics Data System (ADS)

Alexa, O.; Ilie, C. O.; Marinescu, M.; Vilau, R.; Grosu, D.

2015-11-01

In many technical disciplines modem data analysis techniques has been successfully applied to understand the complexity of the system. The growing volume of theoretical knowledge about systems dynamic's offered researchers the opportunity to look for non-linear dynamics in data whose evolution linear models are unable to explain in a satisfactory manner. One approach in this respect is Recurrence Analysis - RA which is a graphical method designed to locate hidden recurring patterns, nonstationarity and structural changes. RA approach arose in natural sciences like physics and biology but quickly was adopted in economics and engineering. Meanwhile. The fast development of computer resources has provided powerful tools to perform this new and complex model. One free software which was used to perform our analysis is Visual Recurrence Analysis - VRA developed by Eugene Kononov. As is presented in this paper, the recurrence plot investigation for the analyzing of the internal combustion engine shows some of the RPA capabilities in this domain. We chose two specific engine parameters measured in two different tests to perform the RPA. These parameters are injection impulse width and engine angular speed and the tests are I11n and I51n. There were computed graphs for each of them. Graphs were analyzed and compared to obtain a conclusion. This work is an incipient research, being one of the first attempts of using recurrence plot for analyzing automotive dynamics. It opens a wide field of action for future research programs.

Synthetic biology approaches in cancer immunotherapy, genetic network engineering, and genome editing.

PubMed

Chakravarti, Deboki; Cho, Jang Hwan; Weinberg, Benjamin H; Wong, Nicole M; Wong, Wilson W

2016-04-18

Investigations into cells and their contents have provided evolving insight into the emergence of complex biological behaviors. Capitalizing on this knowledge, synthetic biology seeks to manipulate the cellular machinery towards novel purposes, extending discoveries from basic science to new applications. While these developments have demonstrated the potential of building with biological parts, the complexity of cells can pose numerous challenges. In this review, we will highlight the broad and vital role that the synthetic biology approach has played in applying fundamental biological discoveries in receptors, genetic circuits, and genome-editing systems towards translation in the fields of immunotherapy, biosensors, disease models and gene therapy. These examples are evidence of the strength of synthetic approaches, while also illustrating considerations that must be addressed when developing systems around living cells.

Mammalian Cardiovascular Patterning as Determined by Hemodynamic Forces and Blood Vessel Genetics

NASA Astrophysics Data System (ADS)

Anderson, Gregory Arthur

Cardiovascular development is a process that involves the timing of multiple molecular events, and numerous subtle three-dimensional conformational changes. Traditional developmental biology techniques have provided large quantities of information as to how these complex organ systems develop. However, the major drawback of the majority of current developmental biological imaging is that they are two-dimensional in nature. It is now well recognized that circulation of blood is required for normal patterning and remodeling of blood vessels. Normal blood vessel formation is dependent upon a complex network of signaling pathways, and genetic mutations in these pathways leads to impaired vascular development, heart failure, and lethality. As such, it is not surprising that mutant mice with aberrant cardiovascular patterning are so common, since normal development requires proper coordination between three systems: the heart, the blood, and the vasculature. This thesis describes the implementation of a three-dimensional imaging technique, optical projection tomography (OPT), in conjunction with a computer-based registration algorithm to statistically analyze developmental differences in groups of wild-type mouse embryos. Embryos that differ by only a few hours' gestational time are shown to have developmental differences in blood vessel formation and heart development progression that can be discerned. This thesis describes how we analyzed mouse models of cardiovascular perturbation by OPT to detect morphological differences in embryonic development in both qualitative and quantitative ways. Both a blood vessel specific mutation and a cardiac specific mutation were analyzed, providing evidence that developmental defects of these types can be quantified. Finally, we describe the implementation of OPT imaging to identify statistically significant phenotypes from three different mouse models of cardiovascular perturbation across a range of developmental time points. Image registration methods, combined with intensity- and deformation-based analyses are described and utilized to fully characterize myosin light chain 2a (Mlc2a), delta-like ligand 4 (Dll4), and Endoglin (Eng) mutant mouse embryos. We show that Eng mutant embryos are statistically similar to the Mlc2a phenotype, confirming that these mouse mutants suffer from a primary cardiac developmental defect. Thus, a loss of hemodynamic force caused by defective pumping of the heart is the primary developmental defect affecting these mice.

2D Hydrodynamic Based Logic Modeling Tool for River Restoration Decision Analysis: A Quantitative Approach to Project Prioritization

NASA Astrophysics Data System (ADS)

Bandrowski, D.; Lai, Y.; Bradley, N.; Gaeuman, D. A.; Murauskas, J.; Som, N. A.; Martin, A.; Goodman, D.; Alvarez, J.

2014-12-01

In the field of river restoration sciences there is a growing need for analytical modeling tools and quantitative processes to help identify and prioritize project sites. 2D hydraulic models have become more common in recent years and with the availability of robust data sets and computing technology, it is now possible to evaluate large river systems at the reach scale. The Trinity River Restoration Program is now analyzing a 40 mile segment of the Trinity River to determine priority and implementation sequencing for its Phase II rehabilitation projects. A comprehensive approach and quantitative tool has recently been developed to analyze this complex river system referred to as: 2D-Hydrodynamic Based Logic Modeling (2D-HBLM). This tool utilizes various hydraulic output parameters combined with biological, ecological, and physical metrics at user-defined spatial scales. These metrics and their associated algorithms are the underpinnings of the 2D-HBLM habitat module used to evaluate geomorphic characteristics, riverine processes, and habitat complexity. The habitat metrics are further integrated into a comprehensive Logic Model framework to perform statistical analyses to assess project prioritization. The Logic Model will analyze various potential project sites by evaluating connectivity using principal component methods. The 2D-HBLM tool will help inform management and decision makers by using a quantitative process to optimize desired response variables with balancing important limiting factors in determining the highest priority locations within the river corridor to implement restoration projects. Effective river restoration prioritization starts with well-crafted goals that identify the biological objectives, address underlying causes of habitat change, and recognizes that social, economic, and land use limiting factors may constrain restoration options (Bechie et. al. 2008). Applying natural resources management actions, like restoration prioritization, is essential for successful project implementation (Conroy and Peterson, 2013). Evaluating tradeoffs and examining alternatives to improve fish habitat through optimization modeling is not just a trend but rather the scientific strategy by which management needs embrace and apply in its decision framework.

Biological and Clinical Aspects of Lanthanide Coordination Compounds

PubMed Central

Misra, Sudhindra N.; M., Indira Devi; Shukla, Ram S.

2004-01-01

The coordinating chemistry of lanthanides, relevant to the biological, biochemical and medical aspects, makes a significant contribution to understanding the basis of application of lanthanides, particularly in biological and medical systems. The importance of the applications of lanthanides, as an excellent diagnostic and prognostic probe in clinical diagnostics, and an anticancer material, is remarkably increasing. Lanthanide complexes based X-ray contrast imaging and lanthanide chelates based contrast enhancing agents for magnetic resonance imaging (MRI) are being excessively used in radiological analysis in our body systems. The most important property of the chelating agents, in lanthanide chelate complex, is its ability to alter the behaviour of lanthanide ion with which it binds in biological systems, and the chelation markedly modifies the biodistribution and excretion profile of the lanthanide ions. The chelating agents, especially aminopoly carboxylic acids, being hydrophilic, increase the proportion of their complex excreted from complexed lanthanide ion form biological systems. Lanthanide polyamino carboxylate-chelate complexes are used as contrast enhancing agents for Magnetic Resonance Imaging. Conjugation of antibodies and other tissue specific molecules to lanthanide chelates has led to a new type of specific MRI contrast agents and their conjugated MRI contrast agents with improved relaxivity, functioning in the body similar to drugs. Many specific features of contrast agent assisted MRI make it particularly effective for musculoskeletal and cerebrospinal imaging. Lanthanide-chelate contrast agents are effectively used in clinical diagnostic investigations involving cerebrospinal diseases and in evaluation of central nervous system. Chelated lanthanide complexes shift reagent aided 23Na NMR spectroscopic analysis is used in cellular, tissue and whole organ systems. PMID:18365075

FIM, a Novel FTIR-Based Imaging Method for High Throughput Locomotion Analysis

PubMed Central

Otto, Nils; Löpmeier, Tim; Valkov, Dimitar; Jiang, Xiaoyi; Klämbt, Christian

2013-01-01

We designed a novel imaging technique based on frustrated total internal reflection (FTIR) to obtain high resolution and high contrast movies. This FTIR-based Imaging Method (FIM) is suitable for a wide range of biological applications and a wide range of organisms. It operates at all wavelengths permitting the in vivo detection of fluorescent proteins. To demonstrate the benefits of FIM, we analyzed large groups of crawling Drosophila larvae. The number of analyzable locomotion tracks was increased by implementing a new software module capable of preserving larval identity during most collision events. This module is integrated in our new tracking program named FIMTrack which subsequently extracts a number of features required for the analysis of complex locomotion phenotypes. FIM enables high throughput screening for even subtle behavioral phenotypes. We tested this newly developed setup by analyzing locomotion deficits caused by the glial knockdown of several genes. Suppression of kinesin heavy chain (khc) or rab30 function led to contraction pattern or head sweeping defects, which escaped in previous analysis. Thus, FIM permits forward genetic screens aimed to unravel the neural basis of behavior. PMID:23349775

Characterization, stoichiometry, and stability of salivary protein-tannin complexes by ESI-MS and ESI-MS/MS.

PubMed

Canon, Francis; Paté, Franck; Meudec, Emmanuelle; Marlin, Thérèse; Cheynier, Véronique; Giuliani, Alexandre; Sarni-Manchado, Pascale

2009-12-01

Numerous protein-polyphenol interactions occur in biological and food domains particularly involving proline-rich proteins, which are representative of the intrinsically unstructured protein group (IUP). Noncovalent protein-ligand complexes are readily detected by electrospray ionization mass spectrometry (ESI-MS), which also gives access to ligand binding stoichiometry. Surprisingly, the study of interactions between polyphenolic molecules and proteins is still an area where ESI-MS has poorly benefited, whereas it has been extensively applied to the detection of noncovalent complexes. Electrospray ionization mass spectrometry has been applied to the detection and the characterization of the complexes formed between tannins and a human salivary proline-rich protein (PRP), namely IB5. The study of the complex stability was achieved by low-energy collision-induced dissociation (CID) measurements, which are commonly implemented using triple quadrupole, hybrid quadrupole time-of-flight, or ion trap instruments. Complexes composed of IB5 bound to a model polyphenol EgCG have been detected by ESI-MS and further analyzed by MS/MS. Mild ESI interface conditions allowed us to observe intact noncovalent PRP-tannin complexes with stoichiometries ranging from 1:1 to 1:5. Thus, ESI-MS shows its efficiency for (1) the study of PRP-tannin interactions, (2) the determination of stoichiometry, and (3) the study of complex stability. We were able to establish unambiguously both their stoichiometries and their overall subunit architecture via tandem mass spectrometry and solution disruption experiments. Our results prove that IB5.EgCG complexes are maintained intact in the gas phase.

Informing biological design by integration of systems and synthetic biology.

PubMed

Smolke, Christina D; Silver, Pamela A

2011-03-18

Synthetic biology aims to make the engineering of biology faster and more predictable. In contrast, systems biology focuses on the interaction of myriad components and how these give rise to the dynamic and complex behavior of biological systems. Here, we examine the synergies between these two fields. Copyright © 2011 Elsevier Inc. All rights reserved.

Synthesis, characterization and biological activity of Cu(II), Ni(II) and Zn(II) complexes of biopolymeric Schiff bases of salicylaldehydes and chitosan.

PubMed

de Araújo, Eliene Leandro; Barbosa, Hellen Franciane Gonçalves; Dockal, Edward Ralph; Cavalheiro, Éder Tadeu Gomes

2017-02-01

Schiff bases have been prepared from biopolymer chitosan and salicylaldehyde, 5-methoxysalicylaldehyde, and 5-nitrosalicylaldehyde. Ligands were synthesized in a 1:1.5mol ratio, and their Cu(II), Ni(II) and Zn(II) complexes in a 1:1mol ratio (ligand:metal). Ligands were characterized by 1 H NMR and FTIR, resulting in degrees of substitution from 43.7 to 78.7%. Complexes were characterized using FTIR, electronic spectra, XPRD. The compounds were confirmed by the presence of an imine bond stretching in the 1630-1640cm -1 and νMetal-N and νMetal-O at <600cm -1 . Electronic spectra revealed that both Cu(II) and Ni(II) complexes present a square plane geometry. The crystallinity values were investigated by X-ray powder diffraction. Thermal behavior of all compounds was evaluated by TGA/DTG and DTA curves with mass losses related to dehydration and decomposition, with characteristic events for ligand and complexes. Schiff base complexes presented lower thermal stability and crystallinity than the starting chitosan. Residues were the metallic oxides as confirmed by XPRD, whose amounts were used in the calculation of the percentage of complexed metal ions. Surface morphologies were analyzed with SEM-EDAX. Preliminary cytotoxicity tests were performed using MTT assay with HeLa cells. Despite the differences in solubility, the free bases presented relatively low toxicity. Copyright © 2016 Elsevier B.V. All rights reserved.

Physicochemical impact studies of gamma rays on "aspirin" analgesics drug and its metal complexes in solid form: Synthesis, spectroscopic and biological assessment of Ca(II), Mg(II), Sr(II) and Ba(II) aspirinate complexes

NASA Astrophysics Data System (ADS)

Refat, Moamen S.; Sharshar, T.; Elsabawy, Khaled M.; Heiba, Zein K.

2013-09-01

Metal aspirinate complexes, M2(Asp)4, where M is Mg(II), Ca(II), Sr(II) or Ba(II) are formed by refluxed of aspirin (Asp) with divalent non-transition metal ions of group (II) and characterized by elemental analysis and spectroscopic measurements (infrared, electronic, 1H NMR, Raman, X-ray powder diffraction and scanning electron microscopy). Elemental analysis of the chelates suggests the stoichiometry is 1:2 (metal:ligand). Infrared spectra of the complexes agree with the coordination to the central metal atom through three donation sites of two oxygen atoms of bridge bidentate carboxylate group and oxygen atom of sbnd Cdbnd O of acetyl group. Infrared spectra coupled with the results of elemental analyzes suggested a distorted octahedral structure for the M(II) aspirinate complexes. Gamma irradiation was tested as a method for stabilization of aspirin as well as their complexes. The effect of gamma irradiation, with dose of 80 Gy, on the properties of aspirinate complexes was studied. The aspirinate chelates have been screened for their in vitro antibacterial activity against four bacteria, gram-positive (Bacillus subtilis and Staphylococcus aureus) and gram-negative (Escherichia coli and Pseudomonas aeruginosa) and two strains of fungus (Aspergillus flavus and Candida albicans). The metal chelates were shown to possess more antibacterial activity than the free aspirin chelate.

NF-κB transcriptional activity is modulated by FK506-binding proteins FKBP51 and FKBP52: a role for peptidyl-prolyl isomerase activity.

PubMed

Erlejman, Alejandra G; De Leo, Sonia A; Mazaira, Gisela I; Molinari, Alejandro M; Camisay, María Fernanda; Fontana, Vanina; Cox, Marc B; Piwien-Pilipuk, Graciela; Galigniana, Mario D

2014-09-19

Hsp90 binding immunophilins FKBP51 and FKBP52 modulate steroid receptor trafficking and hormone-dependent biological responses. With the purpose to expand this model to other nuclear factors that are also subject to nuclear-cytoplasmic shuttling, we analyzed whether these immunophilins modulate NF-κB signaling. It is demonstrated that FKBP51 impairs both the nuclear translocation rate of NF-κB and its transcriptional activity. The inhibitory action of FKBP51 requires neither the peptidylprolyl-isomerase activity of the immunophilin nor its association with Hsp90. The TPR domain of FKBP51 is essential. On the other hand, FKBP52 favors the nuclear retention time of RelA, its association to a DNA consensus binding sequence, and NF-κB transcriptional activity, the latter effect being strongly dependent on the peptidylprolyl-isomerase activity and also on the TPR domain of FKBP52, but its interaction with Hsp90 is not required. In unstimulated cells, FKBP51 forms endogenous complexes with cytoplasmic RelA. Upon cell stimulation with phorbol ester, the NF-κB soluble complex exchanges FKBP51 for FKBP52, and the NF-κB biological effect is triggered. Importantly, FKBP52 is functionally recruited to the promoter region of NF-κB target genes, whereas FKBP51 is released. Competition assays demonstrated that both immunophilins antagonize one another, and binding assays with purified proteins suggest that the association of RelA and immunophilins could be direct. These observations suggest that the biological action of NF-κB in different cell types could be positively regulated by a high FKBP52/FKBP51 expression ratio by favoring NF-κB nuclear retention, recruitment to the promoter regions of target genes, and transcriptional activity. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.