Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2.

PubMed

Sorokin, Anatoly; Le Novère, Nicolas; Luna, Augustin; Czauderna, Tobias; Demir, Emek; Haw, Robin; Mi, Huaiyu; Moodie, Stuart; Schreiber, Falk; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

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...

Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants

PubMed Central

Regan, Kelly; Wang, Kanix; Doughty, Emily; Li, Haiquan; Li, Jianrong; Lee, Younghee; Kann, Maricel G

2012-01-01

Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome. Materials and methods An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism. Results Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001). Discussion The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP). Conclusion These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics. PMID:22319180

New strategy for protein interactions and application to structure-based drug design

NASA Astrophysics Data System (ADS)

Zou, Xiaoqin

One of the greatest challenges in computational biophysics is to predict interactions between biological molecules, which play critical roles in biological processes and rational design of therapeutic drugs. Biomolecular interactions involve delicate interplay between multiple interactions, including electrostatic interactions, van der Waals interactions, solvent effect, and conformational entropic effect. Accurate determination of these complex and subtle interactions is challenging. Moreover, a biological molecule such as a protein usually consists of thousands of atoms, and thus occupies a huge conformational space. The large degrees of freedom pose further challenges for accurate prediction of biomolecular interactions. Here, I will present our development of physics-based theory and computational modeling on protein interactions with other molecules. The major strategy is to extract microscopic energetics from the information embedded in the experimentally-determined structures of protein complexes. I will also present applications of the methods to structure-based therapeutic design. Supported by NSF CAREER Award DBI-0953839, NIH R01GM109980, and the American Heart Association (Midwest Affiliate) [13GRNT16990076].

Nestedness across biological scales

PubMed Central

Marquitti, Flavia M. D.; Raimundo, Rafael L. G.; Sebastián-González, Esther; Coltri, Patricia P.; Perez, S. Ivan; Brandt, Débora Y. C.; Nunes, Kelly; Daura-Jorge, Fábio G.; Floeter, Sergio R.; Guimarães, Paulo R.

2017-01-01

Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks. PMID:28166284

Protein-protein interaction predictions using text mining methods.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

2015-03-01

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

Endogenous Biologically Inspired Art of Complex Systems.

PubMed

Ji, Haru; Wakefield, Graham

2016-01-01

Since 2007, Graham Wakefield and Haru Ji have looked to nature for inspiration as they have created a series of "artificial natures," or interactive visualizations of biologically inspired complex systems that can evoke nature-like aesthetic experiences within mixed-reality art installations. This article describes how they have applied visualization, sonification, and interaction design in their work with artificial ecosystems and organisms using specific examples from their exhibited installations.

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 BioPlex Network: A Systematic Exploration of the Human Interactome.

PubMed

Huttlin, Edward L; Ting, Lily; Bruckner, Raphael J; Gebreab, Fana; Gygi, Melanie P; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K; Wühr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E; De Camilli, Pietro; Paulo, Joao A; Harper, J Wade; Gygi, Steven P

2015-07-16

Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors. Copyright © 2015 Elsevier Inc. All rights reserved.

The BioPlex Network: A Systematic Exploration of the Human Interactome

PubMed Central

Huttlin, Edward L.; Ting, Lily; Bruckner, Raphael J.; Gebreab, Fana; Gygi, Melanie P.; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K.; Wühr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A.; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E.; DeCamilli, Pietro; Paulo, Joao A.; Harper, J. Wade; Gygi, Steven P.

2015-01-01

SUMMARY Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally-related proteins. Finally, BioPlex, in combination with other approaches can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial Amyotrophic Lateral Sclerosis perturb a defined community of interactors. PMID:26186194

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.

Mode of action from dose-response microarray data: case study using 10 environmental chemicals

EPA Science Inventory

Ligand-activated nuclear receptors regulate many biological processes through complex interactions with biological macromolecules. Certain xenobiotics alter nuclear receptor signaling through direct or indirect interactions. Defining the mode of action of such xenobiotics is di...

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.

Predicting Physical Interactions between Protein Complexes*

PubMed Central

Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

2013-01-01

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

Multi-Dimensional Scaling based grouping of known complexes and intelligent protein complex detection.

PubMed

Rehman, Zia Ur; Idris, Adnan; Khan, Asifullah

2018-06-01

Protein-Protein Interactions (PPI) play a vital role in cellular processes and are formed because of thousands of interactions among proteins. Advancements in proteomics technologies have resulted in huge PPI datasets that need to be systematically analyzed. Protein complexes are the locally dense regions in PPI networks, which extend important role in metabolic pathways and gene regulation. In this work, a novel two-phase protein complex detection and grouping mechanism is proposed. In the first phase, topological and biological features are extracted for each complex, and prediction performance is investigated using Bagging based Ensemble classifier (PCD-BEns). Performance evaluation through cross validation shows improvement in comparison to CDIP, MCode, CFinder and PLSMC methods Second phase employs Multi-Dimensional Scaling (MDS) for the grouping of known complexes by exploring inter complex relations. It is experimentally observed that the combination of topological and biological features in the proposed approach has greatly enhanced prediction performance for protein complex detection, which may help to understand various biological processes, whereas application of MDS based exploration may assist in grouping potentially similar complexes. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

Structure Prediction of Protein Complexes

NASA Astrophysics Data System (ADS)

Pierce, Brian; Weng, Zhiping

Protein-protein interactions are critical for biological function. They directly and indirectly influence the biological systems of which they are a part. Antibodies bind with antigens to detect and stop viruses and other infectious agents. Cell signaling is performed in many cases through the interactions between proteins. Many diseases involve protein-protein interactions on some level, including cancer and prion diseases.

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

AlignNemo: a local network alignment method to integrate homology and topology.

PubMed

Ciriello, Giovanni; Mina, Marco; Guzzi, Pietro H; Cannataro, Mario; Guerra, Concettina

2012-01-01

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways

PubMed Central

Kawakami, Eiryo; Singh, Vivek K; Matsubara, Kazuko; Ishii, Takashi; Matsuoka, Yukiko; Hase, Takeshi; Kulkarni, Priya; Siddiqui, Kenaz; Kodilkar, Janhavi; Danve, Nitisha; Subramanian, Indhupriya; Katoh, Manami; Shimizu-Yoshida, Yuki; Ghosh, Samik; Jere, Abhay; Kitano, Hiroaki

2016-01-01

Cellular stress responses require exquisite coordination between intracellular signaling molecules to integrate multiple stimuli and actuate specific cellular behaviors. Deciphering the web of complex interactions underlying stress responses is a key challenge in understanding robust biological systems and has the potential to lead to the discovery of targeted therapeutics for diseases triggered by dysregulation of stress response pathways. We constructed large-scale molecular interaction maps of six major stress response pathways in Saccharomyces cerevisiae (baker’s or budding yeast). Biological findings from over 900 publications were converted into standardized graphical formats and integrated into a common framework. The maps are posted at http://www.yeast-maps.org/yeast-stress-response/ for browse and curation by the research community. On the basis of these maps, we undertook systematic analyses to unravel the underlying architecture of the networks. A series of network analyses revealed that yeast stress response pathways are organized in bow–tie structures, which have been proposed as universal sub-systems for robust biological regulation. Furthermore, we demonstrated a potential role for complexes in stabilizing the conserved core molecules of bow–tie structures. Specifically, complex-mediated reversible reactions, identified by network motif analyses, appeared to have an important role in buffering the concentration and activity of these core molecules. We propose complex-mediated reactions as a key mechanism mediating robust regulation of the yeast stress response. Thus, our comprehensive molecular interaction maps provide not only an integrated knowledge base, but also a platform for systematic network analyses to elucidate the underlying architecture in complex biological systems. PMID:28725465

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.

Application of linker technique to trap transiently interacting protein complexes for structural studies

PubMed Central

Reddy Chichili, Vishnu Priyanka; Kumar, Veerendra; Sivaraman, J.

2016-01-01

Protein-protein interactions are key events controlling several biological processes. We have developed and employed a method to trap transiently interacting protein complexes for structural studies using glycine-rich linkers to fuse interacting partners, one of which is unstructured. Initial steps involve isothermal titration calorimetry to identify the minimum binding region of the unstructured protein in its interaction with its stable binding partner. This is followed by computational analysis to identify the approximate site of the interaction and to design an appropriate linker length. Subsequently, fused constructs are generated and characterized using size exclusion chromatography and dynamic light scattering experiments. The structure of the chimeric protein is then solved by crystallization, and validated both in vitro and in vivo by substituting key interacting residues of the full length, unlinked proteins with alanine. This protocol offers the opportunity to study crucial and currently unattainable transient protein interactions involved in various biological processes. PMID:26985443

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

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

PubMed

Singh, Nitesh Kumar; Ernst, Mathias; Liebscher, Volkmar; Fuellen, Georg; Taher, Leila

2016-10-20

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

A taxonomy of visualization tasks for the analysis of biological pathway data.

PubMed

Murray, Paul; McGee, Fintan; Forbes, Angus G

2017-02-15

Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity. Here, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy. Our taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts.

Biological potential of oxo-vanadium salicylediene amino-acid complexes as cytotoxic, antimicrobial, antioxidant and DNA interaction.

PubMed

Adam, Mohamed Shaker S; Elsawy, Hany

2018-05-04

New series of oxo-vanadium N-salicyledieneamino acid Schiff base complexes are synthesized and characterized. They are synthesized from the reaction of sodium salicylaldehyde-5-sulfonate, some amino acids, alanine (VOHL1), leucine (VOHL2) or glycine (VOHL3) in an aqueous media, and leucine (VOHLpy1) or tryptophan (VOHLpy2) in pyridine with vanadyl acetylacetonate. The complexes are characterized by EA, TGA, IR, UV-Visible and mass spectra, conductivity and magnetic measurements. The biological activity of the VO-complexes shows that VOHL1, VOHL2 and VOHL3 exhibit anti-proliferative effect and may be used as anticancer drugs. VO-complexes manifest high toxicity, except VOHL2 is less toxic, and could be applied for the human being. VOHL1, VOHL2 and VOHL3 display remarkable SOD like potential and act as high inhibiting reagents. VOHLpy1 and VOHLpy2 show low inhibiting potentials. VO-complexes have good anti-oxidant effect, in which VOHL3 affords the best antioxidant activity. The interaction between VO-complexes and DNA is studied spectrophotometrically and by gel electrophoresis. Binding constants and spectrophotometric parameters indicate a strong interaction between VO-complexes and DNA. VO-complexes have respectable anti-bacterial and antifungal activities, where VOHL3 shows the maximum potential. DFT calculations of VOHL1 and VOHL3 were discussed in the light of their biological activity, which are convenient with the obtained results. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

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

Multidimensional approaches for studying plant defence against insects: from ecology to omics and synthetic biology.

PubMed

Barah, Pankaj; Bones, Atle M

2015-02-01

The biggest challenge for modern biology is to integrate multidisciplinary approaches towards understanding the organizational and functional complexity of biological systems at different hierarchies, starting from the subcellular molecular mechanisms (microscopic) to the functional interactions of ecological communities (macroscopic). The plant-insect interaction is a good model for this purpose with the availability of an enormous amount of information at the molecular and the ecosystem levels. Changing global climatic conditions are abruptly resetting plant-insect interactions. Integration of discretely located heterogeneous information from the ecosystem to genes and pathways will be an advantage to understand the complexity of plant-insect interactions. This review will present the recent developments in omics-based high-throughput experimental approaches, with particular emphasis on studying plant defence responses against insect attack. The review highlights the importance of using integrative systems approaches to study plant-insect interactions from the macroscopic to the microscopic level. We analyse the current efforts in generating, integrating and modelling multiomics data to understand plant-insect interaction at a systems level. As a future prospect, we highlight the growing interest in utilizing the synthetic biology platform for engineering insect-resistant plants. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Exploring biological interaction networks with tailored weighted quasi-bicliques

PubMed Central

2012-01-01

Background Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges. Results We introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. Conclusions We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks. PMID:22759421

Protein-protein interaction networks (PPI) and complex diseases

PubMed Central

Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

2014-01-01

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Physical and biological mechanisms of nanosecond- and microsecond-pulsed FE-DBD plasma interaction with biological objects

NASA Astrophysics Data System (ADS)

Dobrynin, Danil

2013-09-01

Mechanisms of plasma interaction with living tissues and cells can be quite complex, owing to the complexity of both the plasma and the tissue. Thus, unification of all the mechanisms under one umbrella might not be possible. Here, analysis of interaction of floating electrode dielectric barrier discharge (FE-DBD) with living tissues and cells is presented and biological and physical mechanisms are discussed. In physical mechanisms, charged species are identified as the major contributors to the desired effect and a mechanism of this interaction is proposed. Biological mechanisms are also addressed and a hypothesis of plasma selectivity and its effects is offered. Spatially uniform nanosecond and sub-nanosecond short-pulsed dielectric barrier discharge plasmas are gaining popularity in biological and medical applications due to their increased uniformity, lower plasma temperature, lower surface power density, and higher concentration of the active species produced. In this presentation we will compare microsecond pulsed plasmas with nanosecond driven systems and their applications in biology and medicine with specific focus on wound healing and tissue regeneration. Transition from negative to positive streamer will be discussed with proposed hypothesis of uniformity mechanisms of positive streamer and the reduced dependence on morphology and surface chemistry of the second electrode (human body) being treated. Uniform plasma offers a more uniform delivery of active species to the tissue/surface being treated thus leading to better control over the biological results.

System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks

PubMed Central

Miao, Yingbo; Zhang, Liangcai; Wang, Yang; Feng, Rennan; Yang, Lei; Zhang, Shihua; Jiang, Yongshuai; Liu, Guiyou

2014-01-01

Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM. PMID:25243143

Thermodynamic characterization of the multivalent interactions underlying rapid and selective translocation through the nuclear pore complex

PubMed Central

Hayama, Ryo; Sparks, Samuel; Hecht, Lee M.; Dutta, Kaushik; Karp, Jerome M.; Cabana, Christina M.; Rout, Michael P.; Cowburn, David

2018-01-01

Intrinsically disordered proteins (IDPs) play important roles in many biological systems. Given the vast conformational space that IDPs can explore, the thermodynamics of the interactions with their partners is closely linked to their biological functions. Intrinsically disordered regions of Phe–Gly nucleoporins (FG Nups) that contain multiple phenylalanine–glycine repeats are of particular interest, as their interactions with transport factors (TFs) underlie the paradoxically rapid yet also highly selective transport of macromolecules mediated by the nuclear pore complex. Here, we used NMR and isothermal titration calorimetry to thermodynamically characterize these multivalent interactions. These analyses revealed that a combination of low per-FG motif affinity and the enthalpy–entropy balance prevents high-avidity interaction between FG Nups and TFs, whereas the large number of FG motifs promotes frequent FG–TF contacts, resulting in enhanced selectivity. Our thermodynamic model underlines the importance of functional disorder of FG Nups. It helps explain the rapid and selective translocation of TFs through the nuclear pore complex and further expands our understanding of the mechanisms of “fuzzy” interactions involving IDPs. PMID:29374059

Decoding the Heart through Next Generation Sequencing Approaches.

PubMed

Pawlak, Michal; Niescierowicz, Katarzyna; Winata, Cecilia Lanny

2018-06-07

: Vertebrate organs develop through a complex process which involves interaction between multiple signaling pathways at the molecular, cell, and tissue levels. Heart development is an example of such complex process which, when disrupted, results in congenital heart disease (CHD). This complexity necessitates a holistic approach which allows the visualization of genome-wide interaction networks, as opposed to assessment of limited subsets of factors. Genomics offers a powerful solution to address the problem of biological complexity by enabling the observation of molecular processes at a genome-wide scale. The emergence of next generation sequencing (NGS) technology has facilitated the expansion of genomics, increasing its output capacity and applicability in various biological disciplines. The application of NGS in various aspects of heart biology has resulted in new discoveries, generating novel insights into this field of study. Here we review the contributions of NGS technology into the understanding of heart development and its disruption reflected in CHD and discuss how emerging NGS based methodologies can contribute to the further understanding of heart repair.

Complex formation of blueberry (Vaccinium angustifolium) anthocyanins during freeze-drying and its influence on their biological activity.

PubMed

Correa-Betanzo, Julieta; Padmanabhan, Priya; Corredig, Milena; Subramanian, Jayasankar; Paliyath, Gopinadhan

2015-03-25

Biological activity of polyphenols is influenced by their uptake and is highly influenced by their interactions with the food matrix. This study evaluated the complex formation of blueberry polyphenols with fruit matrixes such as pectin and cellulose and their effect on the biological and antiproliferative properties of human colon cell lines HT-29 and CRL 1790. Free or complexed polyphenols were isolated by dialyzing aqueous or methanolic blueberry homogenates. Seven phenolic compounds and thirteen anthocyanins were identified in blueberry extracts. Blueberry extracts showed varying degrees of antioxidant and antiproliferative activities, as well as α-glucosidase activity. Fruit matrix containing cellulose and pectin, or purified polygalacturonic acid and cellulose, did not retain polyphenols and showed very low antioxidant or antiproliferative activities. These findings suggest that interactions between polyphenols and the food matrix may be more complex than a simple association and may play an important role in the bioefficacy of blueberry polyphenols.

Large-scale study of the interactions between proteins involved in type IV pilus biology in Neisseria meningitidis: characterization of a subcomplex involved in pilus assembly.

PubMed

Georgiadou, Michaella; Castagnini, Marta; Karimova, Gouzel; Ladant, Daniel; Pelicic, Vladimir

2012-06-01

The functionally versatile type IV pili (Tfp) are one of the most widespread virulence factors in bacteria. However, despite generating much research interest for decades, the molecular mechanisms underpinning the various aspects of Tfp biology remain poorly understood, mainly because of the complexity of the system. In the human pathogen Neisseria meningitidis for example, 23 proteins are dedicated to Tfp biology, 15 of which are essential for pilus biogenesis. One of the important gaps in our knowledge concerns the topology of this multiprotein machinery. Here we have used a bacterial two-hybrid system to identify and quantify the interactions between 11 Pil proteins from N. meningitidis. We identified 20 different binary interactions, many of which are novel. This represents the most complex interaction network between Pil proteins reported to date and indicates, among other things, that PilE, PilM, PilN and PilO, which are involved in pilus assembly, indeed interact. We focused our efforts on this subset of proteins and used a battery of assays to determine the membrane topology of PilN and PilO, map the interaction domains between PilE, PilM, PilN and PilO, and show that a widely conserved N-terminal motif in PilN is essential for both PilM-PilN interactions and pilus assembly. Finally, we show that PilP (another protein involved in pilus assembly) forms a complex with PilM, PilN and PilO. Taken together, these findings have numerous implications for understanding Tfp biology and provide a useful blueprint for future studies. © 2012 Blackwell Publishing Ltd.

Computational Methods to Predict Protein Interaction Partners

NASA Astrophysics Data System (ADS)

Valencia, Alfonso; Pazos, Florencio

In the new paradigm for studying biological phenomena represented by Systems Biology, cellular components are not considered in isolation but as forming complex networks of relationships. Protein interaction networks are among the first objects studied from this new point of view. Deciphering the interactome (the whole network of interactions for a given proteome) has been shown to be a very complex task. Computational techniques for detecting protein interactions have become standard tools for dealing with this problem, helping and complementing their experimental counterparts. Most of these techniques use genomic or sequence features intuitively related with protein interactions and are based on "first principles" in the sense that they do not involve training with examples. There are also other computational techniques that use other sources of information (i.e. structural information or even experimental data) or are based on training with examples.

A System Biology Perspective on Environment-Host-Microbe Interactions.

PubMed

Chen, Lianmin; Garmaeva, Sanzhima; Zherankova, Alexandra; Fu, Jingyuan; Wijmenga, Cisca

2018-04-16

A vast, complex and dynamic consortium of microorganisms known as the gut microbiome colonizes the human gut. Over the past few decades we have developed an increased awareness of its important role in human health. In this review we discuss the role of the gut microbiome in complex diseases and the possible causal scenarios behind its interactions with the host genome and environmental factors. We then propose a new analysis framework that combines a systems biology approach, cross-kingdom integration of multiple levels of omics data, and innovative in vitro models to yield an integrated picture of human host-microbe interactions. This new framework will lay the foundation for the development of the next phase in personalized medicine.

RNAseq and Proteomics for Analysing Complex Oomycete Plant Interactions.

PubMed

Burra, Dharani D; Vetukuri, Ramesh R; Resjö, Svante; Grenville-Briggs, Laura J; Andreasson, Erik

2016-01-01

The oomycetes include some of the most devastating plant pathogens. In this review we discuss the latest results from oomycete and plant studies with emphasis on interaction studies. We focus on the outcomes of RNAseq and proteomics studies and some pitfalls of these approaches. Both pathogenic interactions and biological control are discussed. We underline the usefulness of studies at several levels of complexity from studies of one organism, up to two or more and within agricultural fields (managed settings) up to wild ecosystems. Finally we identify areas of future interest such as detailed interactome studies, dual RNAseq studies, peptide modification studies and population/meta omics with or without biological control agents.

Spectroscopic exploration of interaction between PEG-functionalized Ag2S nanoparticles with bovine serum albumin

NASA Astrophysics Data System (ADS)

Prasanth, S.; RitheshRaj, D.; Vineeshkumar, T. V.; Sudarsanakumar, C.

2018-05-01

The introduction of nanoparticles into biological fluids often leads to the formation of biocorona over the surface of nanoparticles. For the effective use of nanoparticles in biological applications it is very essential to understand their interactions with proteins. Herein, we investigated the interactions of Poly ethylene glycol capped Ag2S nanoparticles with Bovine Serum Albumin by spectroscopic techniques. By the addition of Ag2S nanoparticles, a ground state complex is formed. The CD spectroscopy reveals that the secondary structure of BSA is altered by complexation with PEG-Ag2S nanoparticles, while the overall tertiary structure remains closer to that of native BSA.

Nanomaterial interactions with biomembranes: Bridging the gap between soft matter models and biological context.

PubMed

Werner, Marco; Auth, Thorsten; Beales, Paul A; Fleury, Jean Baptiste; Höök, Fredrik; Kress, Holger; Van Lehn, Reid C; Müller, Marcus; Petrov, Eugene P; Sarkisov, Lev; Sommer, Jens-Uwe; Baulin, Vladimir A

2018-04-03

Synthetic polymers, nanoparticles, and carbon-based materials have great potential in applications including drug delivery, gene transfection, in vitro and in vivo imaging, and the alteration of biological function. Nature and humans use different design strategies to create nanomaterials: biological objects have emerged from billions of years of evolution and from adaptation to their environment resulting in high levels of structural complexity; in contrast, synthetic nanomaterials result from minimalistic but controlled design options limited by the authors' current understanding of the biological world. This conceptual mismatch makes it challenging to create synthetic nanomaterials that possess desired functions in biological media. In many biologically relevant applications, nanomaterials must enter the cell interior to perform their functions. An essential transport barrier is the cell-protecting plasma membrane and hence the understanding of its interaction with nanomaterials is a fundamental task in biotechnology. The authors present open questions in the field of nanomaterial interactions with biological membranes, including: how physical mechanisms and molecular forces acting at the nanoscale restrict or inspire design options; which levels of complexity to include next in computational and experimental models to describe how nanomaterials cross barriers via passive or active processes; and how the biological media and protein corona interfere with nanomaterial functionality. In this Perspective, the authors address these questions with the aim of offering guidelines for the development of next-generation nanomaterials that function in biological media.

Synchronization in complex networks

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

Arenas, A.; Diaz-Guilera, A.; Moreno, Y.

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analyticalmore » approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.« less

A new multi-scale method to reveal hierarchical modular structures in biological networks.

PubMed

Jiao, Qing-Ju; Huang, Yan; Shen, Hong-Bin

2016-11-15

Biological networks are effective tools for studying molecular interactions. Modular structure, in which genes or proteins may tend to be associated with functional modules or protein complexes, is a remarkable feature of biological networks. Mining modular structure from biological networks enables us to focus on a set of potentially important nodes, which provides a reliable guide to future biological experiments. The first fundamental challenge in mining modular structure from biological networks is that the quality of the observed network data is usually low owing to noise and incompleteness in the obtained networks. The second problem that poses a challenge to existing approaches to the mining of modular structure is that the organization of both functional modules and protein complexes in networks is far more complicated than was ever thought. For instance, the sizes of different modules vary considerably from each other and they often form multi-scale hierarchical structures. To solve these problems, we propose a new multi-scale protocol for mining modular structure (named ISIMB) driven by a node similarity metric, which works in an iteratively converged space to reduce the effects of the low data quality of the observed network data. The multi-scale node similarity metric couples both the local and the global topology of the network with a resolution regulator. By varying this resolution regulator to give different weightings to the local and global terms in the metric, the ISIMB method is able to fit the shape of modules and to detect them on different scales. Experiments on protein-protein interaction and genetic interaction networks show that our method can not only mine functional modules and protein complexes successfully, but can also predict functional modules from specific to general and reveal the hierarchical organization of protein complexes.

Protein-Protein Interactions of Azurin Complex by Coarse-Grained Simulations with a Gō-Like Model

NASA Astrophysics Data System (ADS)

Rusmerryani, Micke; Takasu, Masako; Kawaguchi, Kazutomo; Saito, Hiroaki; Nagao, Hidemi

Proteins usually perform their biological functions by forming a complex with other proteins. It is very important to study the protein-protein interactions since these interactions are crucial in many processes of a living organism. In this study, we develop a coarse grained model to simulate protein complex in liquid system. We carry out molecular dynamics simulations with topology-based potential interactions to simulate dynamical properties of Pseudomonas Aeruginosa azurin complex systems. Azurin is known to play an essential role as an anticancer agent and bind many important intracellular molecules. Some physical properties are monitored during simulation time to get a better understanding of the influence of protein-protein interactions to the azurin complex dynamics. These studies will provide valuable insights for further investigation on protein-protein interactions in more realistic system.

Proteomic analysis in non-denaturing condition of the secretome reveals the presence of multienzyme complexes in Penicillium purpurogenum.

PubMed

Gonzalez-Vogel, Alvaro; Eyzaguirre, Jaime; Oleas, Gabriela; Callegari, Eduardo; Navarrete, Mario

2011-01-01

Proteins secreted by filamentous fungi play key roles in different aspects of their biology. The fungus Penicillium purpurogenum, used as a model organism, is able to degrade hemicelluloses and pectins by secreting a variety of enzymes to the culture medium. This work shows that these enzymes interact with each other to form high molecular weight, catalytically active complexes. By using a proteomics approach, we were able to identify several protein complexes in the secretome of this fungus. The expression and assembly of these complexes depend on the carbon source used and display molecular masses ranging from 300 to 700 kDa. These complexes are composed of a variety of enzymes, including arabinofuranosidases, acetyl xylan esterases, feruloyl esterases, β-glucosidases and xylanases. The protein-protein interactions in these multienzyme complexes were confirmed by coimmunoprecipitation assays. One of the complexes was purified from sugar beet pulp cultures and the subunits identified by tandem mass spectrometry. A better understanding of the biological significance of these kinds of interactions will help in the comprehension of the degradation mechanisms used by fungi and may be of special interest to the biotechnology industry.

PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions.

PubMed

Dong, Jie; Yao, Zhi-Jiang; Zhang, Lin; Luo, Feijun; Lin, Qinlu; Lu, Ai-Ping; Chen, Alex F; Cao, Dong-Sheng

2018-03-20

With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline. Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently. PyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html .

The adsorption interaction of a rutin-biopolymer complex with nanosized silica particles

NASA Astrophysics Data System (ADS)

Fedyanina, T. V.; Barvinchenko, V. N.; Lipkovskaya, N. A.; Pogorelyi, V. K.

2008-10-01

The influence of complex formation with biopolymers on the optical and acid properties of natural flavonoid rutin was studied. The adsorption interaction of biologically active flavonoids from officinal plants with the surface of nanosized silica particles was found to depend on the chemical nature of the biopolymer and adsorbate and solution properties.

The Promise of Systems Biology Approaches for Revealing Host Pathogen Interactions in Malaria

PubMed Central

Zuck, Meghan; Austin, Laura S.; Danziger, Samuel A.; Aitchison, John D.; Kaushansky, Alexis

2017-01-01

Despite global eradication efforts over the past century, malaria remains a devastating public health burden, causing almost half a million deaths annually (WHO, 2016). A detailed understanding of the mechanisms that control malaria infection has been hindered by technical challenges of studying a complex parasite life cycle in multiple hosts. While many interventions targeting the parasite have been implemented, the complex biology of Plasmodium poses a major challenge, and must be addressed to enable eradication. New approaches for elucidating key host-parasite interactions, and predicting how the parasite will respond in a variety of biological settings, could dramatically enhance the efficacy and longevity of intervention strategies. The field of systems biology has developed methodologies and principles that are well poised to meet these challenges. In this review, we focus our attention on the Liver Stage of the Plasmodium lifecycle and issue a “call to arms” for using systems biology approaches to forge a new era in malaria research. These approaches will reveal insights into the complex interplay between host and pathogen, and could ultimately lead to novel intervention strategies that contribute to malaria eradication. PMID:29201016

Comprehensive Characterization of Minichromosome Maintenance Complex (MCM) Protein Interactions Using Affinity and Proximity Purifications Coupled to Mass Spectrometry.

PubMed

Dubois, Marie-Line; Bastin, Charlotte; Lévesque, Dominique; Boisvert, François-Michel

2016-09-02

The extensive identification of protein-protein interactions under different conditions is an important challenge to understand the cellular functions of proteins. Here we use and compare different approaches including affinity purification and purification by proximity coupled to mass spectrometry to identify protein complexes. We explore the complete interactome of the minichromosome maintenance (MCM) complex by using both approaches for all of the different MCM proteins. Overall, our analysis identified unique and shared interaction partners and proteins enriched for distinct biological processes including DNA replication, DNA repair, and cell cycle regulation. Furthermore, we mapped the changes in protein interactions of the MCM complex in response to DNA damage, identifying a new role for this complex in DNA repair. In summary, we demonstrate the complementarity of these approaches for the characterization of protein interactions within the MCM complex.

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

PubMed Central

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

2013-01-01

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

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME.

PubMed

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2016-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

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.

A method to identify and analyze biological programs through automated reasoning

PubMed Central

Yordanov, Boyan; Dunn, Sara-Jane; Kugler, Hillel; Smith, Austin; Martello, Graziano; Emmott, Stephen

2016-01-01

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich, but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function. PMID:27668090

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

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.

Identifying protein complexes in PPI network using non-cooperative sequential game.

PubMed

Maulik, Ujjwal; Basu, Srinka; Ray, Sumanta

2017-08-21

Identifying protein complexes from protein-protein interaction (PPI) network is an important and challenging task in computational biology as it helps in better understanding of cellular mechanisms in various organisms. In this paper we propose a noncooperative sequential game based model for protein complex detection from PPI network. The key hypothesis is that protein complex formation is driven by mechanism that eventually optimizes the number of interactions within the complex leading to dense subgraph. The hypothesis is drawn from the observed network property named small world. The proposed multi-player game model translates the hypothesis into the game strategies. The Nash equilibrium of the game corresponds to a network partition where each protein either belong to a complex or form a singleton cluster. We further propose an algorithm to find the Nash equilibrium of the sequential game. The exhaustive experiment on synthetic benchmark and real life yeast networks evaluates the structural as well as biological significance of the network partitions.

c-Mantic: A Cytoscape plugin for Semantic Web

EPA Science Inventory

Semantic Web tools can streamline the process of storing, analyzing and sharing biological information. Visualization is important for communicating such complex biological relationships. Here we use the flexibility and speed of the Cytoscape platform to interactively visualize s...

The emerging genomics and systems biology research lead to systems genomics studies.

PubMed

Yang, Mary Qu; Yoshigoe, Kenji; Yang, William; Tong, Weida; Qin, Xiang; Dunker, A; Chen, Zhongxue; Arbania, Hamid R; Liu, Jun S; Niemierko, Andrzej; Yang, Jack Y

2014-01-01

Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology. The Mid-South Bioinformatics Centre (MBC) and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and University of Arkansas for Medical Sciences are particularly interested in promoting education and research advancement in this prospectively emerging field. Based on past investigations and research outcomes, MBC is further utilizing differential gene and isoform/exon expression from RNA-seq and co-regulation from the ChiP-seq specific for different phenotypes in combination with protein-protein interactions, and protein-DNA interactions to construct high-level gene networks for an integrative genome-phoneme investigation at systems biology level.

Carbohydrates in Supramolecular Chemistry.

PubMed

Delbianco, Martina; Bharate, Priya; Varela-Aramburu, Silvia; Seeberger, Peter H

2016-02-24

Carbohydrates are involved in a variety of biological processes. The ability of sugars to form a large number of hydrogen bonds has made them important components for supramolecular chemistry. We discuss recent advances in the use of carbohydrates in supramolecular chemistry and reveal that carbohydrates are useful building blocks for the stabilization of complex architectures. Systems are presented according to the scaffold that supports the glyco-conjugate: organic macrocycles, dendrimers, nanomaterials, and polymers are considered. Glyco-conjugates can form host-guest complexes, and can self-assemble by using carbohydrate-carbohydrate interactions and other weak interactions such as π-π interactions. Finally, complex supramolecular architectures based on carbohydrate-protein interactions are discussed.

Identification of continuous interaction sites in PLA(2)-based protein complexes by peptide arrays.

PubMed

Fortes-Dias, Consuelo Latorre; Santos, Roberta Márcia Marques dos; Magro, Angelo José; Fontes, Marcos Roberto de Mattos; Chávez-Olórtegui, Carlos; Granier, Claude

2009-01-01

Crotoxin (CA.CB) is a beta-neurotoxin from Crotalus durissus terrificus snake venom that is responsible for main envenomation effects upon biting by this snake. It is a heterodimer of an acidic protein (CA) devoid of any biological activity per se and a basic, enzymatically active, PLA(2) counterpart (CB). Both lethal and enzymatic activities of crotoxin have been shown to be inhibited by CNF, a protein from the blood of C. d. terrificus snakes. CNF replaces CA in the CA.CB complex, forming a stable, non-toxic complex CNF.CB. The molecular sites involved in the tight interfacial protein-protein interactions in these PLA(2)-based complexes have not been clearly determined. To help address this question, we used the peptide arrays approach to map possible interfacial interaction sites in CA.CB and CNF.CB. Amino acid stretches putatively involved in these interactions were firstly identified in the primary structure of CB. Further analysis of the interfacial availability of these stretches in the presumed biologically active structure of CB, suggested two interaction main sites, located at the amino-terminus and beta-wing regions. Peptide segments at the carboxyl-terminus of CB were also suggested to play a secondary role in the binding of both CA and CNF.

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Cell Biology and Microbiology: A Continuous Cross-Feeding.

PubMed

Pizarro-Cerdá, Javier; Cossart, Pascale

2016-07-01

Microbiology and cell biology both involve the study of cells, albeit at different levels of complexity and scale. Interactions between both fields during the past 25 years have led to major conceptual and technological advances that have reshaped the whole biology landscape and its biomedical applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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.

Accurate characterization of weak macromolecular interactions by titration of NMR residual dipolar couplings: application to the CD2AP SH3-C:ubiquitin complex.

PubMed

Ortega-Roldan, Jose Luis; Jensen, Malene Ringkjøbing; Brutscher, Bernhard; Azuaga, Ana I; Blackledge, Martin; van Nuland, Nico A J

2009-05-01

The description of the interactome represents one of key challenges remaining for structural biology. Physiologically important weak interactions, with dissociation constants above 100 muM, are remarkably common, but remain beyond the reach of most of structural biology. NMR spectroscopy, and in particular, residual dipolar couplings (RDCs) provide crucial conformational constraints on intermolecular orientation in molecular complexes, but the combination of free and bound contributions to the measured RDC seriously complicates their exploitation for weakly interacting partners. We develop a robust approach for the determination of weak complexes based on: (i) differential isotopic labeling of the partner proteins facilitating RDC measurement in both partners; (ii) measurement of RDC changes upon titration into different equilibrium mixtures of partially aligned free and complex forms of the proteins; (iii) novel analytical approaches to determine the effective alignment in all equilibrium mixtures; and (iv) extraction of precise RDCs for bound forms of both partner proteins. The approach is demonstrated for the determination of the three-dimensional structure of the weakly interacting CD2AP SH3-C:Ubiquitin complex (K(d) = 132 +/- 13 muM) and is shown, using cross-validation, to be highly precise. We expect this methodology to extend the remarkable and unique ability of NMR to study weak protein-protein complexes.

Accurate characterization of weak macromolecular interactions by titration of NMR residual dipolar couplings: application to the CD2AP SH3-C:ubiquitin complex

PubMed Central

Ortega-Roldan, Jose Luis; Jensen, Malene Ringkjøbing; Brutscher, Bernhard; Azuaga, Ana I.; Blackledge, Martin; van Nuland, Nico A. J.

2009-01-01

The description of the interactome represents one of key challenges remaining for structural biology. Physiologically important weak interactions, with dissociation constants above 100 μM, are remarkably common, but remain beyond the reach of most of structural biology. NMR spectroscopy, and in particular, residual dipolar couplings (RDCs) provide crucial conformational constraints on intermolecular orientation in molecular complexes, but the combination of free and bound contributions to the measured RDC seriously complicates their exploitation for weakly interacting partners. We develop a robust approach for the determination of weak complexes based on: (i) differential isotopic labeling of the partner proteins facilitating RDC measurement in both partners; (ii) measurement of RDC changes upon titration into different equilibrium mixtures of partially aligned free and complex forms of the proteins; (iii) novel analytical approaches to determine the effective alignment in all equilibrium mixtures; and (iv) extraction of precise RDCs for bound forms of both partner proteins. The approach is demonstrated for the determination of the three-dimensional structure of the weakly interacting CD2AP SH3-C:Ubiquitin complex (Kd = 132 ± 13 μM) and is shown, using cross-validation, to be highly precise. We expect this methodology to extend the remarkable and unique ability of NMR to study weak protein–protein complexes. PMID:19359362

Application of Biologically-Based Lumping To Investigate the Toxicological Interactions of a Complex Gasoline Mixture

EPA Science Inventory

People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. However, investigators have often considered complex mixtures as one lumped entity. Valuable information can be obtained from these exp...

APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.

PubMed

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.

APG: an Active Protein-Gene Network Model to Quantify Regulatory Signals in Complex Biological Systems

PubMed Central

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information. PMID:23346354

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.

Intermolecular hydrogen bonds in hetero-complexes of biologically active aromatic molecules probed by the methods of vibrational spectroscopy

NASA Astrophysics Data System (ADS)

Semenov, M. A.; Blyzniuk, Iu. N.; Bolbukh, T. V.; Shestopalova, A. V.; Evstigneev, M. P.; Maleev, V. Ya.

2012-09-01

By the methods of vibrational spectroscopy (Infrared and Raman) the investigation of the hetero-association of biologically active aromatic compounds: flavin-mononucleotide (FMN), ethidium bromide (EB) and proflavine (PRF) was performed in aqueous solutions. It was shown that between the functional groups (Cdbnd O and NH2) the intermolecular hydrogen bonds are formed in the hetero-complexes FMN-EB and FMN-PRF, additionally stabilizing these structures. An estimation of the enthalpy of Н-bonding obtained from experimental shifts of carbonyl vibrational frequencies has shown that the H-bonds do not dominate in the magnitude of experimentally measured total enthalpy of the hetero-association reactions. The main stabilization is likely due to intermolecular interactions of the molecules in these complexes and their interaction with water environment.

Mapping Proteome-Wide Interactions of Reactive Chemicals Using Chemoproteomic Platforms

PubMed Central

Counihan, Jessica L.; Ford, Breanna; Nomura, Daniel K.

2015-01-01

A large number of pharmaceuticals, endogenous metabolites, and environmental chemicals act through covalent mechanisms with protein targets. Yet, their specific interactions with the proteome still remain poorly defined for most of these reactive chemicals. Deciphering direct protein targets of reactive small-molecules is critical in understanding their biological action, off-target effects, potential toxicological liabilities, and development of safer and more selective agents. Chemoproteomic technologies have arisen as a powerful strategy that enable the assessment of proteome-wide interactions of these irreversible agents directly in complex biological systems. We review here several chemoproteomic strategies that have facilitated our understanding of specific protein interactions of irreversibly-acting pharmaceuticals, endogenous metabolites, and environmental electrophiles to reveal novel pharmacological, biological, and toxicological mechanisms. PMID:26647369

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

Experimental and Theoretical Approaches for the Surface Interaction between Copper and Activated Sludge Microorganisms at Molecular Scale

NASA Astrophysics Data System (ADS)

Luo, Hong-Wei; Chen, Jie-Jie; Sheng, Guo-Ping; Su, Ji-Hu; Wei, Shi-Qiang; Yu, Han-Qing

2014-11-01

Interactions between metals and activated sludge microorganisms substantially affect the speciation, immobilization, transport, and bioavailability of trace heavy metals in biological wastewater treatment plants. In this study, the interaction of Cu(II), a typical heavy metal, onto activated sludge microorganisms was studied in-depth using a multi-technique approach. The complexing structure of Cu(II) on microbial surface was revealed by X-ray absorption fine structure (XAFS) and electron paramagnetic resonance (EPR) analysis. EPR spectra indicated that Cu(II) was held in inner-sphere surface complexes of octahedral coordination with tetragonal distortion of axial elongation. XAFS analysis further suggested that the surface complexation between Cu(II) and microbial cells was the distorted inner-sphere coordinated octahedra containing four short equatorial bonds and two elongated axial bonds. To further validate the results obtained from the XAFS and EPR analysis, density functional theory calculations were carried out to explore the structural geometry of the Cu complexes. These results are useful to better understand the speciation, immobilization, transport, and bioavailability of metals in biological wastewater treatment plants.

Inhibition of cathelicidin activity by bacterial exopolysaccharides.

PubMed

Foschiatti, Michela; Cescutti, Paola; Tossi, Alessandro; Rizzo, Roberto

2009-06-01

The interaction of bacterial exopolysaccharides, produced by opportunistic lung pathogens, with antimicrobial peptides of the innate primate immune system was investigated. The exopolysaccharides were produced by Pseudomonas aeruginosa, Inquilinus limosus and clinical isolates of the Burkholderia cepacia complex, bacteria that are all involved in lung infections of cystic fibrosis patients. The effects of the biological activities of three orthologous cathelicidins from Homo sapiens sapiens, Pongo pygmaeus (orangutan) and Presbitys obscurus (dusky leaf monkey) were examined. Inhibition of the antimicrobial activity of peptides was assessed using minimum inhibitory concentration assays on a reference Escherichia coli strain in the presence and absence of exopolysaccharides, whereas complex formation between peptides and exopolysaccharides was investigated by means of circular dichroism, fluorescence spectroscopy and atomic force microscopy. Biological assays revealed that the higher the negative charge of exopolysaccharides the stronger was their inhibiting effect. Spectroscopic studies indicated the formation of molecular complexes of varying stability between peptides and exopolysaccharides, explaining the inhibition. Atomic force microscopy provided a direct visualization of the molecular complexes. A model is proposed where peptides with an alpha-helical conformation interact with exopolysaccharides through electrostatic and other non-covalent interactions.

Ionic interactions in biological and physical systems: a variational treatment.

PubMed

Eisenberg, Bob

2013-01-01

Chemistry is about chemical reactions. Chemistry is about electrons changing their configurations as atoms and molecules react. Chemistry has for more than a century studied reactions as if they occurred in ideal conditions of infinitely dilute solutions. But most reactions occur in salt solutions that are not ideal. In those solutions everything (charged) interacts with everything else (charged) through the electric field, which is short and long range extending to the boundaries of the system. Mathematics has recently been developed to deal with interacting systems of this sort. The variational theory of complex fluids has spawned the theory of liquid crystals (or vice versa). In my view, ionic solutions should be viewed as complex fluids, particularly in the biological and engineering context. In both biology and electrochemistry ionic solutions are mixtures highly concentrated (to approximately 10 M) where they are most important, near electrodes, nucleic ids, proteins, active sites of enzymes, and ionic channels. Ca2+ is always involved in biological solutions because the concentration (really free energy per mole) of Ca2+ in a particular location is the signal that controls many biological functions. Such interacting systems are not simple fluids, and it is no wonder that analysis of interactions, such as the Hofmeister series, rooted in that tradition has not succeeded as one would hope. Here, we present a variational treatment of ard spheres in a frictional dielectric with the hope that such a treatment of an lectrolyte as a complex fluid will be productive. The theory automatically extends to spatially nonuniform boundary conditions and the nonequilibrium systems and flows they produce. The theory is unavoidably self-consistent since differential equations are derived (not assumed) from models of (Helmholtz free) nergy and dissipation of the electrolyte. The origin of the Hofmeister series is (in my view) an inverse problem that becomes well posed when enough data from disjoint experimental traditions are interpreted with a self-consistent theory.

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.

Design of copper DNA intercalators with leishmanicidal activity.

PubMed

Navarro, Maribel; Cisneros-Fajardo, Efrén José; Sierralta, Aníbal; Fernández-Mestre, Mercedes; Silva, Pedro; Arrieche, Dwight; Marchán, Edgar

2003-04-01

The complexes [Cu(dppz)(NO(3))]NO(3) (1), [Cu(dppz)(2)(NO(3))]NO(3) (2), [Cu(dpq)(NO(3))]NO(3) (3), and [Cu(dpq)(2)(NO(3))]NO(3) (4) were synthesized and characterized by elemental analysis, FAB-mass spectrometry, EPR, UV, and IR spectroscopies, and molar conductivity. DNA interaction studies showed that intercalation is an important way of interacting with DNA for these complexes. The biological activity of these copper complexes was evaluated on Leishmania braziliensis promastigotes, and the results showed leishmanicidal activity. Preliminary ultrastructural studies with the most active complex (2) at 1 h revealed parasite swelling and binucleated cells. This finding suggests that the leishmanicidal activity of the copper complexes could be associated with their interaction with the parasitic DNA.

Review Article: Shallow Draughts--Larsen-Freeman and Cameron on Complexity

ERIC Educational Resources Information Center

Gregg, Kevin R.

2010-01-01

Complexity theory is a field of physics that studies the nature and behavior of complex systems, systems whose elements interact in complex and unpredictable ways. Recent years have seen a number of attempts to extend its scope to the biological and social sciences, and now Larsen-Freeman and Cameron offer a view of applied linguistics from a…

Joining Forces: Integrating Proteomics and Cross-linking with the Mass Spectrometry of Intact Complexes*

PubMed Central

Stengel, Florian; Aebersold, Ruedi; Robinson, Carol V.

2012-01-01

Protein assemblies are critical for cellular function and understanding their physical organization is the key aim of structural biology. However, applying conventional structural biology approaches is challenging for transient, dynamic, or polydisperse assemblies. There is therefore a growing demand for hybrid technologies that are able to complement classical structural biology methods and thereby broaden our arsenal for the study of these important complexes. Exciting new developments in the field of mass spectrometry and proteomics have added a new dimension to the study of protein-protein interactions and protein complex architecture. In this review, we focus on how complementary mass spectrometry-based techniques can greatly facilitate structural understanding of protein assemblies. PMID:22180098

A novel approach to simulate gene-environment interactions in complex diseases.

PubMed

Amato, Roberto; Pinelli, Michele; D'Andrea, Daniel; Miele, Gennaro; Nicodemi, Mario; Raiconi, Giancarlo; Cocozza, Sergio

2010-01-05

Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.

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.

Recent opportunities for an increasing role for physical explanations in biology.

PubMed

Morange, Michel

2011-06-01

Relations between physics and biology have been always difficult. One reason is that physical approaches to the phenomena of life have frequently been conceived by their authors as alternatives to biological explanations. My argument is that molecular descriptions and explanations have been pushed so far that they have reached their limits: these limits constitute a favourable niche in which physical explanations can develop. I will focus on the field of molecular and cell biology and give many examples of these recent physical studies made possible by the precision of molecular observations. The nature of these niches is probably diverse. I consider that it is too early to have a global view of the interactions between biological and physical explanations, and to organize them into different categories. Such interactions are not new within the life sciences: the history of biology reveals a complex, permanently moving landscape of interactions between biological and physical explanations. Copyright © 2010 Elsevier Ltd. All rights reserved.

NPIDB: Nucleic acid-Protein Interaction DataBase.

PubMed

Kirsanov, Dmitry D; Zanegina, Olga N; Aksianov, Evgeniy A; Spirin, Sergei A; Karyagina, Anna S; Alexeevski, Andrei V

2013-01-01

The Nucleic acid-Protein Interaction DataBase (http://npidb.belozersky.msu.ru/) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from the Protein Data Bank (3846 complexes in October 2012). It provides a web interface and a set of tools for extracting biologically meaningful characteristics of nucleoprotein complexes. The content of the database is updated weekly. The current version of the Nucleic acid-Protein Interaction DataBase is an upgrade of the version published in 2007. The improvements include a new web interface, new tools for calculation of intermolecular interactions, a classification of SCOP families that contains DNA-binding protein domains and data on conserved water molecules on the DNA-protein interface.

Responses to olfactory signals reflect network structure of flower-visitor interactions.

PubMed

Junker, Robert R; Höcherl, Nicole; Blüthgen, Nico

2010-07-01

1. Network analyses provide insights into the diversity and complexity of ecological interactions and have motivated conclusions about community stability and co-evolution. However, biological traits and mechanisms such as chemical signals regulating the interactions between individual species--the microstructure of a network--are poorly understood. 2. We linked the responses of receivers (flower visitors) towards signals (flower scent) to the structure of a highly diverse natural flower-insect network. For each interaction, we define link temperature--a newly developed metric--as the deviation of the observed interaction strength from neutrality, assuming that animals randomly interact with flowers. 3. Link temperature was positively correlated to the specific visitors' responses to floral scents, experimentally examined in a mobile olfactometer. Thus, communication between plants and consumers via phytochemical signals reflects a significant part of the microstructure in a complex network. Negative as well as positive responses towards floral scents contributed to these results, where individual experience was important apart from innate behaviour. 4. Our results indicate that: (1) biological mechanisms have a profound impact on the microstructure of complex networks that underlies the outcome of aggregate statistics, and (2) floral scents act as a filter, promoting the visitation of some flower visitors, but also inhibiting the visitation of others.

Spatial Modeling Tools for Cell Biology

DTIC Science & Technology

2006-10-01

multiphysics modeling expertise. A graphical user interface (GUI) for CoBi, JCoBi, was written in Java and interactive 3D graphics. CoBi has been...tools (C++ and Java ) to simulate complex cell and organ biology problems. CoBi has been designed to interact with the other Bio-SPICE software...fall of 2002. VisIt supports C++, Python and Java interfaces. The C++ and Java interfaces make it possible to provide alternate user interfaces for

Simulating Life

ERIC Educational Resources Information Center

Sinclair, Michael; Dauerty, Helene; Alber, Mark

2016-01-01

Biomodeling is the study of the structures and behaviors of interacting biological entities such as molecules, cells, or organisms. While physical and chemical processes give rise to various spatial and temporal structures, even the simplest biological phenomenon is infinitely more complex (Kling 2004). Over the past decade, much of biomodeling…

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

PubMed Central

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2017-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments. PMID:28190948

Enzyme Biosensors for Biomedical Applications: Strategies for Safeguarding Analytical Performances in Biological Fluids

PubMed Central

Rocchitta, Gaia; Spanu, Angela; Babudieri, Sergio; Latte, Gavinella; Madeddu, Giordano; Galleri, Grazia; Nuvoli, Susanna; Bagella, Paola; Demartis, Maria Ilaria; Fiore, Vito; Manetti, Roberto; Serra, Pier Andrea

2016-01-01

Enzyme-based chemical biosensors are based on biological recognition. In order to operate, the enzymes must be available to catalyze a specific biochemical reaction and be stable under the normal operating conditions of the biosensor. Design of biosensors is based on knowledge about the target analyte, as well as the complexity of the matrix in which the analyte has to be quantified. This article reviews the problems resulting from the interaction of enzyme-based amperometric biosensors with complex biological matrices containing the target analyte(s). One of the most challenging disadvantages of amperometric enzyme-based biosensor detection is signal reduction from fouling agents and interference from chemicals present in the sample matrix. This article, therefore, investigates the principles of functioning of enzymatic biosensors, their analytical performance over time and the strategies used to optimize their performance. Moreover, the composition of biological fluids as a function of their interaction with biosensing will be presented. PMID:27249001

A mathematical applications into the cells.

PubMed

Tiwari, Manjul

2012-01-01

Biology has become the new "physics" of mathematics, one of the areas of greatest mathematical applications. In turn, mathematics has provided powerful tools and metaphors to approach the astonishing complexity of biological systems. This has allowed the development of sound theoretical frameworks. Here, in this review article, some of the most significant contributions of mathematics to biology, ranging from population genetics, to developmental biology, and to networks of species interactions are summarized.

Intermolecular hydrogen bonds in hetero-complexes of biologically active aromatic molecules probed by the methods of vibrational spectroscopy.

PubMed

Semenov, M A; Blyzniuk, Iu N; Bolbukh, T V; Shestopalova, A V; Evstigneev, M P; Maleev, V Ya

2012-09-01

By the methods of vibrational spectroscopy (Infrared and Raman) the investigation of the hetero-association of biologically active aromatic compounds: flavin-mononucleotide (FMN), ethidium bromide (EB) and proflavine (PRF) was performed in aqueous solutions. It was shown that between the functional groups (CO and NH(2)) the intermolecular hydrogen bonds are formed in the hetero-complexes FMN-EB and FMN-PRF, additionally stabilizing these structures. An estimation of the enthalpy of Н-bonding obtained from experimental shifts of carbonyl vibrational frequencies has shown that the H-bonds do not dominate in the magnitude of experimentally measured total enthalpy of the hetero-association reactions. The main stabilization is likely due to intermolecular interactions of the molecules in these complexes and their interaction with water environment. Copyright © 2012 Elsevier B.V. All rights reserved.

Application of Biologically Based Lumping To Investigate the Toxicokinetic Interactions of a Complex Gasoline Mixture.

PubMed

Jasper, Micah N; Martin, Sheppard A; Oshiro, Wendy M; Ford, Jermaine; Bushnell, Philip J; El-Masri, Hisham

2016-03-15

People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. We developed an approach that applies chemical lumping methods to complex mixtures, in this case gasoline, based on biologically relevant parameters used in physiologically based pharmacokinetic (PBPK) modeling. Inhalation exposures were performed with rats to evaluate the performance of our PBPK model and chemical lumping method. There were 109 chemicals identified and quantified in the vapor in the chamber. The time-course toxicokinetic profiles of 10 target chemicals were also determined from blood samples collected during and following the in vivo experiments. A general PBPK model was used to compare the experimental data to the simulated values of blood concentration for 10 target chemicals with various numbers of lumps, iteratively increasing from 0 to 99. Large reductions in simulation error were gained by incorporating enzymatic chemical interactions, in comparison to simulating the individual chemicals separately. The error was further reduced by lumping the 99 nontarget chemicals. The same biologically based lumping approach can be used to simplify any complex mixture with tens, hundreds, or thousands of constituents.

Binding of small molecules at interface of protein-protein complex - A newer approach to rational drug design.

PubMed

Gurung, A B; Bhattacharjee, A; Ajmal Ali, M; Al-Hemaid, F; Lee, Joongku

2017-02-01

Protein-protein interaction is a vital process which drives many important physiological processes in the cell and has also been implicated in several diseases. Though the protein-protein interaction network is quite complex but understanding its interacting partners using both in silico as well as molecular biology techniques can provide better insights for targeting such interactions. Targeting protein-protein interaction with small molecules is a challenging task because of druggability issues. Nevertheless, several studies on the kinetics as well as thermodynamic properties of protein-protein interactions have immensely contributed toward better understanding of the affinity of these complexes. But, more recent studies on hot spots and interface residues have opened up new avenues in the drug discovery process. This approach has been used in the design of hot spot based modulators targeting protein-protein interaction with the objective of normalizing such interactions.

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.

Encyrtid parasitoids of soft scale insects: biology, behavior, and their use in biological control.

PubMed

Kapranas, Apostolos; Tena, Alejandro

2015-01-07

Parasitoids of the hymenopterous family Encyrtidae are one of the most important groups of natural enemies of soft scale insects and have been used extensively in biological control. We summarize existing knowledge of the biology, ecology, and behavior of these parasitoids and how it relates to biological control. Soft scale stage/size and phenology are important determinants of host range and host utilization, which are key aspects in understanding how control by these parasitoids is exerted. Furthermore, the nutritional ecology of encyrtids and their physiological interactions with their hosts affect soft scale insect population dynamics. Lastly, the interactions among encyrtids, heteronomous parasitoids, and ants shape parasitoid species complexes and consequently have a direct impact on the biological control of soft scale insects.

Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution

PubMed Central

He, Yi-Ming; Ma, Bin-Guang

2016-01-01

Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions. PMID:27220911

Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution

NASA Astrophysics Data System (ADS)

He, Yi-Ming; Ma, Bin-Guang

2016-05-01

Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions.

Interactions of flavanoids with bradykinin in aqueous solution

Treesearch

B. Berke; F.L. Tobiason; T. Hatano; C Cheze; J. Vercauteren; Richard W. Hemingway

1999-01-01

Complexation with proteins is central to much of the biological and industrial significance of plant polyphenols. Definition of the interaction of these two classes of biopolymers has, therefore, been studied for decades. The most important mechanism seems to involve hydrophobic interactions and also hydrogen bonding, but to a smaller extent. Study of specific...

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

Diversity-oriented synthetic strategy for developing a chemical modulator of protein-protein interaction

NASA Astrophysics Data System (ADS)

Kim, Jonghoon; Jung, Jinjoo; Koo, Jaeyoung; Cho, Wansang; Lee, Won Seok; Kim, Chanwoo; Park, Wonwoo; Park, Seung Bum

2016-10-01

Diversity-oriented synthesis (DOS) can provide a collection of diverse and complex drug-like small molecules, which is critical in the development of new chemical probes for biological research of undruggable targets. However, the design and synthesis of small-molecule libraries with improved biological relevance as well as maximized molecular diversity represent a key challenge. Herein, we employ functional group-pairing strategy for the DOS of a chemical library containing privileged substructures, pyrimidodiazepine or pyrimidine moieties, as chemical navigators towards unexplored bioactive chemical space. To validate the utility of this DOS library, we identify a new small-molecule inhibitor of leucyl-tRNA synthetase-RagD protein-protein interaction, which regulates the amino acid-dependent activation of mechanistic target of rapamycin complex 1 signalling pathway. This work highlights that privileged substructure-based DOS strategy can be a powerful research tool for the construction of drug-like compounds to address challenging biological targets.

Identifying Ant-Mirid Spatial Interactions to Improve Biological Control in Cacao-Based Agroforestry System.

PubMed

Bagny Beilhe, Leïla; Piou, Cyril; Tadu, Zéphirin; Babin, Régis

2018-06-06

The use of ants for biological control of insect pests was the first reported case of conservation biological control. Direct and indirect community interactions between ants and pests lead to differential spatial pattern. We investigated spatial interactions between mirids, the major cocoa pest in West Africa and numerically dominant ant species, using bivariate point pattern analysis to identify potential biological control agents. We assume that potential biological control agents should display negative spatial interactions with mirids considering their niche overlap. The mirid/ant data were collected in complex cacao-based agroforestry systems sampled in three agroecological areas over a forest-savannah gradient in Cameroon. Three species, Crematogaster striatula Emery (Hymenoptera: Formicidae), Crematogaster clariventris Mayr (Hymenoptera: Formicidae), and Oecophylla longinoda Latreille (Hymenoptera: Formicidae) with high predator and aggressive behaviors were identified as dominant and showed negative spatial relationships with mirids. The weaver ant, O. longinoda was identified as the only potential biological control agent, considering its ubiquity in the plots, the similarity in niche requirements, and the spatial segregation with mirids resulting probably from exclusion mechanisms. Combining bivariate point pattern analysis to good knowledge of insect ecology was an effective method to identify a potentially good biological control agent.

Synthesis and characterization of new unsymmetrical Schiff base Zn (II) and Co (II) complexes and study of their interactions with bovin serum albumin and DNA by spectroscopic techniques.

PubMed

Sedighipoor, Maryam; Kianfar, Ali Hossein; Sabzalian, Mohammad R; Abyar, Fatemeh

2018-06-05

Two novel tetra-coordinated Cobalt(II) and Zinc (II) chelate series with the general formula of [Co (L)·2H 2 O] (1) and [Zn (L)] (2) [L=N-2-hydroxyacetophenon-N'-2-hydroxynaphthaldehyde-1,2 phenylenediimine)] with biologically active Schiff base ligands were synthesized and recognized by elemental analysis and multi-nuclear spectroscopy (IR and 1 H and 13 C NMR); then, their biological activities including DNA and protein interactions were studied. The interaction of the synthesized compounds with bovine serum albumin (BSA) was investigated via fluorescence spectroscopy, showing the affinity of the complexes for these proteins with relatively high binding constant values and the changed secondary BSA structure in the presence of the complexes. The interaction of these compounds with CT-DNA was considered by UV-Vis technique, emission titration, viscosity measurements, helix melting methods, and circular dichroism (CD) spectroscopy, confirming that the complexes were bound to CT-DNA by the intercalation binding mode. Furthermore, the complexes had the capability to displace the DNA-bound MB, as shown by the competitive studies of these complexes with methylene blue (MB), thereby suggesting the intercalation mode for the competition. Finally, the theoretical studies carried out by the docking method were performed to calculate the binding constants and recognize the binding site of the BSA and DNA by the complexes. In addition, in vitro and in silico studies showed that the compounds were degradable by bacterial and fungal biodegradation activities. Copyright © 2018 Elsevier B.V. All rights reserved.

Synthesis and characterization of new unsymmetrical Schiff base Zn (II) and Co (II) complexes and study of their interactions with bovin serum albumin and DNA by spectroscopic techniques

NASA Astrophysics Data System (ADS)

Sedighipoor, Maryam; Kianfar, Ali Hossein; Sabzalian, Mohammad R.; Abyar, Fatemeh

2018-06-01

Two novel tetra-coordinated Cobalt(II) and Zinc (II) chelate series with the general formula of [Co (L)·2H2O] (1) and [Zn (L)] (2) [L = N-2-hydroxyacetophenon-N‧-2-hydroxynaphthaldehyde-1,2 phenylenediimine)] with biologically active Schiff base ligands were synthesized and recognized by elemental analysis and multi-nuclear spectroscopy (IR and 1H and 13C NMR); then, their biological activities including DNA and protein interactions were studied. The interaction of the synthesized compounds with bovine serum albumin (BSA) was investigated via fluorescence spectroscopy, showing the affinity of the complexes for these proteins with relatively high binding constant values and the changed secondary BSA structure in the presence of the complexes. The interaction of these compounds with CT-DNA was considered by UV-Vis technique, emission titration, viscosity measurements, helix melting methods, and circular dichroism (CD) spectroscopy, confirming that the complexes were bound to CT-DNA by the intercalation binding mode. Furthermore, the complexes had the capability to displace the DNA-bound MB, as shown by the competitive studies of these complexes with methylene blue (MB), thereby suggesting the intercalation mode for the competition. Finally, the theoretical studies carried out by the docking method were performed to calculate the binding constants and recognize the binding site of the BSA and DNA by the complexes. In addition, in vitro and in silico studies showed that the compounds were degradable by bacterial and fungal biodegradation activities.

Gene-Environment Interactions in Cardiovascular Disease

PubMed Central

Flowers, Elena; Froelicher, Erika Sivarajan; Aouizerat, Bradley E.

2011-01-01

Background Historically, models to describe disease were exclusively nature-based or nurture-based. Current theoretical models for complex conditions such as cardiovascular disease acknowledge the importance of both biologic and non-biologic contributors to disease. A critical feature is the occurrence of interactions between numerous risk factors for disease. The interaction between genetic (i.e. biologic, nature) and environmental (i.e. non-biologic, nurture) causes of disease is an important mechanism for understanding both the etiology and public health impact of cardiovascular disease. Objectives The purpose of this paper is to describe theoretical underpinnings of gene-environment interactions, models of interaction, methods for studying gene-environment interactions, and the related concept of interactions between epigenetic mechanisms and the environment. Discussion Advances in methods for measurement of genetic predictors of disease have enabled an increasingly comprehensive understanding of the causes of disease. In order to fully describe the effects of genetic predictors of disease, it is necessary to place genetic predictors within the context of known environmental risk factors. The additive or multiplicative effect of the interaction between genetic and environmental risk factors is often greater than the contribution of either risk factor alone. PMID:21684212

Biological network extraction from scientific literature: state of the art and challenges.

PubMed

Li, Chen; Liakata, Maria; Rebholz-Schuhmann, Dietrich

2014-09-01

Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

DNA binding and biological studies of some novel water-soluble polymer-copper(II)-phenanthroline complexes.

PubMed

Kumar, Rajendran Senthil; Arunachalam, Sankaralingam; Periasamy, Vaiyapuri Subbarayan; Preethy, Christo Paul; Riyasdeen, Anvarbatcha; Akbarsha, Mohammad Abdulkader

2008-10-01

Some novel water-soluble polymer-copper(II)-phenanthroline complex samples, [Cu(phen)2(BPEI)]Cl(2).4H2O (phen=1,10-phenanthroline, BPEI=branched polyethyleneimine), with different degrees of copper complex content in the polymer chain have been prepared by ligand substitution method in water-ethanol medium and characterized by infrared, UV-visible, EPR spectral and elemental analysis methods. The binding of these complex samples with DNA has been investigated by electronic absorption spectroscopy, emission spectroscopy and gel retardation assay. Electrostatic interactions between DNA molecule and polymer-copper(II) complex molecule containing many high positive charges have been observed. Besides these ionic interactions, van der Waals interactions, hydrogen bonding and other partial intercalation binding modes may also exist in this system. The polymer-copper(II) complex with higher degree of copper complex content was screened for its antimicrobial activity and antitumor activity.

Noninvasive imaging of protein-protein interactions in living organisms.

PubMed

Haberkorn, Uwe; Altmann, Annette

2003-06-01

Genomic research is expected to generate new types of complex observational data, changing the types of experiments as well as our understanding of biological processes. The investigation and definition of relationships among proteins is essential for understanding the function of each gene and the mechanisms of biological processes that specific genes are involved in. Recently, a study by Paulmurugan et al. demonstrated a tool for in vivo noninvasive imaging of protein-protein interactions and intracellular networks.

Using Biomolecules to Separate Plutonium

NASA Astrophysics Data System (ADS)

Gogolski, Jarrod

Used nuclear fuel has traditionally been treated through chemical separations of the radionuclides for recycle or disposal. This research considers a biological approach to such separations based on a series of complex and interdependent interactions that occur naturally in the human body with plutonium. These biological interactions are mediated by the proteins serum transferrin and the transferrin receptor. Transferrin to plutonium in vivo and can deposit plutonium into cells after interacting with the transferrin receptor protein at the cell surface. Using cerium as a non-radioactive surrogate for plutonium, it was found that cerium(IV) required multiple synergistic anions to bind in the N-lobe of the bilobal transferrin protein, creating a conformation of the cerium-loaded protein that would be unable to interact with the transferrin receptor protein to achieve a separation. The behavior of cerium binding to transferrin has contributed to understanding how plutonium(IV)-transferrin interacts in vivo and in biological separations.

A global interaction network maps a wiring diagram of cellular function

PubMed Central

Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N.; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D.; Pelechano, Vicent; Styles, Erin B.; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S.; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F.; Li, Sheena C.; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; Luis, Bryan-Joseph San; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W.; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G.; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M.; Moore, Claire L.; Rosebrock, Adam P.; Caudy, Amy A.; Myers, Chad L.; Andrews, Brenda; Boone, Charles

2017-01-01

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing over 23 million double mutants, identifying ~550,000 negative and ~350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell. PMID:27708008

Micro/nanofabricated environments for synthetic biology.

PubMed

Collier, C Patrick; Simpson, Michael L

2011-08-01

A better understanding of how confinement, crowding and reduced dimensionality modulate reactivity and reaction dynamics will aid in the rational and systematic discovery of functionality in complex biological systems. Artificial microfabricated and nanofabricated structures have helped elucidate the effects of nanoscale spatial confinement and segregation on biological behavior, particularly when integrated with microfluidics, through precise control in both space and time of diffusible signals and binding interactions. Examples of nanostructured interfaces for synthetic biology include the development of cell-like compartments for encapsulating biochemical reactions, nanostructured environments for fundamental studies of diffusion, molecular transport and biochemical reaction kinetics, and regulation of biomolecular interactions as functions of microfabricated and nanofabricated topological constraints. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

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.

MECHANISTIC INDICATORS OF CHILDHOOD ASTHMA (MICA): A SYSTEMS BIOLOGY APPROACH FOR THE INTEGRATION OF MULTIFACTORIAL EXPOSURE AND ENVIRONMENTAL HEALTH DATA

EPA Science Inventory

Modem methods in molecular biology and advanced computational tools show promise in elucidating complex interactions that occur between genes and environmental factors in diseases such as asthma. However, appropriately designed studies are critical for these methods to reach the...

NMR studies of protein-nucleic acid interactions.

PubMed

Varani, Gabriele; Chen, Yu; Leeper, Thomas C

2004-01-01

Protein-DNA and protein-RNA complexes play key functional roles in every living organism. Therefore, the elucidation of their structure and dynamics is an important goal of structural and molecular biology. Nuclear magnetic resonance (NMR) studies of protein and nucleic acid complexes have common features with studies of protein-protein complexes: the interaction surfaces between the molecules must be carefully delineated, the relative orientation of the two species needs to be accurately and precisely determined, and close intermolecular contacts defined by nuclear Overhauser effects (NOEs) must be obtained. However, differences in NMR properties (e.g., chemical shifts) and biosynthetic pathways for sample productions generate important differences. Chemical shift differences between the protein and nucleic acid resonances can aid the NMR structure determination process; however, the relatively limited dispersion of the RNA ribose resonances makes the process of assigning intermolecular NOEs more difficult. The analysis of the resulting structures requires computational tools unique to nucleic acid interactions. This chapter summarizes the most important elements of the structure determination by NMR of protein-nucleic acid complexes and their analysis. The main emphasis is on recent developments (e.g., residual dipolar couplings and new Web-based analysis tools) that have facilitated NMR studies of these complexes and expanded the type of biological problems to which NMR techniques of structural elucidation can now be applied.

An organelle-specific protein landscape identifies novel diseases and molecular mechanisms

PubMed Central

Boldt, Karsten; van Reeuwijk, Jeroen; Lu, Qianhao; Koutroumpas, Konstantinos; Nguyen, Thanh-Minh T.; Texier, Yves; van Beersum, Sylvia E. C.; Horn, Nicola; Willer, Jason R.; Mans, Dorus A.; Dougherty, Gerard; Lamers, Ideke J. C.; Coene, Karlien L. M.; Arts, Heleen H.; Betts, Matthew J.; Beyer, Tina; Bolat, Emine; Gloeckner, Christian Johannes; Haidari, Khatera; Hetterschijt, Lisette; Iaconis, Daniela; Jenkins, Dagan; Klose, Franziska; Knapp, Barbara; Latour, Brooke; Letteboer, Stef J. F.; Marcelis, Carlo L.; Mitic, Dragana; Morleo, Manuela; Oud, Machteld M.; Riemersma, Moniek; Rix, Susan; Terhal, Paulien A.; Toedt, Grischa; van Dam, Teunis J. P.; de Vrieze, Erik; Wissinger, Yasmin; Wu, Ka Man; Apic, Gordana; Beales, Philip L.; Blacque, Oliver E.; Gibson, Toby J.; Huynen, Martijn A.; Katsanis, Nicholas; Kremer, Hannie; Omran, Heymut; van Wijk, Erwin; Wolfrum, Uwe; Kepes, François; Davis, Erica E.; Franco, Brunella; Giles, Rachel H.; Ueffing, Marius; Russell, Robert B.; Roepman, Ronald; Al-Turki, Saeed; Anderson, Carl; Antony, Dinu; Barroso, Inês; Bentham, Jamie; Bhattacharya, Shoumo; Carss, Keren; Chatterjee, Krishna; Cirak, Sebahattin; Cosgrove, Catherine; Danecek, Petr; Durbin, Richard; Fitzpatrick, David; Floyd, Jamie; Reghan Foley, A.; Franklin, Chris; Futema, Marta; Humphries, Steve E.; Hurles, Matt; Joyce, Chris; McCarthy, Shane; Mitchison, Hannah M.; Muddyman, Dawn; Muntoni, Francesco; O'Rahilly, Stephen; Onoufriadis, Alexandros; Payne, Felicity; Plagnol, Vincent; Raymond, Lucy; Savage, David B.; Scambler, Peter; Schmidts, Miriam; Schoenmakers, Nadia; Semple, Robert; Serra, Eva; Stalker, Jim; van Kogelenberg, Margriet; Vijayarangakannan, Parthiban; Walter, Klaudia; Whittall, Ros; Williamson, Kathy

2016-01-01

Cellular organelles provide opportunities to relate biological mechanisms to disease. Here we use affinity proteomics, genetics and cell biology to interrogate cilia: poorly understood organelles, where defects cause genetic diseases. Two hundred and seventeen tagged human ciliary proteins create a final landscape of 1,319 proteins, 4,905 interactions and 52 complexes. Reverse tagging, repetition of purifications and statistical analyses, produce a high-resolution network that reveals organelle-specific interactions and complexes not apparent in larger studies, and links vesicle transport, the cytoskeleton, signalling and ubiquitination to ciliary signalling and proteostasis. We observe sub-complexes in exocyst and intraflagellar transport complexes, which we validate biochemically, and by probing structurally predicted, disruptive, genetic variants from ciliary disease patients. The landscape suggests other genetic diseases could be ciliary including 3M syndrome. We show that 3M genes are involved in ciliogenesis, and that patient fibroblasts lack cilia. Overall, this organelle-specific targeting strategy shows considerable promise for Systems Medicine. PMID:27173435

Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions.

PubMed

Record, Sydne; Strecker, Angela; Tuanmu, Mao-Ning; Beaudrot, Lydia; Zarnetske, Phoebe; Belmaker, Jonathan; Gerstner, Beth

2018-01-01

There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species' responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. We systematically reviewed SDM studies published from 2003-2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.

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.

Towards the understanding of network information processing in biology

NASA Astrophysics Data System (ADS)

Singh, Vijay

Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

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.

Synthesis, spectroscopic characterization, biological screenings, DNA binding study and POM analyses of transition metal carboxylates.

PubMed

Uddin, Noor; Sirajuddin, Muhammad; Uddin, Nizam; Tariq, Muhammad; Ullah, Hameed; Ali, Saqib; Tirmizi, Syed Ahmed; Khan, Abdur Rehman

2015-04-05

This article contains the synthesis of a novel carboxylic acid derivative, its transition metal complexes and evaluation of biological applications. Six carboxylate complexes of transition metals, Zn(II) and Hg(II), have been successfully synthesized and characterized by FT-IR and NMR (1H, 13C). The ligand, HL, (4-[(2,6-Diethylphenyl)amino]-4-oxobutanoic acid) was also characterized by single crystal X-ray analysis. The complexation occurs via oxygen atoms of the carboxylate moiety. FT-IR date show the bidentate nature of the carboxylate moiety of the ligand as the Δν value in all complexes is less than that of the free ligand. The ligand and its complexes were screened for antifungal and antileishmanial activities. The results showed that the ligand and its complexes are active with few exceptions. UV-visible spectroscopy and viscometry results reveal that the ligand and its complexes interact with the DNA via intercalative mode of interaction. A new and efficient strategy to identify the pharmacophores and anti-pharmacophores sites in carboxylate derivatives for the antibacterial/antifungal activity using Petra, Osiris and Molinspiration (POM) analyses was also carried out. Copyright © 2014 Elsevier B.V. All rights reserved.

Synthesis, spectroscopic characterization, biological screenings, DNA binding study and POM analyses of transition metal carboxylates

NASA Astrophysics Data System (ADS)

Uddin, Noor; Sirajuddin, Muhammad; Uddin, Nizam; Tariq, Muhammad; Ullah, Hameed; Ali, Saqib; Tirmizi, Syed Ahmed; Khan, Abdur Rehman

2015-04-01

This article contains the synthesis of a novel carboxylic acid derivative, its transition metal complexes and evaluation of biological applications. Six carboxylate complexes of transition metals, Zn(II) and Hg(II), have been successfully synthesized and characterized by FT-IR and NMR (1H, 13C). The ligand, HL, (4-[(2,6-Diethylphenyl)amino]-4-oxobutanoic acid) was also characterized by single crystal X-ray analysis. The complexation occurs via oxygen atoms of the carboxylate moiety. FT-IR date show the bidentate nature of the carboxylate moiety of the ligand as the Δν value in all complexes is less than that of the free ligand. The ligand and its complexes were screened for antifungal and antileishmanial activities. The results showed that the ligand and its complexes are active with few exceptions. UV-visible spectroscopy and viscometry results reveal that the ligand and its complexes interact with the DNA via intercalative mode of interaction. A new and efficient strategy to identify the pharmacophores and anti-pharmacophores sites in carboxylate derivatives for the antibacterial/antifungal activity using Petra, Osiris and Molinspiration (POM) analyses was also carried out.

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.

Vitronectin--master controller or micromanager?

PubMed

Leavesley, David I; Kashyap, Abhishek S; Croll, Tristan; Sivaramakrishnan, Manaswini; Shokoohmand, Ali; Hollier, Brett G; Upton, Zee

2013-10-01

The concept that the mammalian glycoprotein vitronectin acts as a biological 'glue' and key controller of mammalian tissue repair and remodelling activity is emerging from nearly 50 years of experimental in vitro and in vivo data. Unexpectedly, the vitronectin-knockout (VN-KO) mouse was found to be viable and to have largely normal phenotype. However, diligent observation revealed that the VN-KO animal exhibits delayed coagulation and poor wound healing. This is interpreted to indicate that VN occupies a role in the earliest events of thrombogenesis and tissue repair. VN is the foundation upon which the thrombus grows in an organised structure. In addition to sealing the wound, the thrombus also serves to protect the underlying tissue from oxidation, is a reservoir of mitogens and tissue repair mediators, and provides a provisional scaffold for the repairing tissue. In the absence of VN (e.g., VN-KO animal), this cascade is disrupted before it begins. A wide variety of biologically active species associate with VN. Although initial studies were focused on mitogens, other classes of bioactives (e.g., glycosaminoglycans and metalloproteinases) are now also known to specifically interact with VN. Although some interactions are transient, others are long-lived and often result in multi-protein complexes. Multi-protein complexes provide several advantages: prolonging molecular interactions, sustaining local concentrations, facilitating co-stimulation of cell surface receptors and thereby enhancing cellular/biological responses. We contend that these, or equivalent, multi-protein complexes facilitate VN polyfunctionality in vivo. It is also likely that many of the species demonstrated to associate with VN in vitro, also associate with VN in vivo in similar multi-protein complexes. Thus, the predominant biological function of VN is that of a master controller of the extracellular environment; informing, and possibly instructing cells 'where' to behave, 'when' to behave and 'how' to behave (i.e., appropriately for the current circumstance). © 2013 International Union of Biochemistry and Molecular Biology.

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.

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.

Platform construction and extraction mechanism study of magnetic mixed hemimicelles solid-phase extraction

NASA Astrophysics Data System (ADS)

Xiao, Deli; Zhang, Chan; He, Jia; Zeng, Rong; Chen, Rong; He, Hua

2016-12-01

Simple, accurate and high-throughput pretreatment method would facilitate large-scale studies of trace analysis in complex samples. Magnetic mixed hemimicelles solid-phase extraction has the power to become a key pretreatment method in biological, environmental and clinical research. However, lacking of experimental predictability and unsharpness of extraction mechanism limit the development of this promising method. Herein, this work tries to establish theoretical-based experimental designs for extraction of trace analytes from complex samples using magnetic mixed hemimicelles solid-phase extraction. We selected three categories and six sub-types of compounds for systematic comparative study of extraction mechanism, and comprehensively illustrated the roles of different force (hydrophobic interaction, π-π stacking interactions, hydrogen-bonding interaction, electrostatic interaction) for the first time. What’s more, the application guidelines for supporting materials, surfactants and sample matrix were also summarized. The extraction mechanism and platform established in the study render its future promising for foreseeable and efficient pretreatment under theoretical based experimental design for trace analytes from environmental, biological and clinical samples.

Visualization of molecular structures using HoloLens-based augmented reality

PubMed Central

Hoffman, MA; Provance, JB

2017-01-01

Biological molecules and biologically active small molecules are complex three dimensional structures. Current flat screen monitors are limited in their ability to convey the full three dimensional characteristics of these molecules. Augmented reality devices, including the Microsoft HoloLens, offer an immersive platform to change how we interact with molecular visualizations. We describe a process to incorporate the three dimensional structures of small molecules and complex proteins into the Microsoft HoloLens using aspirin and the human leukocyte antigen (HLA) as examples. Small molecular structures can be introduced into the HoloStudio application, which provides native support for rotating, resizing and performing other interactions with these molecules. Larger molecules can be imported through the Unity gaming development platform and then Microsoft Visual Developer. The processes described here can be modified to import a wide variety of molecular structures into augmented reality systems and improve our comprehension of complex structural features. PMID:28815109

Streptomyces metabolites in divergent microbial interactions.

PubMed

Takano, Hideaki; Nishiyama, Tatsuya; Amano, Sho-ichi; Beppu, Teruhiko; Kobayashi, Michihiko; Ueda, Kenji

2016-03-01

Streptomyces and related bacteria produce a wide variety of secondary metabolites. Of these, many compounds have industrial applications, but the question of why this group of microorganism produces such various kinds of biologically active substances has not yet been clearly answered. Here, we overview the results from our studies on the novel function and role of Streptomyces metabolites. The diverged action of negative and positive influences onto the physiology of various microorganisms infers the occurrence of complex microbial interactions due to the effect of small molecules produced by Streptomyces. The interactions may serve as a basis for the constitution of biological community.

Temporal changes in species interactions in simple aquatic bacterial communities

PubMed Central

2012-01-01

Background Organisms modify their environment and in doing so change the quantity and possibly the quality of available resources. Due to the two-way relationship between organisms and their resource environment, and the complexity it brings to biological communities, measuring species interactions reliably in any biological system is a challenging task. As the resource environment changes, the intensity and even the sign of interactions may vary in time. We used Serratia marcescens and Novosphingobium capsulatum bacteria to study how the interaction between resource environment and organisms influence the growth of the bacterial species during circa 200 generations. We used a sterile-filtering method to measure how changes in resource environment are reflected in growth rates of the two species. Results Changes in the resource environment caused complex time and species composition-dependent effects on bacterial growth performance. Variation in the quality of the growth medium indicated existence of temporally fluctuating within-species facilitation and inhibition, and between-species asymmetric facilitation. Conclusions The interactions between the community members could not be fully predicted based only on the knowledge of the growth performance of each member in isolation. Growth dynamics in sterile-filtered samples of the conditioned growth medium can reveal both biologically meaningful changes in resource availability and temporally changing facilitative resource-mediated interactions between study species. This is the first study we are aware of where the filter-sterilization – growth assay method is applied to study the effect of long-term changes in the environment on species interactions. PMID:22984961

Systems biology impact on antiepileptic drug discovery.

PubMed

Margineanu, Doru Georg

2012-02-01

Systems biology (SB), a recent trend in bioscience research to consider the complex interactions in biological systems from a holistic perspective, sees the disease as a disturbed network of interactions, rather than alteration of single molecular component(s). SB-relying network pharmacology replaces the prevailing focus on specific drug-receptor interaction and the corollary of rational drug design of "magic bullets", by the search for multi-target drugs that would act on biological networks as "magic shotguns". Epilepsy being a multi-factorial, polygenic and dynamic pathology, SB approach appears particularly fit and promising for antiepileptic drug (AED) discovery. In fact, long before the advent of SB, AED discovery already involved some SB-like elements. A reported SB project aimed to find out new drug targets in epilepsy relies on a relational database that integrates clinical information, recordings from deep electrodes and 3D-brain imagery with histology and molecular biology data on modified expression of specific genes in the brain regions displaying spontaneous epileptic activity. Since hitting a single target does not treat complex diseases, a proper pharmacological promiscuity might impart on an AED the merit of being multi-potent. However, multi-target drug discovery entails the complicated task of optimizing multiple activities of compounds, while having to balance drug-like properties and to control unwanted effects. Specific design tools for this new approach in drug discovery barely emerge, but computational methods making reliable in silico predictions of poly-pharmacology did appear, and their progress might be quite rapid. The current move away from reductionism into network pharmacology allows expecting that a proper integration of the intrinsic complexity of epileptic pathology in AED discovery might result in literally anti-epileptic drugs. Copyright © 2011 Elsevier B.V. All rights reserved.

Identifying protein complexes based on brainstorming strategy.

PubMed

Shen, Xianjun; Zhou, Jin; Yi, Li; Hu, Xiaohua; He, Tingting; Yang, Jincai

2016-11-01

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance. Copyright © 2016 Elsevier Inc. All rights reserved.

Modular scanning FCS quantifies receptor-ligand interactions in living multicellular organisms.

PubMed

Ries, Jonas; Yu, Shuizi Rachel; Burkhardt, Markus; Brand, Michael; Schwille, Petra

2009-09-01

Analysis of receptor-ligand interactions in vivo is key to biology but poses a considerable challenge to quantitative microscopy. Here we combine static-volume, two-focus and dual-color scanning fluorescence correlation spectroscopy to solve this task at cellular resolution in complex biological environments. We quantified the mobility of fibroblast growth factor receptors Fgfr1 and Fgfr4 in cell membranes of living zebrafish embryos and determined their in vivo binding affinities to their ligand Fgf8.

Systems Biology-Based Investigation of Host-Plasmodium Interactions.

PubMed

Smith, Maren L; Styczynski, Mark P

2018-05-18

Malaria is a serious, complex disease caused by parasites of the genus Plasmodium. Plasmodium parasites affect multiple tissues as they evade immune responses, replicate, sexually reproduce, and transmit between vertebrate and invertebrate hosts. The explosion of omics technologies has enabled large-scale collection of Plasmodium infection data, revealing systems-scale patterns, mechanisms of pathogenesis, and the ways that host and pathogen affect each other. Here, we provide an overview of recent efforts using systems biology approaches to study host-Plasmodium interactions and the biological themes that have emerged from these efforts. We discuss some of the challenges in using systems biology for this goal, key research efforts needed to address those issues, and promising future malaria applications of systems biology. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quantitative genetic-interaction mapping in mammalian cells

PubMed Central

Roguev, Assen; Talbot, Dale; Negri, Gian Luca; Shales, Michael; Cagney, Gerard; Bandyopadhyay, Sourav; Panning, Barbara; Krogan, Nevan J

2013-01-01

Mapping genetic interactions (GIs) by simultaneously perturbing pairs of genes is a powerful tool for understanding complex biological phenomena. Here we describe an experimental platform for generating quantitative GI maps in mammalian cells using a combinatorial RNA interference strategy. We performed ~11,000 pairwise knockdowns in mouse fibroblasts, focusing on 130 factors involved in chromatin regulation to create a GI map. Comparison of the GI and protein-protein interaction (PPI) data revealed that pairs of genes exhibiting positive GIs and/or similar genetic profiles were predictive of the corresponding proteins being physically associated. The mammalian GI map identified pathways and complexes but also resolved functionally distinct submodules within larger protein complexes. By integrating GI and PPI data, we created a functional map of chromatin complexes in mouse fibroblasts, revealing that the PAF complex is a central player in the mammalian chromatin landscape. PMID:23407553

An integrative approach to inferring biologically meaningful gene modules.

PubMed

Cho, Ji-Hoon; Wang, Kai; Galas, David J

2011-07-26

The ability to construct biologically meaningful gene networks and modules is critical for contemporary systems biology. Though recent studies have demonstrated the power of using gene modules to shed light on the functioning of complex biological systems, most modules in these networks have shown little association with meaningful biological function. We have devised a method which directly incorporates gene ontology (GO) annotation in construction of gene modules in order to gain better functional association. We have devised a method, Semantic Similarity-Integrated approach for Modularization (SSIM) that integrates various gene-gene pairwise similarity values, including information obtained from gene expression, protein-protein interactions and GO annotations, in the construction of modules using affinity propagation clustering. We demonstrated the performance of the proposed method using data from two complex biological responses: 1. the osmotic shock response in Saccharomyces cerevisiae, and 2. the prion-induced pathogenic mouse model. In comparison with two previously reported algorithms, modules identified by SSIM showed significantly stronger association with biological functions. The incorporation of semantic similarity based on GO annotation with gene expression and protein-protein interaction data can greatly enhance the functional relevance of inferred gene modules. In addition, the SSIM approach can also reveal the hierarchical structure of gene modules to gain a broader functional view of the biological system. Hence, the proposed method can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level.

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

Single-molecule pull-down (SiMPull) for new-age biochemistry: methodology and biochemical applications of single-molecule pull-down (SiMPull) for probing biomolecular interactions in crude cell extracts.

PubMed

Aggarwal, Vasudha; Ha, Taekjip

2014-11-01

Macromolecular interactions play a central role in many biological processes. Protein-protein interactions have mostly been studied by co-immunoprecipitation, which cannot provide quantitative information on all possible molecular connections present in the complex. We will review a new approach that allows cellular proteins and biomolecular complexes to be studied in real-time at the single-molecule level. This technique is called single-molecule pull-down (SiMPull), because it integrates principles of conventional immunoprecipitation with the powerful single-molecule fluorescence microscopy. SiMPull is used to count how many of each protein is present in the physiological complexes found in cytosol and membranes. Concurrently, it serves as a single-molecule biochemical tool to perform functional studies on the pulled-down proteins. In this review, we will focus on the detailed methodology of SiMPull, its salient features and a wide range of biological applications in comparison with other biosensing tools. © 2014 WILEY Periodicals, Inc.

DETECTION OF TWO ISOMERIC BINDING CONFIGURATIONS IN A PROTEIN-APTAMER COMPLEX WITH A BIOLOGICAL NANOPORE

PubMed Central

Van Meervelt, Veerle; Soskine, Misha; Maglia, Giovanni

2015-01-01

Protein-DNA interactions play critical roles in biological systems, and they often involve complex mechanisms and dynamics that are not easily measured by ensemble experiments. Recently, we have shown that folded proteins can be internalised inside ClyA nanopores and studied by ionic current recordings at the single-molecule level. Here, we use ClyA nanopores to sample the interaction between the G-quadruplex fold of the thrombin binding aptamer (TBA) and human thrombin (HT). Surprisingly, the internalisation of the HT:TBA complex inside the nanopore induced two types of current blockades with distinguished residual current and lifetime. Using single nucleobase substitutions to TBA we showed that these two types of blockades originate from TBA binding to thrombin with two isomeric orientations. Voltage dependencies and the use of ClyA nanopores with two different diameters allowed assessing the effect of the applied potential and confinement, and revealed that the two binding configurations of TBA to HT display different lifetimes. These results show that the ClyA nanopores might provide a new approach to probe conformational heterogeneity in protein:DNA interactions. PMID:25493908

A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

PubMed Central

2011-01-01

Background Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species. Results We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis. Conclusions We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis. PMID:21232107

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

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

Parent-Child Interactions, Peripheral Serotonin, and Self-Inflicted Injury in Adolescents

ERIC Educational Resources Information Center

Crowell, Sheila E.; Beauchaine, Theodore P.; McCauley, Elizabeth; Smith, Cindy J.; Vasilev, Christina A.; Stevens, Adrianne L.

2008-01-01

Self-inflicted injury in adolescence indicates significant emotional and psychological suffering. Although data on the etiology of self-injury are limited, current theories suggest that the emotional lability observed among self-injuring adolescents results from complex interactions between individual biological vulnerabilities and environmental…

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

GENIUS: web server to predict local gene networks and key genes for biological functions.

PubMed

Puelma, Tomas; Araus, Viviana; Canales, Javier; Vidal, Elena A; Cabello, Juan M; Soto, Alvaro; Gutiérrez, Rodrigo A

2017-03-01

GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods. GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius . genius.psbl@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Mapping complex traits as a dynamic system

PubMed Central

Sun, Lidan; Wu, Rongling

2017-01-01

Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a “system” in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states. PMID:25772476

Inhibition of herpesvirus and influenza virus replication by blocking polymerase subunit interactions.

PubMed

Palù, Giorgio; Loregian, Arianna

2013-09-01

Protein-protein interactions (PPIs) play a key role in many biological processes, including virus replication in the host cell. Since most of the PPIs are functionally essential, a possible strategy to inhibit virus replication is based on the disruption of viral protein complexes by peptides or small molecules that interfere with subunit interactions. In particular, an attractive target for antiviral drugs is the binding between the subunits of essential viral enzymes. This review describes the development of new antiviral compounds that inhibit herpesvirus and influenza virus replication by blocking interactions between subunit proteins of their polymerase complexes. Copyright © 2013 Elsevier B.V. All rights reserved.

Nanomaterial-microbe cross-talk: physicochemical principles and (patho)biological consequences.

PubMed

Westmeier, D; Hahlbrock, A; Reinhardt, C; Fröhlich-Nowoisky, J; Wessler, S; Vallet, C; Pöschl, U; Knauer, S K; Stauber, R H

2018-05-17

The applications of nanoparticles (NPs) are increasing exponentially in consumer products, biotechnology and biomedicine, and humans, as well as the environment, are increasingly being exposed to NPs. Analogously, various (pathogenic) microorganisms are present at all the major exposure and entry sites for NPs in the human body as well as in environmental habitats. However, the field has just started to explore the complex interplay between NPs and microbes and the (patho)biological consequences. Based on recent insights, herein, we critically reviewed the available knowledge about the interaction of NPs with microbes and the analytical investigations including the latest intravital imaging tools. We have commented on how the NPs' characteristics influence complex formation with microorganisms, presented the underlying physicochemical forces, and provided examples of how this knowledge can be used to rationally control the NP-microbe interaction. We concluded by discussing the role of the biomolecule corona in NP-microbe crosstalk and speculated the impact of NP-microbe complex formation on the (patho)biological outcome and fate of microbial pathogens. The presented insights will not only support the field in engineering NPs with improved anti-microbial activity but also stimulate research on the biomedical and toxicological relevance of nanomaterial-microbiome complex formation for the anthropocene in general.

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.

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

Ask not what physics can do for biology—ask what biology can do for physics

NASA Astrophysics Data System (ADS)

Frauenfelder, Hans

2014-10-01

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.

An individual-based modeling approach to simulating recreation use in wilderness settings

Treesearch

Randy Gimblett; Terry Daniel; Michael J. Meitner

2000-01-01

Landscapes protect biological diversity and provide unique opportunities for human-nature interactions. Too often, these desirable settings suffer from extremely high visitation. Given the complexity of social, environmental and economic interactions, resource managers need tools that provide insights into the cause and effect relationships between management actions...

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 advanced web query interface for biological databases

PubMed Central

Latendresse, Mario; Karp, Peter D.

2010-01-01

Although most web-based biological databases (DBs) offer some type of web-based form to allow users to author DB queries, these query forms are quite restricted in the complexity of DB queries that they can formulate. They can typically query only one DB, and can query only a single type of object at a time (e.g. genes) with no possible interaction between the objects—that is, in SQL parlance, no joins are allowed between DB objects. Writing precise queries against biological DBs is usually left to a programmer skillful enough in complex DB query languages like SQL. We present a web interface for building precise queries for biological DBs that can construct much more precise queries than most web-based query forms, yet that is user friendly enough to be used by biologists. It supports queries containing multiple conditions, and connecting multiple object types without using the join concept, which is unintuitive to biologists. This interactive web interface is called the Structured Advanced Query Page (SAQP). Users interactively build up a wide range of query constructs. Interactive documentation within the SAQP describes the schema of the queried DBs. The SAQP is based on BioVelo, a query language based on list comprehension. The SAQP is part of the Pathway Tools software and is available as part of several bioinformatics web sites powered by Pathway Tools, including the BioCyc.org site that contains more than 500 Pathway/Genome DBs. PMID:20624715

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.

Capturing cooperative interactions with the PSI-MI format

PubMed Central

Van Roey, Kim; Orchard, Sandra; Kerrien, Samuel; Dumousseau, Marine; Ricard-Blum, Sylvie; Hermjakob, Henning; Gibson, Toby J.

2013-01-01

The complex biological processes that control cellular function are mediated by intricate networks of molecular interactions. Accumulating evidence indicates that these interactions are often interdependent, thus acting cooperatively. Cooperative interactions are prevalent in and indispensible for reliable and robust control of cell regulation, as they underlie the conditional decision-making capability of large regulatory complexes. Despite an increased focus on experimental elucidation of the molecular details of cooperative binding events, as evidenced by their growing occurrence in literature, they are currently lacking from the main bioinformatics resources. One of the contributing factors to this deficiency is the lack of a computer-readable standard representation and exchange format for cooperative interaction data. To tackle this shortcoming, we added functionality to the widely used PSI-MI interchange format for molecular interaction data by defining new controlled vocabulary terms that allow annotation of different aspects of cooperativity without making structural changes to the underlying XML schema. As a result, we are able to capture cooperative interaction data in a structured format that is backward compatible with PSI-MI–based data and applications. This will facilitate the storage, exchange and analysis of cooperative interaction data, which in turn will advance experimental research on this fundamental principle in biology. Database URL: http://psi-mi-cooperativeinteractions.embl.de/ PMID:24067240

Linear control theory for gene network modeling.

PubMed

Shin, Yong-Jun; Bleris, Leonidas

2010-09-16

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

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.

Quality control methodology for high-throughput protein-protein interaction screening.

PubMed

Vazquez, Alexei; Rual, Jean-François; Venkatesan, Kavitha

2011-01-01

Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

Cox17 Protein Is an Auxiliary Factor Involved in the Control of the Mitochondrial Contact Site and Cristae Organizing System.

PubMed

Chojnacka, Magdalena; Gornicka, Agnieszka; Oeljeklaus, Silke; Warscheid, Bettina; Chacinska, Agnieszka

2015-06-12

The mitochondrial contact site and cristae organizing system (MICOS) is a recently discovered protein complex that is crucial for establishing and maintaining the proper inner membrane architecture and contacts with the outer membrane of mitochondria. The ways in which the MICOS complex is assembled and its integrity is regulated remain elusive. Here, we report a direct link between Cox17, a protein involved in the assembly of cytochrome c oxidase, and the MICOS complex. Cox17 interacts with Mic60, thereby modulating MICOS complex integrity. This interaction does not involve Sco1, a partner of Cox17 in transferring copper ions to cytochrome c oxidase. However, the Cox17-MICOS interaction is regulated by copper ions. We propose that Cox17 is a newly identified factor involved in maintaining the architecture of the MICOS complex. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Understanding the nanoparticle-protein corona complexes using computational and experimental methods.

PubMed

Kharazian, B; Hadipour, N L; Ejtehadi, M R

2016-06-01

Nanoparticles (NP) have capability to adsorb proteins from biological fluids and form protein layer, which is called protein corona. As the cell sees corona coated NPs, the protein corona can dictate biological response to NPs. The composition of protein corona is varied by physicochemical properties of NPs including size, shape, surface chemistry. Processing of protein adsorption is dynamic phenomena; to that end, a protein may desorb or leave a surface vacancy that is rapidly filled by another protein and cause changes in the corona composition mainly by the Vroman effect. In this review, we discuss the interaction between NP and proteins and the available techniques for identification of NP-bound proteins. Also we review current developed computational methods for understanding the NP-protein complex interactions. Copyright © 2016. Published by Elsevier Ltd.

The effects of intramolecular H-bond formation on the stability constant and water exchange rate of the Gd(III)-diethylenetriamine-N'-(3-amino-1,1-propylenephosphonic)-N, N,N'',N''-tetraacetate complex.

PubMed

Baranyai, Zsolt; Gianolio, Eliana; Ramalingam, Kondareddiar; Swenson, Rolf; Ranganathan, Ramachandran; Brücher, E; Aime, Silvio

2007-01-01

The binding interaction of metal chelates to biological macromolecules, though driven by properly devoted recognition synthons, may cause dramatic changes in some property associated with the coordination cage such as the thermodynamic stability or the exchange rate of the metal coordinated water. Such changes are due to electrostatic and H-bonding interactions involving atoms of the coordination cage and atoms of the biological molecule at the binding site. To mimic this type of H-bonding interactions, lanthanide(III) complexes with a DTPA-monophosphonate ligand bearing a propylamino moiety (H6NP-DTPA) were synthesized. Their thermodynamic stabilities and the exchange lifetime of the coordinated water molecule (for the Gd-complex) were compared with those of the analog complexes with DTPA and the parent DTPA-monophosphonate derivative (H6P-DTPA). It was found that the intramolecular H-bond between the epsilon-amino group and the phosphonate moiety in NP-DTPA complexes causes displacements of electric charges in their coordination cage that are markedly pH dependent. In turn, this affects the characteristic properties of the coordination cage. In particular it results in a marked elongation of the exchange lifetime of the coordinated water molecule. (c) 2007 John Wiley & Sons, Ltd.

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.

Network biology discovers pathogen contact points in host protein-protein interactomes.

PubMed

Ahmed, Hadia; Howton, T C; Sun, Yali; Weinberger, Natascha; Belkhadir, Youssef; Mukhtar, M Shahid

2018-06-13

In all organisms, major biological processes are controlled by complex protein-protein interactions networks (interactomes), yet their structural complexity presents major analytical challenges. Here, we integrate a compendium of over 4300 phenotypes with Arabidopsis interactome (AI-1 MAIN ). We show that nodes with high connectivity and betweenness are enriched and depleted in conditional and essential phenotypes, respectively. Such nodes are located in the innermost layers of AI-1 MAIN and are preferential targets of pathogen effectors. We extend these network-centric analyses to Cell Surface Interactome (CSI LRR ) and predict its 35 most influential nodes. To determine their biological relevance, we show that these proteins physically interact with pathogen effectors and modulate plant immunity. Overall, our findings contrast with centrality-lethality rule, discover fast information spreading nodes, and highlight the structural properties of pathogen targets in two different interactomes. Finally, this theoretical framework could possibly be applicable to other inter-species interactomes to reveal pathogen contact points.

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

PubMed Central

2012-01-01

Background Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges. Results To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. Conclusions We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes. PMID:23046740

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.

Elucidation of the binding mechanism of renin using a wide array of computational techniques and biological assays.

PubMed

Tzoupis, Haralambos; Leonis, Georgios; Avramopoulos, Aggelos; Reis, Heribert; Czyżnikowska, Żaneta; Zerva, Sofia; Vergadou, Niki; Peristeras, Loukas D; Papavasileiou, Konstantinos D; Alexis, Michael N; Mavromoustakos, Thomas; Papadopoulos, Manthos G

2015-11-01

We investigate the binding mechanism in renin complexes, involving three drugs (remikiren, zankiren and enalkiren) and one lead compound, which was selected after screening the ZINC database. For this purpose, we used ab initio methods (the effective fragment potential, the variational perturbation theory, the energy decomposition analysis, the atoms-in-molecules), docking, molecular dynamics, and the MM-PBSA method. A biological assay for the lead compound has been performed to validate the theoretical findings. Importantly, binding free energy calculations for the three drug complexes are within 3 kcal/mol of the experimental values, thus further justifying our computational protocol, which has been validated through previous studies on 11 drug-protein systems. The main elements of the discovered mechanism are: (i) minor changes are induced to renin upon drug binding, (ii) the three drugs form an extensive network of hydrogen bonds with renin, whilst the lead compound presented diminished interactions, (iii) ligand binding in all complexes is driven by favorable van der Waals interactions and the nonpolar contribution to solvation, while the lead compound is associated with diminished van der Waals interactions compared to the drug-bound forms of renin, and (iv) the environment (H2O/Na(+)) has a small effect on the renin-remikiren interaction. Copyright © 2015 Elsevier Inc. All rights reserved.

Biological system interactions.

PubMed Central

Adomian, G; Adomian, G E; Bellman, R E

1984-01-01

Mathematical modeling of cellular population growth, interconnected subsystems of the body, blood flow, and numerous other complex biological systems problems involves nonlinearities and generally randomness as well. Such problems have been dealt with by mathematical methods often changing the actual model to make it tractable. The method presented in this paper (and referenced works) allows much more physically realistic solutions. PMID:6585837

The diverse and expanding role of mass spectrometry in structural and molecular biology.

PubMed

Lössl, Philip; van de Waterbeemd, Michiel; Heck, Albert Jr

2016-12-15

The emergence of proteomics has led to major technological advances in mass spectrometry (MS). These advancements not only benefitted MS-based high-throughput proteomics but also increased the impact of mass spectrometry on the field of structural and molecular biology. Here, we review how state-of-the-art MS methods, including native MS, top-down protein sequencing, cross-linking-MS, and hydrogen-deuterium exchange-MS, nowadays enable the characterization of biomolecular structures, functions, and interactions. In particular, we focus on the role of mass spectrometry in integrated structural and molecular biology investigations of biological macromolecular complexes and cellular machineries, highlighting work on CRISPR-Cas systems and eukaryotic transcription complexes. © 2016 The Authors. Published under the terms of the CC BY NC ND 4.0 license.

Surfactant-cobalt(III) complexes: The impact of hydrophobicity on interaction with HSA and DNA - insights from experimental and theoretical approach.

PubMed

Veeralakshmi, Selvakumar; Sabapathi, Gopal; Nehru, Selvan; Venuvanalingam, Ponnambalam; Arunachalam, Sankaralingam

2017-05-01

To develop surfactant-based metallodrugs, it is very important to know about their hydrophobicity, micelle forming capacity, their interaction with biomacromolecules such as proteins and nucleic acids, and biological activities. Here, diethylenetriamine (dien) and tetradecylamine ligand (TA) based surfactant-cobalt(III) complexes with single chain domain, [Co(dien)(TA)Cl 2 ]ClO 4 (1) and double chain domain [Co(dien)(TA) 2 Cl](ClO 4 ) 2 (2) were chosen to study the effect of hydrophobicity on the interaction with human serum albumin and calf thymus DNA. The obtained results showed that (i) single chain surfactant-cobalt(III) complex (1) interact with HSA and DNA via electrostatic interaction and groove binding, respectively; (ii) double chain surfactant-cobalt(III) complex (2) interact with HSA and DNA via hydrophobic interaction and partial intercalation, respectively, due to the play of hydrophobicity by single and double chain domains. Further it is noted that, double chain surfactant-cobalt(III) complex interact strongly with HSA and DNA, compared single chain surfactant-cobalt(III) complex due to their more hydrophobicity nature. DFT and molecular docking studies offer insights into the mechanism and mode of binding towards the molecular target CT-DNA and HSA. Hence, the present findings will create new avenue towards the use of hydrophobic metallodrugs for various therapeutic applications. Copyright © 2017 Elsevier B.V. All rights reserved.

Rapid, Optimized Interactomic Screening

PubMed Central

Hakhverdyan, Zhanna; Domanski, Michal; Hough, Loren; Oroskar, Asha A.; Oroskar, Anil R.; Keegan, Sarah; Dilworth, David J.; Molloy, Kelly R.; Sherman, Vadim; Aitchison, John D.; Fenyö, David; Chait, Brian T.; Jensen, Torben Heick; Rout, Michael P.; LaCava, John

2015-01-01

We must reliably map the interactomes of cellular macromolecular complexes in order to fully explore and understand biological systems. However, there are no methods to accurately predict how to capture a given macromolecular complex with its physiological binding partners. Here, we present a screen that comprehensively explores the parameters affecting the stability of interactions in affinity-captured complexes, enabling the discovery of physiological binding partners and the elucidation of their functional interactions in unparalleled detail. We have implemented this screen on several macromolecular complexes from a variety of organisms, revealing novel profiles even for well-studied proteins. Our approach is robust, economical and automatable, providing an inroad to the rigorous, systematic dissection of cellular interactomes. PMID:25938370

Governability Framework for the Evaluation and Implementation of Complex Public Health Functions

ERIC Educational Resources Information Center

Varghese, Joe; Kutty, V. Raman

2012-01-01

Background: The dominant theoretical basis of our public health practice originates from a positivist or reductionist paradigm. It fails to take into account the complexity emerging out of public health's multiple influences originating from biological and social worlds. A deeper understanding of the interaction of elements that characterize the…

Meta-analysis reveals complex marine biological responses to the interactive effects of ocean acidification and warming

PubMed Central

Harvey, Ben P; Gwynn-Jones, Dylan; Moore, Pippa J

2013-01-01

Ocean acidification and warming are considered two of the greatest threats to marine biodiversity, yet the combined effect of these stressors on marine organisms remains largely unclear. Using a meta-analytical approach, we assessed the biological responses of marine organisms to the effects of ocean acidification and warming in isolation and combination. As expected biological responses varied across taxonomic groups, life-history stages, and trophic levels, but importantly, combining stressors generally exhibited a stronger biological (either positive or negative) effect. Using a subset of orthogonal studies, we show that four of five of the biological responses measured (calcification, photosynthesis, reproduction, and survival, but not growth) interacted synergistically when warming and acidification were combined. The observed synergisms between interacting stressors suggest that care must be made in making inferences from single-stressor studies. Our findings clearly have implications for the development of adaptive management strategies particularly given that the frequency of stressors interacting in marine systems will be likely to intensify in the future. There is now an urgent need to move toward more robust, holistic, and ecologically realistic climate change experiments that incorporate interactions. Without them accurate predictions about the likely deleterious impacts to marine biodiversity and ecosystem functioning over the next century will not be possible. PMID:23610641

Meta-analysis reveals complex marine biological responses to the interactive effects of ocean acidification and warming.

PubMed

Harvey, Ben P; Gwynn-Jones, Dylan; Moore, Pippa J

2013-04-01

Ocean acidification and warming are considered two of the greatest threats to marine biodiversity, yet the combined effect of these stressors on marine organisms remains largely unclear. Using a meta-analytical approach, we assessed the biological responses of marine organisms to the effects of ocean acidification and warming in isolation and combination. As expected biological responses varied across taxonomic groups, life-history stages, and trophic levels, but importantly, combining stressors generally exhibited a stronger biological (either positive or negative) effect. Using a subset of orthogonal studies, we show that four of five of the biological responses measured (calcification, photosynthesis, reproduction, and survival, but not growth) interacted synergistically when warming and acidification were combined. The observed synergisms between interacting stressors suggest that care must be made in making inferences from single-stressor studies. Our findings clearly have implications for the development of adaptive management strategies particularly given that the frequency of stressors interacting in marine systems will be likely to intensify in the future. There is now an urgent need to move toward more robust, holistic, and ecologically realistic climate change experiments that incorporate interactions. Without them accurate predictions about the likely deleterious impacts to marine biodiversity and ecosystem functioning over the next century will not be possible.

An integrative approach to inferring biologically meaningful gene modules

PubMed Central

2011-01-01

Background The ability to construct biologically meaningful gene networks and modules is critical for contemporary systems biology. Though recent studies have demonstrated the power of using gene modules to shed light on the functioning of complex biological systems, most modules in these networks have shown little association with meaningful biological function. We have devised a method which directly incorporates gene ontology (GO) annotation in construction of gene modules in order to gain better functional association. Results We have devised a method, Semantic Similarity-Integrated approach for Modularization (SSIM) that integrates various gene-gene pairwise similarity values, including information obtained from gene expression, protein-protein interactions and GO annotations, in the construction of modules using affinity propagation clustering. We demonstrated the performance of the proposed method using data from two complex biological responses: 1. the osmotic shock response in Saccharomyces cerevisiae, and 2. the prion-induced pathogenic mouse model. In comparison with two previously reported algorithms, modules identified by SSIM showed significantly stronger association with biological functions. Conclusions The incorporation of semantic similarity based on GO annotation with gene expression and protein-protein interaction data can greatly enhance the functional relevance of inferred gene modules. In addition, the SSIM approach can also reveal the hierarchical structure of gene modules to gain a broader functional view of the biological system. Hence, the proposed method can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level. PMID:21791051

The use of selective adsorbents in capillary electrophoresis-mass spectrometry for analyte preconcentration and microreactions: a powerful three-dimensional tool for multiple chemical and biological applications.

PubMed

Guzman, N A; Stubbs, R J

2001-10-01

Much attention has recently been directed to the development and application of online sample preconcentration and microreactions in capillary electrophoresis using selective adsorbents based on chemical or biological specificity. The basic principle involves two interacting chemical or biological systems with high selectivity and affinity for each other. These molecular interactions in nature usually involve noncovalent and reversible chemical processes. Properly bound to a solid support, an "affinity ligand" can selectively adsorb a "target analyte" found in a simple or complex mixture at a wide range of concentrations. As a result, the isolated analyte is enriched and highly purified. When this affinity technique, allowing noncovalent chemical interactions and biochemical reactions to occur, is coupled on-line to high-resolution capillary electrophoresis and mass spectrometry, a powerful tool of chemical and biological information is created. This paper describes the concept of biological recognition and affinity interaction on-line with high-resolution separation, the fabrication of an "analyte concentrator-microreactor", optimization conditions of adsorption and desorption, the coupling to mass spectrometry, and various applications of clinical and pharmaceutical interest.

Exploring pathway interactions in insulin resistant mouse liver

PubMed Central

2011-01-01

Background Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset. Results We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar. Conclusions Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well. PMID:21843341

Structural Analysis of N- and O-glycans Using ZIC-HILIC/Dialysis Coupled to NMR Detection

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

Qu, Yi; Feng, Ju; Deng, Shuang

2014-11-19

Protein glycosylation, an important and complex post-translational modification (PTM), is involved in various biological processes including the receptor-ligand and cell-cell interaction, and plays a crucial role in many biological functions. However, little is known about the glycan structures of important biological complex samples, and the conventional glycan enrichment strategy (i.e., size-exclusion column [SEC] separation,) prior to nuclear magnetic resonance (NMR) detection is time-consuming and tedious. In this study, we employed SEC, Zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC), and ZIC-HILIC coupled with dialysis strategies to enrich the glycopeptides from the pronase E digests of RNase B, followed by NMR analysis ofmore » the glycoconjugate. Our results suggest that the ZIC-HILIC enrichment coupled with dialysis is the most efficient, which was thus applied to the analysis of biological complex sample, the pronase E digest of the secreted proteins from the fungi Aspergillus niger. The NMR spectra revealed that the secreted proteins from A. niger contain both N-linked glycans with a high-mannose core and O-linked glycans bearing mannose and glucose with 1->3 and 1->6 linkages. In all, our study provides compelling evidence that ZIC-HILIC separation coupled to dialysis is superior to the commonly used SEC separation to prepare glycopeptides for the downstream NMR analysis, which could greatly facilitate the future NMR-based glycoproteomics research.« less

Annual Research Review: Developmental Considerations of Gene by Environment Interactions

ERIC Educational Resources Information Center

Lenroot, Rhoshel K.; Giedd, Jay N.

2011-01-01

Biological development is driven by a complex dance between nurture and nature, determined not only by the specific features of the interacting genetic and environmental influences but also by the timing of their rendezvous. The initiation of large-scale longitudinal studies, ever-expanding knowledge of genetics, and increasing availability of…

Understanding Protein Synthesis: An Interactive Card Game Discussion

ERIC Educational Resources Information Center

Lewis, Alison; Peat, Mary; Franklin, Sue

2005-01-01

Protein synthesis is a complex process and students find it difficult to understand. This article describes an interactive discussion "game" used by first year biology students at the University of Sydney. The students, in small groups, use the game in which the processes of protein synthesis are actioned by the students during a…

Platform construction and extraction mechanism study of magnetic mixed hemimicelles solid-phase extraction

PubMed Central

Xiao, Deli; Zhang, Chan; He, Jia; Zeng, Rong; Chen, Rong; He, Hua

2016-01-01

Simple, accurate and high-throughput pretreatment method would facilitate large-scale studies of trace analysis in complex samples. Magnetic mixed hemimicelles solid-phase extraction has the power to become a key pretreatment method in biological, environmental and clinical research. However, lacking of experimental predictability and unsharpness of extraction mechanism limit the development of this promising method. Herein, this work tries to establish theoretical-based experimental designs for extraction of trace analytes from complex samples using magnetic mixed hemimicelles solid-phase extraction. We selected three categories and six sub-types of compounds for systematic comparative study of extraction mechanism, and comprehensively illustrated the roles of different force (hydrophobic interaction, π-π stacking interactions, hydrogen-bonding interaction, electrostatic interaction) for the first time. What’s more, the application guidelines for supporting materials, surfactants and sample matrix were also summarized. The extraction mechanism and platform established in the study render its future promising for foreseeable and efficient pretreatment under theoretical based experimental design for trace analytes from environmental, biological and clinical samples. PMID:27924944

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks.

PubMed

Havugimana, Pierre C; Hu, Pingzhao; Emili, Andrew

2017-10-01

Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'. These include mass spectrometry-based methods to characterize macromolecular complexes on a global-scale and sophisticated data analysis tools - most notably machine learning - that allow for the generation of high-quality protein association maps. Expert commentary: Here, we recount some key lessons learned, with an emphasis on successful workflows, and challenges, arising from our own and other groups' ongoing efforts to generate, interpret and report proteome-scale interaction networks in increasingly diverse biological contexts.

Systems biology approach to bioremediation

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

Chakraborty, Romy; Wu, Cindy H.; Hazen, Terry C.

2012-06-01

Bioremediation has historically been approached as a ‘black box’ in terms of our fundamental understanding. Thus it succeeds and fails, seldom without a complete understanding of why. Systems biology is an integrated research approach to study complex biological systems, by investigating interactions and networks at the molecular, cellular, community, and ecosystem level. The knowledge of these interactions within individual components is fundamental to understanding the dynamics of the ecosystem under investigation. Finally, understanding and modeling functional microbial community structure and stress responses in environments at all levels have tremendous implications for our fundamental understanding of hydrobiogeochemical processes and the potentialmore » for making bioremediation breakthroughs and illuminating the ‘black box’.« less

Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network.

PubMed

Cao, Buwen; Deng, Shuguang; Qin, Hua; Ding, Pingjian; Chen, Shaopeng; Li, Guanghui

2018-06-15

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein⁻protein interaction (PPI) networks. In this study, based on penalized matrix decomposition ( PMD ), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMD pc ) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMD pc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).

Relevance of protein-protein interactions on the biological identity of nanoparticles.

PubMed

Vasti, Cecilia; Bonnet, Laura V; Galiano, Mauricio R; Rojas, Ricardo; Giacomelli, Carla E

2018-06-01

Considering that the use of nanoparticles (NPs) as carriers of therapeutic or theranostic agents has increased in the last years, it is mandatory to understand the interaction between NPs and living systems. In contact with biological fluids, the NPs (synthetic identity) are covered with biomolecules that form a protein corona, which defines the biological identity. It is well known that the protein corona formation is mediated by non-specific physical interactions, but protein-protein interactions (PPI), involving specific recognition sites of the polypeptides, are also involved. This work explores the relationship between the synthetic and biological identities of layered double hydroxides nanoparticles (LDH-NPs) and the effect of the protein corona on the cellular response. With such a purpose, the synthetic identity was modified by coating LDH-NPs with either a single protein or a complex mixture of them, followed by the characterization of the protein corona formed in a commonly used cell culture medium. A proteomic approach was used to identify the protein corona molecules and the PPI network was constructed with a novel bioinformatic tool. The coating on LDH-NPs defines the biological identity in such a way that the composition of the protein corona as well as PPI are changed. Electrostatic interactions appear not to be the only driving force regulating the interactions between NPs, proteins and cells since the specific recognition also play a fundamental role. However, the biological identity of LDH-NPs does not affect the interactions with cells that shows negligible cytotoxicity and high internalization levels. Copyright © 2018 Elsevier B.V. All rights reserved.

Applications of systems approaches in the study of rheumatic diseases.

PubMed

Kim, Ki-Jo; Lee, Saseong; Kim, Wan-Uk

2015-03-01

The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.

Dynamic interactions between a membrane binding protein and lipids induce fluctuating diffusivity

PubMed Central

Yamamoto, Eiji; Akimoto, Takuma; Kalli, Antreas C.; Yasuoka, Kenji; Sansom, Mark S. P.

2017-01-01

Pleckstrin homology (PH) domains are membrane-binding lipid recognition proteins that interact with phosphatidylinositol phosphate (PIP) molecules in eukaryotic cell membranes. Diffusion of PH domains plays a critical role in biological reactions on membrane surfaces. Although diffusivity can be estimated by long-time measurements, it lacks information on the short-time diffusive nature. We reveal two diffusive properties of a PH domain bound to the surface of a PIP-containing membrane using molecular dynamics simulations. One is fractional Brownian motion, attributed to the motion of the lipids with which the PH domain interacts. The other is temporally fluctuating diffusivity; that is, the short-time diffusivity of the bound protein changes substantially with time. Moreover, the diffusivity for short-time measurements is intrinsically different from that for long-time measurements. This fluctuating diffusivity results from dynamic changes in interactions between the PH domain and PIP molecules. Our results provide evidence that the complexity of protein-lipid interactions plays a crucial role in the diffusion of proteins on biological membrane surfaces. Changes in the diffusivity of PH domains and related membrane-bound proteins may in turn contribute to the formation/dissolution of protein complexes in membranes. PMID:28116358

Leveraging Modeling Approaches: Reaction Networks and Rules

PubMed Central

Blinov, Michael L.; Moraru, Ion I.

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high resolution and/or high throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatio-temporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks – the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks. PMID:22161349

Leveraging modeling approaches: reaction networks and rules.

PubMed

Blinov, Michael L; Moraru, Ion I

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high-resolution and/or high-throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatiotemporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks - the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.

The Integrin Receptor in Biologically Relevant Bilayers: Insights from Molecular Dynamics Simulations.

PubMed

Kalli, Antreas C; Rog, Tomasz; Vattulainen, Ilpo; Campbell, Iain D; Sansom, Mark S P

2017-08-01

Integrins are heterodimeric (αβ) cell surface receptors that are potential therapeutic targets for a number of diseases. Despite the existence of structural data for all parts of integrins, the structure of the complete integrin receptor is still not available. We have used available structural data to construct a model of the complete integrin receptor in complex with talin F2-F3 domain. It has been shown that the interactions of integrins with their lipid environment are crucial for their function but details of the integrin/lipid interactions remain elusive. In this study an integrin/talin complex was inserted in biologically relevant bilayers that resemble the cell plasma membrane containing zwitterionic and charged phospholipids, cholesterol and sphingolipids to study the dynamics of the integrin receptor and its effect on bilayer structure and dynamics. The results of this study demonstrate the dynamic nature of the integrin receptor and suggest that the presence of the integrin receptor alters the lipid organization between the two leaflets of the bilayer. In particular, our results suggest elevated density of cholesterol and of phosphatidylserine lipids around the integrin/talin complex and a slowing down of lipids in an annulus of ~30 Å around the protein due to interactions between the lipids and the integrin/talin F2-F3 complex. This may in part regulate the interactions of integrins with other related proteins or integrin clustering thus facilitating signal transduction across cell membranes.

Getting a Grip on Complexes

PubMed Central

Nie, Yan; Viola, Cristina; Bieniossek, Christoph; Trowitzsch, Simon; Vijay-achandran, Lakshmi Sumitra; Chaillet, Maxime; Garzoni, Frederic; Berger, Imre

2009-01-01

We are witnessing tremendous advances in our understanding of the organization of life. Complete genomes are being deciphered with ever increasing speed and accuracy, thereby setting the stage for addressing the entire gene product repertoire of cells, towards understanding whole biological systems. Advances in bioinformatics and mass spectrometric techniques have revealed the multitude of interactions present in the proteome. Multiprotein complexes are emerging as a paramount cornerstone of biological activity, as many proteins appear to participate, stably or transiently, in large multisubunit assemblies. Analysis of the architecture of these assemblies and their manifold interactions is imperative for understanding their function at the molecular level. Structural genomics efforts have fostered the development of many technologies towards achieving the throughput required for studying system-wide single proteins and small interaction motifs at high resolution. The present shift in focus towards large multiprotein complexes, in particular in eukaryotes, now calls for a likewise concerted effort to develop and provide new technologies that are urgently required to produce in quality and quantity the plethora of multiprotein assemblies that form the complexome, and to routinely study their structure and function at the molecular level. Current efforts towards this objective are summarized and reviewed in this contribution. PMID:20514218

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.

Interactive learning and action: realizing the promise of synthetic biology for global health.

PubMed

Betten, A Wieke; Roelofsen, Anneloes; Broerse, Jacqueline E W

2013-09-01

The emerging field of synthetic biology has the potential to improve global health. For example, synthetic biology could contribute to efforts at vaccine development in a context in which vaccines and immunization have been identified by the international community as being crucial to international development efforts and, in particular, the millennium development goals. However, past experience with innovations shows that realizing a technology's potential can be difficult and complex. To achieve better societal embedding of synthetic biology and to make sure it reaches its potential, science and technology development should be made more inclusive and interactive. Responsible research and innovation is based on the premise that a broad range of stakeholders with different views, needs and ideas should have a voice in the technological development and deployment process. The interactive learning and action (ILA) approach has been developed as a methodology to bring societal stakeholders into a science and technology development process. This paper proposes an ILA in five phases for an international effort, with national case studies, to develop socially robust applications of synthetic biology for global health, based on the example of vaccine development. The design is based on results of a recently initiated ILA project on synthetic biology; results from other interactive initiatives described in the literature; and examples of possible applications of synthetic biology for global health that are currently being developed.

Lateral organization of biological membranes: role of long-range interactions.

PubMed

Duneau, Jean-Pierre; Sturgis, James N

2013-12-01

The lateral organization of biological membranes is of great importance in many biological processes, both for the formation of specific structures such as super-complexes and for function as observed in signal transduction systems. Over the last years, AFM studies, particularly of bacterial photosynthetic membranes, have revealed that certain proteins are able to segregate into functional domains with a specific organization. Furthermore, the extended non-random nature of the organization has been suggested to be important for the energy and redox transport properties of these specialized membranes. In the work reported here, using a coarse-grained Monte Carlo approach, we have investigated the nature of interaction potentials able to drive the formation and segregation of specialized membrane domains from the rest of the membrane and furthermore how the internal organization of the segregated domains can be modulated by the interaction potentials. These simulations show that long-range interactions are necessary to allow formation of membrane domains of realistic structure. We suggest that such possibly non-specific interactions may be of great importance in the lateral organization of biological membranes in general and in photosynthetic systems in particular. Finally, we consider the possible molecular origins of such interactions and suggest a fundamental role for lipid-mediated interactions in driving the formation of specialized photosynthetic membrane domains. We call these lipid-mediated interactions a 'lipophobic effect.'

Biological interaction of living cells with COSAN-based synthetic vesicles

PubMed Central

Tarrés, Màrius; Canetta, Elisabetta; Paul, Eleanor; Forbes, Jordan; Azzouni, Karima; Viñas, Clara; Teixidor, Francesc; Harwood, Adrian J.

2015-01-01

Cobaltabisdicarbollide (COSAN) [3,3′-Co(1,2-C2B9H11)2]−, is a complex boron-based anion that has the unusual property of self-assembly into membranes and vesicles. These membranes have similar dimensions to biological membranes found in cells, and previously COSAN has been shown to pass through synthetic lipid membranes and those of living cells without causing breakdown of membrane barrier properties. Here, we investigate the interaction of this inorganic membrane system with living cells. We show that COSAN has no immediate effect on cell viability, and cells fully recover when COSAN is removed following exposure for hours to days. COSAN elicits a range of cell biological effects, including altered cell morphology, inhibition of cell growth and, in some cases, apoptosis. These observations reveal a new biology at the interface between inorganic, synthetic COSAN membranes and naturally occurring biological membranes. PMID:25588708

Biological interaction of living cells with COSAN-based synthetic vesicles.

PubMed

Tarrés, Màrius; Canetta, Elisabetta; Paul, Eleanor; Forbes, Jordan; Azzouni, Karima; Viñas, Clara; Teixidor, Francesc; Harwood, Adrian J

2015-01-15

Cobaltabisdicarbollide (COSAN) [3,3'-Co(1,2-C2B9H11)2](-), is a complex boron-based anion that has the unusual property of self-assembly into membranes and vesicles. These membranes have similar dimensions to biological membranes found in cells, and previously COSAN has been shown to pass through synthetic lipid membranes and those of living cells without causing breakdown of membrane barrier properties. Here, we investigate the interaction of this inorganic membrane system with living cells. We show that COSAN has no immediate effect on cell viability, and cells fully recover when COSAN is removed following exposure for hours to days. COSAN elicits a range of cell biological effects, including altered cell morphology, inhibition of cell growth and, in some cases, apoptosis. These observations reveal a new biology at the interface between inorganic, synthetic COSAN membranes and naturally occurring biological membranes.

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.

MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

PubMed

Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

2012-01-01

Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

RNA and RNP as Building Blocks for Nanotechnology and Synthetic Biology.

PubMed

Ohno, Hirohisa; Saito, Hirohide

2016-01-01

Recent technologies that aimed to elucidate cellular function have revealed essential roles for RNA molecules in living systems. Our knowledge concerning functional and structural information of naturally occurring RNA and RNA-protein (RNP) complexes is increasing rapidly. RNA and RNP interaction motifs are structural units that function as building blocks to constitute variety of complex structures. RNA-central synthetic biology and nanotechnology are constructive approaches that employ the accumulated information and build synthetic RNA (RNP)-based circuits and nanostructures. Here, we describe how to design and construct synthetic RNA (RNP)-based devices and structures at the nanometer-scale for biological and future therapeutic applications. RNA/RNP nanostructures can also be utilized as the molecular scaffold to control the localization or interactions of target molecule(s). Moreover, RNA motifs recognized by RNA-binding proteins can be applied to make protein-responsive translational "switches" that can turn gene expression "on" or "off" depending on the intracellular environment. This "synthetic RNA and RNP world" will expand tools for nanotechnology and synthetic biology. In addition, these reconstructive approaches would lead to a greater understanding of building principle in naturally occurring RNA/RNP molecules and systems. Copyright © 2016 Elsevier Inc. All rights reserved.

Quantification of Degeneracy in Biological Systems for Characterization of Functional Interactions Between Modules

PubMed Central

Li, Yao; Dwivedi, Gaurav; Huang, Wen; Yi, Yingfei

2012-01-01

There is an evolutionary advantage in having multiple components with overlapping functionality (i.e degeneracy) in organisms. While theoretical considerations of degeneracy have been well established in neural networks using information theory, the same concepts have not been developed for differential systems, which form the basis of many biochemical reaction network descriptions in systems biology. Here we establish mathematical definitions of degeneracy, complexity and robustness that allow for the quantification of these properties in a system. By exciting a dynamical system with noise, the mutual information associated with a selected observable output and the interacting subspaces of input components can be used to define both complexity and degeneracy. The calculation of degeneracy in a biological network is a useful metric for evaluating features such as the sensitivity of a biological network to environmental evolutionary pressure. Using a two-receptor signal transduction network, we find that redundant components will not yield high degeneracy whereas compensatory mechanisms established by pathway crosstalk will. This form of analysis permits interrogation of large-scale differential systems for non-identical, functionally equivalent features that have evolved to maintain homeostasis during disruption of individual components. PMID:22619750

Novel insights into an old disease: recent developments in scabies mite biology.

PubMed

Holt, Deborah C; Fischer, Katja

2013-04-01

Scabies is a serious disease of both humans and other animals caused by infestation of the skin with the ectoparasitic mite Sarcoptes scabiei. Our current understanding of scabies mite biology and disease processes is far outweighed by the significant, worldwide impact of the disease. This review summarizes the recent data which furthers our knowledge of mite biology, host specificity and parasite host evasion mechanisms. Recent data concords with the previous work demonstrating limited gene flow between different host-associated populations of scabies mites. This evidence of the host specificity of scabies mites has important implications for disease control programmes. Other studies have begun to decipher the molecular basis of the complex host-parasite interactions underlying scabies infestations. Scabies mites have developed complex mechanisms to interfere with the host defence processes that may also enhance the survival of the associated skin microbiome, consistent with the epidemiological evidence. Recently developed natural host models of scabies are valuable tools to further study the disease processes and to trial novel therapeutic agents. Although significant progress has been made, further research is needed to understand the biology, host-parasite interactions and pathogenesis of this ubiquitous parasite.

Hydrophobic Interaction Chromatography for Bottom-Up Proteomics Analysis of Single Proteins and Protein Complexes.

PubMed

Rackiewicz, Michal; Große-Hovest, Ludger; Alpert, Andrew J; Zarei, Mostafa; Dengjel, Jörn

2017-06-02

Hydrophobic interaction chromatography (HIC) is a robust standard analytical method to purify proteins while preserving their biological activity. It is widely used to study post-translational modifications of proteins and drug-protein interactions. In the current manuscript we employed HIC to separate proteins, followed by bottom-up LC-MS/MS experiments. We used this approach to fractionate antibody species followed by comprehensive peptide mapping as well as to study protein complexes in human cells. HIC-reversed-phase chromatography (RPC)-mass spectrometry (MS) is a powerful alternative to fractionate proteins for bottom-up proteomics experiments making use of their distinct hydrophobic properties.

Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

PubMed Central

2010-01-01

Background Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. Results We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture. Conclusions We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to develop their own GPU implementations, and encourage others to implement their modeling methods on the GPU and to make that code available to the wider community. PMID:20696053

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases.

PubMed

Moulos, Panagiotis; Klein, Julie; Jupp, Simon; Stevens, Robert; Bascands, Jean-Loup; Schanstra, Joost P

2013-07-24

Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

PubMed Central

2013-01-01

Background Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. Results In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. Conclusions The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner. PMID:23883183

Nickel-quinolones interaction. Part 4. Structure and biological evaluation of nickel(II)-enrofloxacin complexes compared to zinc(II) analogues.

PubMed

Skyrianou, Kalliopi C; Psycharis, Vassilis; Raptopoulou, Catherine P; Kessissoglou, Dimitris P; Psomas, George

2011-01-01

The nickel(II) complexes with the second-generation quinolone antibacterial agent enrofloxacin in the presence or absence of the nitrogen-donor heterocyclic ligands 1,10-phenanthroline, 2,2'-bipyridine or pyridine have been synthesized and characterized. Enrofloxacin acts as bidentate ligand coordinated to Ni(II) ion through the ketone oxygen and a carboxylato oxygen. The crystal structure of (1,10-phenanthroline)bis(enrofloxacinato)nickel(II) has been determined by X-ray crystallography. UV study of the interaction of the complexes with calf-thymus DNA (CT DNA) has shown that they bind to CT DNA and bis(pyridine)bis(enrofloxacinato)nickel(II) exhibits the highest binding constant to CT DNA. The cyclic voltammograms of the complexes have shown that in the presence of CT DNA the complexes can bind to CT DNA by the intercalative binding mode which has also been verified by DNA solution viscosity measurements. Competitive study with ethidium bromide (EB) has shown that the complexes can displace the DNA-bound EB indicating that they bind to DNA in strong competition with EB. The complexes exhibit good binding propensity to human or bovine serum albumin protein having relatively high binding constant values. The biological properties of the complexes have been evaluated in comparison to the corresponding Zn(II) enrofloxacinato complexes as well as Ni(II) complexes with the first-generation quinolone oxolinic acid. Copyright © 2010 Elsevier Inc. All rights reserved.

Toward self-organization and complex matter.

PubMed

Lehn, Jean-Marie

2002-03-29

Beyond molecular chemistry based on the covalent bond, supramolecular chemistry aims at developing highly complex chemical systems from components interacting through noncovalent intermolecular forces. Over the past quarter century, supramolecular chemistry has grown into a major field and has fueled numerous developments at the interfaces with biology and physics. Some of the conceptual advances and future challenges are profiled here.

Microfluidics-Based in Vivo Mimetic Systems for the Study of Cellular Biology

PubMed Central

2015-01-01

Conspectus The human body is a complex network of molecules, organelles, cells, tissues, and organs: an uncountable number of interactions and transformations interconnect all the system’s components. In addition to these biochemical components, biophysical components, such as pressure, flow, and morphology, and the location of all of these interactions play an important role in the human body. Technical difficulties have frequently limited researchers from observing cellular biology as it occurs within the human body, but some state-of-the-art analytical techniques have revealed distinct cellular behaviors that occur only in the context of the interactions. These types of findings have inspired bioanalytical chemists to provide new tools to better understand these cellular behaviors and interactions. What blocks us from understanding critical biological interactions in the human body? Conventional approaches are often too naïve to provide realistic data and in vivo whole animal studies give complex results that may or may not be relevant for humans. Microfluidics offers an opportunity to bridge these two extremes: while these studies will not model the complexity of the in vivo human system, they can control the complexity so researchers can examine critical factors of interest carefully and quantitatively. In addition, the use of human cells, such as cells isolated from donated blood, captures human-relevant data and limits the use of animals in research. In addition, researchers can adapt these systems easily and cost-effectively to a variety of high-end signal transduction mechanisms, facilitating high-throughput studies that are also spatially, temporally, or chemically resolved. These strengths should allow microfluidic platforms to reveal critical parameters in the human body and provide insights that will help with the translation of pharmacological advances to clinical trials. In this Account, we describe selected microfluidic innovations within the last 5 years that focus on modeling both biophysical and biochemical interactions in cellular communication, such as flow and cell–cell networks. We also describe more advanced systems that mimic higher level biological networks, such as organ on-a-chip and animal on-a-chip models. Since the first papers in the early 1990s, interest in the bioanalytical use of microfluidics has grown significantly. Advances in micro-/nanofabrication technology have allowed researchers to produce miniaturized, biocompatible assay platforms suitable for microfluidic studies in biochemistry and chemical biology. Well-designed microfluidic platforms can achieve quick, in vitro analyses on pico- and femtoliter volume samples that are temporally, spatially, and chemically resolved. In addition, controlled cell culture techniques using a microfluidic platform have produced biomimetic systems that allow researchers to replicate and monitor physiological interactions. Pioneering work has successfully created cell–fluid, cell–cell, cell–tissue, tissue–tissue, even organ-like level interfaces. Researchers have monitored cellular behaviors in these biomimetic microfluidic environments, producing validated model systems to understand human pathophysiology and to support the development of new therapeutics. PMID:24555566

Microfluidics-based in vivo mimetic systems for the study of cellular biology.

PubMed

Kim, Donghyuk; Wu, Xiaojie; Young, Ashlyn T; Haynes, Christy L

2014-04-15

The human body is a complex network of molecules, organelles, cells, tissues, and organs: an uncountable number of interactions and transformations interconnect all the system's components. In addition to these biochemical components, biophysical components, such as pressure, flow, and morphology, and the location of all of these interactions play an important role in the human body. Technical difficulties have frequently limited researchers from observing cellular biology as it occurs within the human body, but some state-of-the-art analytical techniques have revealed distinct cellular behaviors that occur only in the context of the interactions. These types of findings have inspired bioanalytical chemists to provide new tools to better understand these cellular behaviors and interactions. What blocks us from understanding critical biological interactions in the human body? Conventional approaches are often too naïve to provide realistic data and in vivo whole animal studies give complex results that may or may not be relevant for humans. Microfluidics offers an opportunity to bridge these two extremes: while these studies will not model the complexity of the in vivo human system, they can control the complexity so researchers can examine critical factors of interest carefully and quantitatively. In addition, the use of human cells, such as cells isolated from donated blood, captures human-relevant data and limits the use of animals in research. In addition, researchers can adapt these systems easily and cost-effectively to a variety of high-end signal transduction mechanisms, facilitating high-throughput studies that are also spatially, temporally, or chemically resolved. These strengths should allow microfluidic platforms to reveal critical parameters in the human body and provide insights that will help with the translation of pharmacological advances to clinical trials. In this Account, we describe selected microfluidic innovations within the last 5 years that focus on modeling both biophysical and biochemical interactions in cellular communication, such as flow and cell-cell networks. We also describe more advanced systems that mimic higher level biological networks, such as organ on-a-chip and animal on-a-chip models. Since the first papers in the early 1990s, interest in the bioanalytical use of microfluidics has grown significantly. Advances in micro-/nanofabrication technology have allowed researchers to produce miniaturized, biocompatible assay platforms suitable for microfluidic studies in biochemistry and chemical biology. Well-designed microfluidic platforms can achieve quick, in vitro analyses on pico- and femtoliter volume samples that are temporally, spatially, and chemically resolved. In addition, controlled cell culture techniques using a microfluidic platform have produced biomimetic systems that allow researchers to replicate and monitor physiological interactions. Pioneering work has successfully created cell-fluid, cell-cell, cell-tissue, tissue-tissue, even organ-like level interfaces. Researchers have monitored cellular behaviors in these biomimetic microfluidic environments, producing validated model systems to understand human pathophysiology and to support the development of new therapeutics.

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

Complexation of C60 fullerene with aromatic drugs.

PubMed

Evstigneev, Maxim P; Buchelnikov, Anatoly S; Voronin, Dmitry P; Rubin, Yuriy V; Belous, Leonid F; Prylutskyy, Yuriy I; Ritter, Uwe

2013-02-25

The contributions of various physical factors to the energetics of complexation of aromatic drug molecules with C(60) fullerene are investigated in terms of the calculated magnitudes of equilibrium complexation constants and the components of the net Gibbs free energy. Models of complexation are developed taking into account the polydisperse nature of fullerene solutions in terms of the continuous or discrete (fractal) aggregation of C(60) molecules. Analysis of the energetics has shown that stabilization of the ligand-fullerene complexes in aqueous solution is mainly determined by intermolecular van der Waals interactions and, to lesser extent, by hydrophobic interactions. The results provide a physicochemical basis for a potentially new biotechnological application of fullerenes as modulators of biological activity of aromatic drugs. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries.

PubMed

Drawert, Brian; Engblom, Stefan; Hellander, Andreas

2012-06-22

Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling. We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development. In this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www.urdme.org.

Differential in Vitro Biological Action, Coregulator Interactions, and Molecular Dynamic Analysis of Bisphenol A (BPA), BPAF, and BPS Ligand-ERα Complexes.

PubMed

Li, Yin; Perera, Lalith; Coons, Laurel A; Burns, Katherine A; Tyler Ramsey, J; Pelch, Katherine E; Houtman, René; van Beuningen, Rinie; Teng, Christina T; Korach, Kenneth S

2018-01-31

Bisphenol A (BPA) is an endocrine-disrupting chemical (EDC) that might be harmful to human health. Recently, there has been widespread usage of bisphenol chemicals (BPs), such as bisphenol AF (BPAF) and bisphenol S (BPS), as replacements for BPA. However, the potential biological actions, toxicity, and the molecular mechanism of these compounds are still poorly understood. Our objective was to examine the estrogenic effects of BPA, BPAF, and BPS and the molecular mechanisms of action in the estrogen receptor alpha (ERα) complex. In vitro cell models were used to compare the estrogenic effects of BPA, BPAF, and BPS to estrogen. Microarray Assay for Real-Time Coregulator-Nuclear receptor Interaction (MARCoNI) analysis was used to identify coregulators of BPA, BPAF, and BPS, and molecular dynamic (MD) simulations were used to determine the compounds binding in the ERα complex. We demonstrated that BPA and BPAF have agonistic activity for both ERα and ERβ, but BPS has ERα-selective specificity. We concluded that coregulators were differentially recruited in the presence of BPA, BPAF, or BPS. Interestingly, BPS recruited more corepressors when compared to BPA and BPAF. From a series of MD analysis, we concluded that BPA, BPAF, and BPS can bind to the ER-ligand-binding domain with differing energetics and conformations. In addition, the binding surface of coregulator interactions on ERα was characterized for the BPA, BPAF, and BPS complexes. These findings further our understanding of the molecular mechanisms of EDCs, such as BPs, in ER-mediated transcriptional activation, biological activity, and their effects on physiological functions in human health. https://doi.org/10.1289/EHP2505.

Leveraging advances in biology to design biomaterials

NASA Astrophysics Data System (ADS)

Darnell, Max; Mooney, David J.

2017-12-01

Biomaterials have dramatically increased in functionality and complexity, allowing unprecedented control over the cells that interact with them. From these engineering advances arises the prospect of improved biomaterial-based therapies, yet practical constraints favour simplicity. Tools from the biology community are enabling high-resolution and high-throughput bioassays that, if incorporated into a biomaterial design framework, could help achieve unprecedented functionality while minimizing the complexity of designs by identifying the most important material parameters and biological outputs. However, to avoid data explosions and to effectively match the information content of an assay with the goal of the experiment, material screens and bioassays must be arranged in specific ways. By borrowing methods to design experiments and workflows from the bioprocess engineering community, we outline a framework for the incorporation of next-generation bioassays into biomaterials design to effectively optimize function while minimizing complexity. This framework can inspire biomaterials designs that maximize functionality and translatability.

Life under the Microscope: Single-Molecule Fluorescence Highlights the RNA World.

PubMed

Ray, Sujay; Widom, Julia R; Walter, Nils G

2018-04-25

The emergence of single-molecule (SM) fluorescence techniques has opened up a vast new toolbox for exploring the molecular basis of life. The ability to monitor individual biomolecules in real time enables complex, dynamic folding pathways to be interrogated without the averaging effect of ensemble measurements. In parallel, modern biology has been revolutionized by our emerging understanding of the many functions of RNA. In this comprehensive review, we survey SM fluorescence approaches and discuss how the application of these tools to RNA and RNA-containing macromolecular complexes in vitro has yielded significant insights into the underlying biology. Topics covered include the three-dimensional folding landscapes of a plethora of isolated RNA molecules, their assembly and interactions in RNA-protein complexes, and the relation of these properties to their biological functions. In all of these examples, the use of SM fluorescence methods has revealed critical information beyond the reach of ensemble averages.

DOT2: Macromolecular Docking With Improved Biophysical Models

PubMed Central

Roberts, Victoria A.; Thompson, Elaine E.; Pique, Michael E.; Perez, Martin S.; Eyck, Lynn Ten

2015-01-01

Computational docking is a useful tool for predicting macromolecular complexes, which are often difficult to determine experimentally. Here we present the DOT2 software suite, an updated version of the DOT intermolecular docking program. DOT2 provides straightforward, automated construction of improved biophysical models based on molecular coordinates, offering checkpoints that guide the user to include critical features. DOT has been updated to run more quickly, allow flexibility in grid size and spacing, and generate a complete list of favorable candidate configu-rations. Output can be filtered by experimental data and rescored by the sum of electrostatic and atomic desolvation energies. We show that this rescoring method improves the ranking of correct complexes for a wide range of macromolecular interactions, and demonstrate that biologically relevant models are essential for biologically relevant results. The flexibility and versatility of DOT2 accommodate realistic models of complex biological systems, improving the likelihood of a successful docking outcome. PMID:23695987

A benchmark-multi-disciplinary study of the interaction between the Chesapeake Bay and adjacent waters of the Virginian Sea

NASA Technical Reports Server (NTRS)

Hargis, W. J., Jr.

1981-01-01

The social and economic importance of estuaries are discussed. Major focus is on the Chesapeake Bay and its interaction with the adjacent waters of the Virginia Sea. Associated multiple use development and management problems as well as their internal physical, geological, chemical, and biological complexities are described.

Advance Planning Briefing for Industry. Technology Requirements Briefings

DTIC Science & Technology

2009-02-17

procedure drills through complex multiplayer interactions representative of a motorcade under heavy attack. The tool shall provide a first-person...Integrated Munitions Effect Assessment IMI Interactive Multimedia Instruction IP Internet Protocol IPE Intelligence Preparation of the Environment IR...CTTSO Programs and Mission Areas/Subgroups 13 Requirement Descriptions Blast Effects and Mitigation (BX) 16 Chemical, Biological, Radiological, and

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

Design and implementation of bimolecular fluorescence complementation (BiFC) assays for the visualization of protein interactions in living cells.

PubMed

Kerppola, Tom K

2006-01-01

Bimolecular fluorescence complementation (BiFC) analysis enables direct visualization of protein interactions in living cells. The BiFC assay is based on the discoveries that two non-fluorescent fragments of a fluorescent protein can form a fluorescent complex and that the association of the fragments can be facilitated when they are fused to two proteins that interact with each other. BiFC must be confirmed by parallel analysis of proteins in which the interaction interface has been mutated. It is not necessary for the interaction partners to juxtapose the fragments within a specific distance of each other because they can associate when they are tethered to a complex with flexible linkers. It is also not necessary for the interaction partners to form a complex with a long half-life or a high occupancy since the fragments can associate in a transient complex and un-associated fusion proteins do not interfere with detection of the complex. Many interactions can be visualized when the fusion proteins are expressed at levels comparable to their endogenous counterparts. The BiFC assay has been used for the visualization of interactions between many types of proteins in different subcellular locations and in different cell types and organisms. It is technically straightforward and can be performed using a regular fluorescence microscope and standard molecular biology and cell culture reagents.

Complex between triple helix of collagen and double helix of DNA in aqueous solution.

PubMed

Mrevlishvili, George M; Svintradze, David V

2005-06-01

We demonstrate in this paper that one example of a biologically important and molecular self-assembling complex system is a collagen-DNA ordered aggregate which spontaneously forms in aqueous solutions. Interaction between the collagen and the DNA leads to destruction of the hydration shell of the triple helix and stabilization of the double helix structure. From a molecular biology point of view this nano-scale self-assembling superstructure could increase the stability of DNA against the nucleases during collagen diseases and the growth of collagen fibrills in the presence of DNA.

Nanodiamonds as Carriers for Address Delivery of Biologically Active Substances

NASA Astrophysics Data System (ADS)

Purtov, K. V.; Petunin, A. I.; Burov, A. E.; Puzyr, A. P.; Bondar, V. S.

2010-03-01

Surface of detonation nanodiamonds was functionalized for the covalent attachment of immunoglobulin, and simultaneously bovine serum albumin and Rabbit Anti-Mouse Antibody. The nanodiamond-IgGI125 and RAM-nanodiamond-BSAI125 complexes are stable in blood serum and the immobilized proteins retain their biological activity. It was shown that the RAM-nanodiamond-BSAI125 complex is able to bind to the target antigen immobilized on the Sepharose 6B matrix through antibody-antigen interaction. The idea can be extended to use nanodiamonds as carriers for delivery of bioactive substances (i.e., drugs) to various targets in vivo.

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.

Distinct mechanisms of recognizing endosomal sorting complex required for transport III (ESCRT-III) protein IST1 by different microtubule interacting and trafficking (MIT) domains.

PubMed

Guo, Emily Z; Xu, Zhaohui

2015-03-27

The endosomal sorting complex required for transport (ESCRT) machinery is responsible for membrane remodeling in a number of biological processes including multivesicular body biogenesis, cytokinesis, and enveloped virus budding. In mammalian cells, efficient abscission during cytokinesis requires proper function of the ESCRT-III protein IST1, which binds to the microtubule interacting and trafficking (MIT) domains of VPS4, LIP5, and Spartin via its C-terminal MIT-interacting motif (MIM). Here, we studied the molecular interactions between IST1 and the three MIT domain-containing proteins to understand the structural basis that governs pairwise MIT-MIM interaction. Crystal structures of the three molecular complexes revealed that IST1 binds to the MIT domains of VPS4, LIP5, and Spartin using two different mechanisms (MIM1 mode versus MIM3 mode). Structural comparison revealed that structural features in both MIT and MIM contribute to determine the specific binding mechanism. Within the IST1 MIM sequence, two phenylalanine residues were shown to be important in discriminating MIM1 versus MIM3 binding. These observations enabled us to deduce a preliminary binding code, which we applied to provide CHMP2A, a protein that normally only binds the MIT domain in the MIM1 mode, the additional ability to bind the MIT domain of Spartin in the MIM3 mode. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Complex network theory for the identification and assessment of candidate protein targets.

PubMed

McGarry, Ken; McDonald, Sharon

2018-06-01

In this work we use complex network theory to provide a statistical model of the connectivity patterns of human proteins and their interaction partners. Our intention is to identify important proteins that may be predisposed to be potential candidates as drug targets for therapeutic interventions. Target proteins usually have more interaction partners than non-target proteins, but there are no hard-and-fast rules for defining the actual number of interactions. We devise a statistical measure for identifying hub proteins, we score our target proteins with gene ontology annotations. The important druggable protein targets are likely to have similar biological functions that can be assessed for their potential therapeutic value. Our system provides a statistical analysis of the local and distant neighborhood protein interactions of the potential targets using complex network measures. This approach builds a more accurate model of drug-to-target activity and therefore the likely impact on treating diseases. We integrate high quality protein interaction data from the HINT database and disease associated proteins from the DrugTarget database. Other sources include biological knowledge from Gene Ontology and drug information from DrugBank. The problem is a very challenging one since the data is highly imbalanced between target proteins and the more numerous nontargets. We use undersampling on the training data and build Random Forest classifier models which are used to identify previously unclassified target proteins. We validate and corroborate these findings from the available literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Advances on plant-pathogen interactions from molecular toward systems biology perspectives.

PubMed

Peyraud, Rémi; Dubiella, Ullrich; Barbacci, Adelin; Genin, Stéphane; Raffaele, Sylvain; Roby, Dominique

2017-05-01

In the past 2 decades, progress in molecular analyses of the plant immune system has revealed key elements of a complex response network. Current paradigms depict the interaction of pathogen-secreted molecules with host target molecules leading to the activation of multiple plant response pathways. Further research will be required to fully understand how these responses are integrated in space and time, and exploit this knowledge in agriculture. In this review, we highlight systems biology as a promising approach to reveal properties of molecular plant-pathogen interactions and predict the outcome of such interactions. We first illustrate a few key concepts in plant immunity with a network and systems biology perspective. Next, we present some basic principles of systems biology and show how they allow integrating multiomics data and predict cell phenotypes. We identify challenges for systems biology of plant-pathogen interactions, including the reconstruction of multiscale mechanistic models and the connection of host and pathogen models. Finally, we outline studies on resistance durability through the robustness of immune system networks, the identification of trade-offs between immunity and growth and in silico plant-pathogen co-evolution as exciting perspectives in the field. We conclude that the development of sophisticated models of plant diseases incorporating plant, pathogen and climate properties represent a major challenge for agriculture in the future. © 2016 The Authors. The Plant Journal published by John Wiley & Sons Ltd and Society for Experimental Biology.

Investigating Protein-Ligand Interactions by Solution Nuclear Magnetic Resonance Spectroscopy.

PubMed

Becker, Walter; Bhattiprolu, Krishna Chaitanya; Gubensäk, Nina; Zangger, Klaus

2018-04-17

Protein-ligand interactions are of fundamental importance in almost all processes in living organisms. The ligands comprise small molecules, drugs or biological macromolecules and their interaction strength varies over several orders of magnitude. Solution NMR spectroscopy offers a large repertoire of techniques to study such complexes. Here, we give an overview of the different NMR approaches available. The information they provide ranges from the simple information about the presence of binding or epitope mapping to the complete 3 D structure of the complex. NMR spectroscopy is particularly useful for the study of weak interactions and for the screening of binding ligands with atomic resolution. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Interacting complex systems: Theory and application to real-world situations

NASA Astrophysics Data System (ADS)

Piccinini, Nicola

The interest in complex systems has increased exponentially during the past years because it was found helpful in addressing many of today's challenges. The study of the brain, biology, earthquakes, markets and social sciences are only a few examples of the fields that have benefited from the investigation of complex systems. Internet, the increased mobility of people and the raising energy demand are among the factors that brought in contact complex systems that were isolated till a few years ago. A theory for the interaction between complex systems is becoming more and more urgent to help mankind in this transition. The present work builds upon the most recent results in this field by solving a theoretical problem that prevented previous work to be applied to important complex systems, like the brain. It also shows preliminary laboratory results of perturbation of in vitro neural networks that were done to test the theory. Finally, it gives a preview of the studies that are being done to create a theory that is even closer to the interaction between real complex systems.

Mechanistic understanding of nanoparticles' interactions with extracellular matrix: the cell and immune system.

PubMed

Engin, Ayse Basak; Nikitovic, Dragana; Neagu, Monica; Henrich-Noack, Petra; Docea, Anca Oana; Shtilman, Mikhail I; Golokhvast, Kirill; Tsatsakis, Aristidis M

2017-06-24

Extracellular matrix (ECM) is an extraordinarily complex and unique meshwork composed of structural proteins and glycosaminoglycans. The ECM provides essential physical scaffolding for the cellular constituents, as well as contributes to crucial biochemical signaling. Importantly, ECM is an indispensable part of all biological barriers and substantially modulates the interchange of the nanotechnology products through these barriers. The interactions of the ECM with nanoparticles (NPs) depend on the morphological characteristics of intercellular matrix and on the physical characteristics of the NPs and may be either deleterious or beneficial. Importantly, an altered expression of ECM molecules ultimately affects all biological processes including inflammation. This review critically discusses the specific behavior of NPs that are within the ECM domain, and passing through the biological barriers. Furthermore, regenerative and toxicological aspects of nanomaterials are debated in terms of the immune cells-NPs interactions.

Modelling biological behaviours with the unified modelling language: an immunological case study and critique.

PubMed

Read, Mark; Andrews, Paul S; Timmis, Jon; Kumar, Vipin

2014-10-06

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.

Modelling biological behaviours with the unified modelling language: an immunological case study and critique

PubMed Central

Read, Mark; Andrews, Paul S.; Timmis, Jon; Kumar, Vipin

2014-01-01

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology. PMID:25142524

Application of Biologically-Based Lumping To Investigate the ...

EPA Pesticide Factsheets

People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. However, investigators have often considered complex mixtures as one lumped entity. Valuable information can be obtained from these experiments, though this simplification provides little insight into the impact of a mixture's chemical composition on toxicologically-relevant metabolic interactions that may occur among its constituents. We developed an approach that applies chemical lumping methods to complex mixtures, in this case gasoline, based on biologically relevant parameters used in physiologically-based pharmacokinetic (PBPK) modeling. Inhalation exposures were performed with rats to evaluate performance of our PBPK model. There were 109 chemicals identified and quantified in the vapor in the chamber. The time-course kinetic profiles of 10 target chemicals were also determined from blood samples collected during and following the in vivo experiments. A general PBPK model was used to compare the experimental data to the simulated values of blood concentration for the 10 target chemicals with various numbers of lumps, iteratively increasing from 0 to 99. Large reductions in simulation error were gained by incorporating enzymatic chemical interactions, in comparison to simulating the individual chemicals separately. The error was further reduced by lumping the 99 non-target chemicals. Application of this biologic

Mechanism of Aldolase Control of Sorting Nexin 9 Function in Endocytosis

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

Rangarajan, Erumbi S.; Park, HaJeung; Fortin, Emanuelle

Sorting nexin 9 (SNX9) functions in a complex with the GTPase dynamin-2 at clathrin-coated pits, where it provokes fission of vesicles to complete endocytosis. Here the SNX9-dynamin-2 complex binds to clathrin and adapter protein complex 2 (AP-2) that line these pits, and this occurs through interactions of the low complexity domain (LC4) of SNX9 with AP-2. Intriguingly, localization of the SNX9-dynamin-2 complex to clathrin-coated pits is blocked by interactions with the abundant glycolytic enzyme aldolase, which also binds to the LC4 domain of SNX9. The crystal structure of the LC4 motif of human SNX9 in complex with aldolase explains themore » biochemistry and biology of this interaction, where SNX9 binds near the active site of aldolase via residues 165-171 that are also required for the interactions of SNX9 with AP-2. Accordingly, SNX9 binding to aldolase is structurally precluded by the binding of substrate to the active site. Interactions of SNX9 with aldolase are far more extensive and differ from those of the actin-nucleating factor WASP with aldolase, indicating considerable plasticity in mechanisms that direct the functions of the aldolase as a scaffold protein.« less

Hydrogen bonding and pi-stacking: how reliable are force fields? A critical evaluation of force field descriptions of nonbonded interactions.

PubMed

Paton, Robert S; Goodman, Jonathan M

2009-04-01

We have evaluated the performance of a set of widely used force fields by calculating the geometries and stabilization energies for a large collection of intermolecular complexes. These complexes are representative of a range of chemical and biological systems for which hydrogen bonding, electrostatic, and van der Waals interactions play important roles. Benchmark energies are taken from the high-level ab initio values in the JSCH-2005 and S22 data sets. All of the force fields underestimate stabilization resulting from hydrogen bonding, but the energetics of electrostatic and van der Waals interactions are described more accurately. OPLSAA gave a mean unsigned error of 2 kcal mol(-1) for all 165 complexes studied, and outperforms DFT calculations employing very large basis sets for the S22 complexes. The magnitude of hydrogen bonding interactions are severely underestimated by all of the force fields tested, which contributes significantly to the overall mean error; if complexes which are predominantly bound by hydrogen bonding interactions are discounted, the mean unsigned error of OPLSAA is reduced to 1 kcal mol(-1). For added clarity, web-based interactive displays of the results have been developed which allow comparisons of force field and ab initio geometries to be performed and the structures viewed and rotated in three dimensions.

Understanding the mechanism of DNA deactivation in ion therapy of cancer cells: hydrogen peroxide action*

NASA Astrophysics Data System (ADS)

Piatnytskyi, Dmytro V.; Zdorevskyi, Oleksiy O.; Perepelytsya, Sergiy M.; Volkov, Sergey N.

2015-11-01

Changes in the medium of biological cells under ion beam irradiation has been considered as a possible cause of cell function disruption in the living body. The interaction of hydrogen peroxide, a long-lived molecular product of water radiolysis, with active sites of DNA macromolecule was studied, and the formation of stable DNA-peroxide complexes was considered. The phosphate groups of the macromolecule backbone were picked out among the atomic groups of DNA double helix as a probable target for interaction with hydrogen peroxide molecules. Complexes consisting of combinations including: the DNA phosphate group, H2O2 and H2O molecules, and Na+ counterion, were considered. The counterions have been taken into consideration insofar as under the natural conditions they neutralise DNA sugar-phosphate backbone. The energy of the complexes have been determined by considering the electrostatic and the Van der Waals interactions within the framework of atom-atom potential functions. As a result, the stability of various configurations of molecular complexes was estimated. It was shown that DNA phosphate groups and counterions can form stable complexes with hydrogen peroxide molecules, which are as stable as the complexes with water molecules. It has been demonstrated that the formation of stable complexes of H2O2-Na+-PO4- may be detected experimentally by observing specific vibrations in the low-frequency Raman spectra. The interaction of H2O2 molecule with phosphate group of the double helix backbone can disrupt DNA biological function and induce the deactivation of the cell genetic apparatus. Thus, the production of hydrogen peroxide molecules in the nucleus of living cells can be considered as an additional mechanism by which high-energy ion beams destroy tumour cells during ion beam therapy. Contribution to the Topical Issue "COST Action Nano-IBCT: Nano-scale Processes Behind Ion-Beam Cancer Therapy", edited by Andrey Solov'yov, Nigel Mason, Gustavo García, Eugene Surdutovich.

General and craniofacial development are complex adaptive processes influenced by diversity.

PubMed

Brook, A H; O'Donnell, M Brook; Hone, A; Hart, E; Hughes, T E; Smith, R N; Townsend, G C

2014-06-01

Complex systems are present in such diverse areas as social systems, economies, ecosystems and biology and, therefore, are highly relevant to dental research, education and practice. A Complex Adaptive System in biological development is a dynamic process in which, from interacting components at a lower level, higher level phenomena and structures emerge. Diversity makes substantial contributions to the performance of complex adaptive systems. It enhances the robustness of the process, allowing multiple responses to external stimuli as well as internal changes. From diversity comes variation in outcome and the possibility of major change; outliers in the distribution enhance the tipping points. The development of the dentition is a valuable, accessible model with extensive and reliable databases for investigating the role of complex adaptive systems in craniofacial and general development. The general characteristics of such systems are seen during tooth development: self-organization; bottom-up emergence; multitasking; self-adaptation; variation; tipping points; critical phases; and robustness. Dental findings are compatible with the Random Network Model, the Threshold Model and also with the Scale Free Network Model which has a Power Law distribution. In addition, dental development shows the characteristics of Modularity and Clustering to form Hierarchical Networks. The interactions between the genes (nodes) demonstrate Small World phenomena, Subgraph Motifs and Gene Regulatory Networks. Genetic mechanisms are involved in the creation and evolution of variation during development. The genetic factors interact with epigenetic and environmental factors at the molecular level and form complex networks within the cells. From these interactions emerge the higher level tissues, tooth germs and mineralized teeth. Approaching development in this way allows investigation of why there can be variations in phenotypes from identical genotypes; the phenotype is the outcome of perturbations in the cellular systems and networks, as well as of the genotype. Understanding and applying complexity theory will bring about substantial advances not only in dental research and education but also in the organization and delivery of oral health care. © 2014 Australian Dental Association.

Interaction of polyethyleneimine-anchored copper(II) complexes with tRNA studied by spectroscopy methods and biological activities.

PubMed

Lakshmipraba, Jagadeesan; Arunachalam, Sankaralingam; Gandi, Devadas A; Thirunalasundari, Thyagarajan; Vignesh, Sivanandham; James, Rathinam A

2017-05-01

Ultraviolet-visible, emission and circular dichroism spectroscopic methods were used in transfer RNA (tRNA) interaction studies performed for polyethyleneimine-copper(II) complexes [Cu(phen)(l-Tyr)BPEI]ClO 4 (where phen =1,10-phenanthroline, l-Tyr = l-tyrosine and BPEI = branched polyethyleneimine) with various degrees of coordination (x = 0.059, 0.149, 0.182) in the polymer chain. The results indicated that polyethyleneimine-copper(II) complexes bind with tRNA mostly through surface binding, although other binding modes, such as hydrogen bonding and van der Waals interactions, might also be present. Dye-exclusion, sulforhodamine B and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays of a polyethyleneimine-copper(II) complex with a higher degree of coordination against different cancer cell lines proved that the complex exhibited cytotoxic specificity and a significant cancer cell inhibition rate. Antimicrobial screening showed activity against some human pathogens. Copyright © 2016 John Wiley & Sons, Ltd.

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

PubMed

Liu, Lizhen; Sun, Xiaowu; Song, Wei; Du, Chao

2018-06-01

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

A thorough experimental study of CH/π interactions in water: quantitative structure-stability relationships for carbohydrate/aromatic complexes.

PubMed

Jiménez-Moreno, Ester; Jiménez-Osés, Gonzalo; Gómez, Ana M; Santana, Andrés G; Corzana, Francisco; Bastida, Agatha; Jiménez-Barbero, Jesus; Asensio, Juan Luis

2015-11-13

CH/π interactions play a key role in a large variety of molecular recognition processes of biological relevance. However, their origins and structural determinants in water remain poorly understood. In order to improve our comprehension of these important interaction modes, we have performed a quantitative experimental analysis of a large data set comprising 117 chemically diverse carbohydrate/aromatic stacking complexes, prepared through a dynamic combinatorial approach recently developed by our group. The obtained free energies provide a detailed picture of the structure-stability relationships that govern the association process, opening the door to the rational design of improved carbohydrate-based ligands or carbohydrate receptors. Moreover, this experimental data set, supported by quantum mechanical calculations, has contributed to the understanding of the main driving forces that promote complex formation, underlining the key role played by coulombic and solvophobic forces on the stabilization of these complexes. This represents the most quantitative and extensive experimental study reported so far for CH/π complexes in water.

Extreme disorder in an ultrahigh-affinity protein complex

NASA Astrophysics Data System (ADS)

Borgia, Alessandro; Borgia, Madeleine B.; Bugge, Katrine; Kissling, Vera M.; Heidarsson, Pétur O.; Fernandes, Catarina B.; Sottini, Andrea; Soranno, Andrea; Buholzer, Karin J.; Nettels, Daniel; Kragelund, Birthe B.; Best, Robert B.; Schuler, Benjamin

2018-03-01

Molecular communication in biology is mediated by protein interactions. According to the current paradigm, the specificity and affinity required for these interactions are encoded in the precise complementarity of binding interfaces. Even proteins that are disordered under physiological conditions or that contain large unstructured regions commonly interact with well-structured binding sites on other biomolecules. Here we demonstrate the existence of an unexpected interaction mechanism: the two intrinsically disordered human proteins histone H1 and its nuclear chaperone prothymosin-α associate in a complex with picomolar affinity, but fully retain their structural disorder, long-range flexibility and highly dynamic character. On the basis of closely integrated experiments and molecular simulations, we show that the interaction can be explained by the large opposite net charge of the two proteins, without requiring defined binding sites or interactions between specific individual residues. Proteome-wide sequence analysis suggests that this interaction mechanism may be abundant in eukaryotes.

Modeling synthetic lethality

PubMed Central

Le Meur, Nolwenn; Gentleman, Robert

2008-01-01

Background Synthetic lethality defines a genetic interaction where the combination of mutations in two or more genes leads to cell death. The implications of synthetic lethal screens have been discussed in the context of drug development as synthetic lethal pairs could be used to selectively kill cancer cells, but leave normal cells relatively unharmed. A challenge is to assess genome-wide experimental data and integrate the results to better understand the underlying biological processes. We propose statistical and computational tools that can be used to find relationships between synthetic lethality and cellular organizational units. Results In Saccharomyces cerevisiae, we identified multi-protein complexes and pairs of multi-protein complexes that share an unusually high number of synthetic genetic interactions. As previously predicted, we found that synthetic lethality can arise from subunits of an essential multi-protein complex or between pairs of multi-protein complexes. Finally, using multi-protein complexes allowed us to take into account the pleiotropic nature of the gene products. Conclusions Modeling synthetic lethality using current estimates of the yeast interactome is an efficient approach to disentangle some of the complex molecular interactions that drive a cell. Our model in conjunction with applied statistical methods and computational methods provides new tools to better characterize synthetic genetic interactions. PMID:18789146

8-Nitro-cGMP Attenuates the Interaction between SNARE Complex and Complexin through S-Guanylation of SNAP-25.

PubMed

Kishimoto, Yusuke; Kunieda, Kohei; Kitamura, Atsushi; Kakihana, Yuki; Akaike, Takaaki; Ihara, Hideshi

2018-02-21

8-Nitroguanosine 3',5'-cyclic monophosphate (8-nitro-cGMP) is the second messenger in nitric oxide/reactive oxygen species redox signaling. This molecule covalently binds to protein thiol groups, called S-guanylation, and exerts various biological functions. Recently, we have identified synaptosomal-associated protein 25 (SNAP-25) as a target of S-guanylation, and demonstrated that S-guanylation of SNAP25 enhanced SNARE complex formation. In this study, we have examined the effects of S-guanylation of SNAP-25 on the interaction between the SNARE complex and complexin (cplx), which binds to the SNARE complex with a high affinity. Pull-down assays and coimmunoprecipitation experiments have revealed that S-guanylation of Cys90 in SNAP-25 attenuates the interaction between the SNARE complex and cplx. In addition, blue native-PAGE followed by Western blot analysis revealed that the amount of cplx detected at a high molecular weight decreased upon 8-nitro-cGMP treatment in SH-SY5Y cells. These results demonstrated for the first time that S-guanylation of SNAP-25 attenuates the interaction between the SNARE complex and cplx.

Computational and theoretical approaches for studies of a lipid recognition protein on biological membranes

PubMed Central

Yamamoto, Eiji

2017-01-01

Many cellular functions, including cell signaling and related events, are regulated by the association of peripheral membrane proteins (PMPs) with biological membranes containing anionic lipids, e.g., phosphatidylinositol phosphate (PIP). This association is often mediated by lipid recognition modules present in many PMPs. Here, I summarize computational and theoretical approaches to investigate the molecular details of the interactions and dynamics of a lipid recognition module, the pleckstrin homology (PH) domain, on biological membranes. Multiscale molecular dynamics simulations using combinations of atomistic and coarse-grained models yielded results comparable to those of actual experiments and could be used to elucidate the molecular mechanisms of the formation of protein/lipid complexes on membrane surfaces, which are often difficult to obtain using experimental techniques. Simulations revealed some modes of membrane localization and interactions of PH domains with membranes in addition to the canonical binding mode. In the last part of this review, I address the dynamics of PH domains on the membrane surface. Local PIP clusters formed around the proteins exhibit anomalous fluctuations. This dynamic change in protein-lipid interactions cause temporally fluctuating diffusivity of proteins, i.e., the short-term diffusivity of the bound protein changes substantially with time, and may in turn contribute to the formation/dissolution of protein complexes in membranes. PMID:29159013

Detection of time delays and directional interactions based on time series from complex dynamical systems

NASA Astrophysics Data System (ADS)

Ma, Huanfei; Leng, Siyang; Tao, Chenyang; Ying, Xiong; Kurths, Jürgen; Lai, Ying-Cheng; Lin, Wei

2017-07-01

Data-based and model-free accurate identification of intrinsic time delays and directional interactions is an extremely challenging problem in complex dynamical systems and their networks reconstruction. A model-free method with new scores is proposed to be generally capable of detecting single, multiple, and distributed time delays. The method is applicable not only to mutually interacting dynamical variables but also to self-interacting variables in a time-delayed feedback loop. Validation of the method is carried out using physical, biological, and ecological models and real data sets. Especially, applying the method to air pollution data and hospital admission records of cardiovascular diseases in Hong Kong reveals the major air pollutants as a cause of the diseases and, more importantly, it uncovers a hidden time delay (about 30-40 days) in the causal influence that previous studies failed to detect. The proposed method is expected to be universally applicable to ascertaining and quantifying subtle interactions (e.g., causation) in complex systems arising from a broad range of disciplines.

Protein-protein interactions in the regulation of WRKY transcription factors.

PubMed

Chi, Yingjun; Yang, Yan; Zhou, Yuan; Zhou, Jie; Fan, Baofang; Yu, Jing-Quan; Chen, Zhixiang

2013-03-01

It has been almost 20 years since the first report of a WRKY transcription factor, SPF1, from sweet potato. Great progress has been made since then in establishing the diverse biological roles of WRKY transcription factors in plant growth, development, and responses to biotic and abiotic stress. Despite the functional diversity, almost all analyzed WRKY proteins recognize the TTGACC/T W-box sequences and, therefore, mechanisms other than mere recognition of the core W-box promoter elements are necessary to achieve the regulatory specificity of WRKY transcription factors. Research over the past several years has revealed that WRKY transcription factors physically interact with a wide range of proteins with roles in signaling, transcription, and chromatin remodeling. Studies of WRKY-interacting proteins have provided important insights into the regulation and mode of action of members of the important family of transcription factors. It has also emerged that the slightly varied WRKY domains and other protein motifs conserved within each of the seven WRKY subfamilies participate in protein-protein interactions and mediate complex functional interactions between WRKY proteins and between WRKY and other regulatory proteins in the modulation of important biological processes. In this review, we summarize studies of protein-protein interactions for WRKY transcription factors and discuss how the interacting partners contribute, at different levels, to the establishment of the complex regulatory and functional network of WRKY transcription factors.

Soil biology research across latitude, elevation and disturbance gradients: A review of forest studies from Puerto Rico during the past 25 years

Treesearch

Grizelle González; D. Lodge

2017-01-01

Progress in understanding changes in soil biology in response to latitude, elevation and disturbance gradients has generally lagged behind studies of above-ground plants and animals owing to methodological constraints and high diversity and complexity of interactions in below-ground food webs. New methods have opened research opportunities in below-ground systems,...

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.

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

PubMed Central

Macropol, Kathy; Can, Tolga; Singh, Ambuj K

2009-01-01

Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439

Linking Biological Responses of Terrestrial N Eutrophication to the Final Ecosystem Goods and Services Classification System

NASA Astrophysics Data System (ADS)

Bell, M. D.; Clark, C.; Blett, T.

2015-12-01

The response of a biological indicator to N deposition can indicate that an ecosystem has surpassed a critical load and is at risk of significant change. The importance of this exceedance is often difficult to digest by policy makers and public audiences if the change is not linked to a familiar ecosystem endpoint. A workshop was held to bring together scientists, resource managers, and policy makers with expertise in ecosystem functioning, critical loads, and economics in an effort to identify the ecosystem services impacted by air pollution. This was completed within the framework of the Final Ecosystem Goods and Services (FEGS) Classification System to produce a product that identified distinct interactions between society and the effects of nitrogen pollution. From each change in a biological indicator, we created multiple ecological production functions to identify the cascading effects of the change to a measureable ecosystem service that a user interacts with either by enjoying, consuming, or appreciating the good or service, or using it as an input in the human economy. This FEGS metric was then linked to a beneficiary group that interacts with the service. Chains detailing the links from the biological indicator to the beneficiary group were created for aquatic and terrestrial acidification and eutrophication at the workshop, and here we present a subset of the workshop results by highlighting for 9 different ecosystems affected by terrestrial eutrophication. A total of 213 chains that linked to 37 unique FEGS metrics and impacted 15 beneficiary groups were identified based on nitrogen deposition mediated changes to biological indicators. The chains within each ecosystem were combined in flow charts to show the complex, overlapping relationships among biological indicators, ecosystem services, and beneficiary groups. Strength of relationship values were calculated for each chain based on support for the link in the scientific literature. We produced the complex diagrams to provide details to scientists and experts, identify research gaps, and understand the complexity of critical load exceedances. We then simplified the flow charts into stories that could produce an emotional connection and resonate with a general audience. These stories are important steps to breach the gaps between science and policy.

[Parametabolism as Non-Specific Modifier of Supramolecular Interactions in Living Systems].

PubMed

Kozlov, V A; Sapozhnikov, S P; Sheptuhina, A I; Golenkov, A V

2015-01-01

As it became known recently, in addition to the enzyme (enzymes and/or ribozymes) in living organisms occur a large number of ordinary chemical reactions without the participation of biological catalysts. These reactions are distinguished by low speed and, as a rule, the irreversibility. For example, along with diabetes mellitus, glycation and fructosilation of proteins are observed resulted in posttranslational modification with the low- or nonfunctioning protein formation which is poorly exposed to enzymatic proteolysis and therefore accumulates in the body. In addition, the known processes such as the nonenzymatic carbomoylation, pyridoxylation and thiamiation proteins. There is a reasonable basis to believe that alcoholic injury also realized through parametabolic secondary metabolites synthesis such as acetaldehyde. At the same time, the progress in supramolecular chemistry proves that in biological objects there is another large group ofparametabolic reactions caused by the formation of supramolecular complexes. Obviously, known parameterizes interactions can modify the formation of supramolecular complexes in living objects. These processes are of considerable interest for fundamental biology and fundamental and practical medicine, but they remain unexplored due to a lack of awareness of a wide range of researchers.

FUSE: a profit maximization approach for functional summarization of biological networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes; Yu, Hanry

2012-03-21

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

Synthesis and characterization of glucosyl-curcuminoids as Fe3+ suppliers in the treatment of iron deficiency.

PubMed

Ferrari, Erika; Arezzini, Beatrice; Ferrali, Marco; Lazzari, Sandra; Pignedoli, Francesca; Spagnolo, Ferdinando; Saladini, Monica

2009-10-01

The Fe(3+) chelating ability of some curcumin glucosyl derivatives (Glc-H; Glc-OH; Glc-OCH(3)) is tested by means of UV and NMR study. The pK(a) values of the ligands and the overall stability constants of Fe(3+) and Ga(3+) complexes are evaluated from UV spectra. The only metal binding site of the ligand is the beta-diketo moiety in the keto-enolic form; the glucosyl moiety does not interact with metal ion but it contributes to the stability of metal/ligand 1:2 complexes by means of hydrophilic interactions. These glucosyl derivatives are able to bind Fe(3+) in a wide pH rage, forming complex species thermodynamically more stable than those of other ligands commonly used in the treatment of iron deficiency. In addition they demonstrate to have a poor affinity for competitive biological metal ions such as Ca(2+). All ligands and their iron complexes have a good lypophilicity (log P > -0.7) suggesting an efficient gastrointestinal absorption in view of their possible use as iron supplements in oral therapy. The ligand molecules are also tested for their antioxidant properties in "ex vivo" biological system.

A journey from reductionist to systemic cell biology aboard the schooner Tara.

PubMed

Karsenti, Eric

2012-07-01

In this essay I describe my personal journey from reductionist to systems cell biology and describe how this in turn led to a 3-year sea voyage to explore complex ocean communities. In describing this journey, I hope to convey some important principles that I gleaned along the way. I realized that cellular functions emerge from multiple molecular interactions and that new approaches borrowed from statistical physics are required to understand the emergence of such complex systems. Then I wondered how such interaction networks developed during evolution. Because life first evolved in the oceans, it became a natural thing to start looking at the small organisms that compose the plankton in the world's oceans, of which 98% are … individual cells-hence the Tara Oceans voyage, which finished on 31 March 2012 in Lorient, France, after a 60,000-mile around-the-world journey that collected more than 30,000 samples from 153 sampling stations.

Nonlinear mechanics of hybrid polymer networks that mimic the complex mechanical environment of cells

NASA Astrophysics Data System (ADS)

Jaspers, Maarten; Vaessen, Sarah L.; van Schayik, Pim; Voerman, Dion; Rowan, Alan E.; Kouwer, Paul H. J.

2017-05-01

The mechanical properties of cells and the extracellular environment they reside in are governed by a complex interplay of biopolymers. These biopolymers, which possess a wide range of stiffnesses, self-assemble into fibrous composite networks such as the cytoskeleton and extracellular matrix. They interact with each other both physically and chemically to create a highly responsive and adaptive mechanical environment that stiffens when stressed or strained. Here we show that hybrid networks of a synthetic mimic of biological networks and either stiff, flexible and semi-flexible components, even very low concentrations of these added components, strongly affect the network stiffness and/or its strain-responsive character. The stiffness (persistence length) of the second network, its concentration and the interaction between the components are all parameters that can be used to tune the mechanics of the hybrids. The equivalence of these hybrids with biological composites is striking.

Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology

PubMed Central

Rossin, Elizabeth J.; Lage, Kasper; Raychaudhuri, Soumya; Xavier, Ramnik J.; Tatar, Diana; Benita, Yair

2011-01-01

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein–protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease. PMID:21249183

Pragmatic information in biology and physics.

PubMed

Roederer, Juan G

2016-03-13

I will show how an objective definition of the concept of information and the consideration of recent results about information processing in the human brain help clarify some fundamental aspects of physics and biology. Rather than attempting to define information ab initio, I introduce the concept of interaction between material bodies as a primary concept. Two distinct categories can be identified: (i) interactions which can always be reduced to a superposition of physical interactions (forces) between elementary constituents; and (ii) interactions between complex bodies which cannot be expressed as a superposition of interactions between parts, and in which patterns and forms (in space and/or time) play the determining role. Pragmatic information is then defined as the link between a given pattern and the ensuing pattern-specific change. I will show that pragmatic information is a biological concept; it plays no active role in the purely physical domain-it only does so when a living organism intervenes. The consequences for physics (including foundations of quantum mechanics) and biology (including brain function) will be discussed. This will include speculations about three fundamental transitions, from the quantum to the classical domain, from natural inanimate to living systems, and from subhuman to human brain information-processing operations, introduced here in their direct connection with the concept of pragmatic information. © 2016 The Author(s).

Identifying cooperative transcriptional regulations using protein–protein interactions

PubMed Central

Nagamine, Nobuyoshi; Kawada, Yuji; Sakakibara, Yasubumi

2005-01-01

Cooperative transcriptional activations among multiple transcription factors (TFs) are important to understand the mechanisms of complex transcriptional regulations in eukaryotes. Previous studies have attempted to find cooperative TFs based on gene expression data with gene expression profiles as a measure of similarity of gene regulations. In this paper, we use protein–protein interaction data to infer synergistic binding of cooperative TFs. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. First, the protein–protein interaction networks are used to calculate the similarity of biological processes among genes. Second, we integrate this similarity and the chromatin immuno-precipitation data to identify cooperative TFs. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative TFs that have literature evidences but could not be identified by the previous method. Further, 12 new possible pairs have been inferred and we have examined the biological relevances for them. However, since a typical problem using protein–protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy. PMID:16126847

Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

PubMed Central

2013-01-01

Background In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. Results We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Conclusions Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes. PMID:23663484

Modeling transcriptional networks regulating secondary growth and wood formation in forest trees

Treesearch

Lijun Liu; Vladimir Filkov; Andrew Groover

2013-01-01

The complex interactions among the genes that underlie a biological process can be modeled and presented as a transcriptional network, in which genes (nodes) and their interactions (edges) are shown in a graphical form similar to a wiring diagram. A large number of genes have been identified that are expressed during the radial woody growth of tree stems (secondary...

An Introduction to Drug Discovery by Probing Protein-Substrate Interactions Using Saturation Transfer Difference-Nuclear Magnetic Resonance (STD-NMR)

ERIC Educational Resources Information Center

Guegan, Jean-Paul; Daniellou, Richard

2012-01-01

NMR spectroscopy is a powerful tool for characterizing and identifying molecules and nowadays is even used to characterize complex systems in biology. In the experiment presented here, students learned how to apply this modern technique to probe interactions between small molecules and proteins. With the use of simple organic synthesis, students…

Computational prediction of protein hot spot residues.

PubMed

Morrow, John Kenneth; Zhang, Shuxing

2012-01-01

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

Optical micromanipulation of nanoparticles and cells inside living zebrafish.

PubMed

Johansen, Patrick Lie; Fenaroli, Federico; Evensen, Lasse; Griffiths, Gareth; Koster, Gerbrand

2016-03-21

Regulation of biological processes is often based on physical interactions between cells and their microenvironment. To unravel how and where interactions occur, micromanipulation methods can be used that offer high-precision control over the duration, position and magnitude of interactions. However, lacking an in vivo system, micromanipulation has generally been done with cells in vitro, which may not reflect the complex in vivo situation inside multicellular organisms. Here using optical tweezers we demonstrate micromanipulation throughout the transparent zebrafish embryo. We show that different cells, as well as injected nanoparticles and bacteria can be trapped and that adhesion properties and membrane deformation of endothelium and macrophages can be analysed. This non-invasive micromanipulation inside a whole-organism gives direct insights into cell interactions that are not accessible using existing approaches. Potential applications include screening of nanoparticle-cell interactions for cancer therapy or tissue invasion studies in cancer and infection biology.

Computational Prediction of Hot Spot Residues

PubMed Central

Morrow, John Kenneth; Zhang, Shuxing

2013-01-01

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues. PMID:22316154

Toxicity of silver nanoparticles in biological systems: Does the complexity of biological systems matter?

PubMed

Vazquez-Muñoz, Roberto; Borrego, Belen; Juárez-Moreno, Karla; García-García, Maritza; Mota Morales, Josué D; Bogdanchikova, Nina; Huerta-Saquero, Alejandro

2017-07-05

Currently, nanomaterials are more frequently in our daily life, specifically in biomedicine, electronics, food, textiles and catalysis just to name a few. Although nanomaterials provide many benefits, recently their toxicity profiles have begun to be explored. In this work, the toxic effects of silver nanoparticles (35nm-average diameter and Polyvinyl-Pyrrolidone-coated) on biological systems of different levels of complexity was assessed in a comprehensive and comparatively way, through a variety of viability and toxicological assays. The studied organisms included viruses, bacteria, microalgae, fungi, animal and human cells (including cancer cell lines). It was found that biological systems of different taxonomical groups are inhibited at concentrations of silver nanoparticles within the same order of magnitude. Thus, the toxicity of nanomaterials on biological/living systems, constrained by their complexity, e.g. taxonomic groups, resulted contrary to the expected. The fact that cells and virus are inhibited with a concentration of silver nanoparticles within the same order of magnitude could be explained considering that silver nanoparticles affects very primitive cellular mechanisms by interacting with fundamental structures for cells and virus alike. Copyright © 2017 Elsevier B.V. All rights reserved.

An exploration of alternative visualisations of the basic helix-loop-helix protein interaction network

PubMed Central

Holden, Brian J; Pinney, John W; Lovell, Simon C; Amoutzias, Grigoris D; Robertson, David L

2007-01-01

Background Alternative representations of biochemical networks emphasise different aspects of the data and contribute to the understanding of complex biological systems. In this study we present a variety of automated methods for visualisation of a protein-protein interaction network, using the basic helix-loop-helix (bHLH) family of transcription factors as an example. Results Network representations that arrange nodes (proteins) according to either continuous or discrete information are investigated, revealing the existence of protein sub-families and the retention of interactions following gene duplication events. Methods of network visualisation in conjunction with a phylogenetic tree are presented, highlighting the evolutionary relationships between proteins, and clarifying the context of network hubs and interaction clusters. Finally, an optimisation technique is used to create a three-dimensional layout of the phylogenetic tree upon which the protein-protein interactions may be projected. Conclusion We show that by incorporating secondary genomic, functional or phylogenetic information into network visualisation, it is possible to move beyond simple layout algorithms based on network topology towards more biologically meaningful representations. These new visualisations can give structure to complex networks and will greatly help in interpreting their evolutionary origins and functional implications. Three open source software packages (InterView, TVi and OptiMage) implementing our methods are available. PMID:17683601

Pulmonary immunity and extracellular matrix interactions.

PubMed

O'Dwyer, David N; Gurczynski, Stephen J; Moore, Bethany B

2018-04-09

The lung harbors a complex immune system composed of both innate and adaptive immune cells. Recognition of infection and injury by receptors on lung innate immune cells is crucial for generation of antigen-specific responses by adaptive immune cells. The extracellular matrix of the lung, comprising the interstitium and basement membrane, plays a key role in the regulation of these immune systems. The matrix consists of several hundred assembled proteins that interact to form a bioactive scaffold. This template, modified by enzymes, acts to facilitate cell function and differentiation and changes dynamically with age and lung disease. Herein, we explore relationships between innate and adaptive immunity and the lung extracellular matrix. We discuss the interactions between extracellular matrix proteins, including glycosaminoglycans, with prominent effects on innate immune signaling effectors such as toll-like receptors. We describe the relationship of extracellular matrix proteins with adaptive immunity and leukocyte migration to sites of injury within the lung. Further study of these interactions will lead to greater knowledge of the role of matrix biology in lung immunity. The development of novel therapies for acute and chronic lung disease is dependent on a comprehensive understanding of these complex matrix-immunity interactions. Copyright © 2017 International Society of Matrix Biology. Published by Elsevier B.V. All rights reserved.

Investigating cholesterol metabolism and ageing using a systems biology approach.

PubMed

Morgan, A E; Mooney, K M; Wilkinson, S J; Pickles, N A; Mc Auley, M T

2017-08-01

CVD accounted for 27 % of all deaths in the UK in 2014, and was responsible for 1·7 million hospital admissions in 2013/2014. This condition becomes increasingly prevalent with age, affecting 34·1 and 29·8 % of males and females over 75 years of age respectively in 2011. The dysregulation of cholesterol metabolism with age, often observed as a rise in LDL-cholesterol, has been associated with the pathogenesis of CVD. To compound this problem, it is estimated by 2050, 22 % of the world's population will be over 60 years of age, in culmination with a growing resistance and intolerance to pre-existing cholesterol regulating drugs such as statins. Therefore, it is apparent research into additional therapies for hypercholesterolaemia and CVD prevention is a growing necessity. However, it is also imperative to recognise this complex biological system cannot be studied using a reductionist approach; rather its biological uniqueness necessitates a more integrated methodology, such as that offered by systems biology. In this review, we firstly discuss cholesterol metabolism and how it is affected by diet and the ageing process. Next, we describe therapeutic strategies for hypercholesterolaemia, and finally how the systems biology paradigm can be utilised to investigate how ageing interacts with complex systems such as cholesterol metabolism. We conclude by emphasising the need for nutritionists to work in parallel with the systems biology community, to develop novel approaches to studying cholesterol metabolism and its interaction with ageing.

Coevolution at protein complex interfaces can be detected by the complementarity trace with important impact for predictive docking

PubMed Central

Madaoui, Hocine; Guerois, Raphaël

2008-01-01

Protein surfaces are under significant selection pressure to maintain interactions with their partners throughout evolution. Capturing how selection pressure acts at the interfaces of protein–protein complexes is a fundamental issue with high interest for the structural prediction of macromolecular assemblies. We tackled this issue under the assumption that, throughout evolution, mutations should minimally disrupt the physicochemical compatibility between specific clusters of interacting residues. This constraint drove the development of the so-called Surface COmplementarity Trace in Complex History score (SCOTCH), which was found to discriminate with high efficiency the structure of biological complexes. SCOTCH performances were assessed not only with respect to other evolution-based approaches, such as conservation and coevolution analyses, but also with respect to statistically based scoring methods. Validated on a set of 129 complexes of known structure exhibiting both permanent and transient intermolecular interactions, SCOTCH appears as a robust strategy to guide the prediction of protein–protein complex structures. Of particular interest, it also provides a basic framework to efficiently track how protein surfaces could evolve while keeping their partners in contact. PMID:18511568

Entropy in molecular recognition by proteins

PubMed Central

Caro, José A.; Harpole, Kyle W.; Kasinath, Vignesh; Lim, Jackwee; Granja, Jeffrey; Valentine, Kathleen G.; Sharp, Kim A.

2017-01-01

Molecular recognition by proteins is fundamental to molecular biology. Dissection of the thermodynamic energy terms governing protein–ligand interactions has proven difficult, with determination of entropic contributions being particularly elusive. NMR relaxation measurements have suggested that changes in protein conformational entropy can be quantitatively obtained through a dynamical proxy, but the generality of this relationship has not been shown. Twenty-eight protein–ligand complexes are used to show a quantitative relationship between measures of fast side-chain motion and the underlying conformational entropy. We find that the contribution of conformational entropy can range from favorable to unfavorable, which demonstrates the potential of this thermodynamic variable to modulate protein–ligand interactions. For about one-quarter of these complexes, the absence of conformational entropy would render the resulting affinity biologically meaningless. The dynamical proxy for conformational entropy or “entropy meter” also allows for refinement of the contributions of solvent entropy and the loss in rotational-translational entropy accompanying formation of high-affinity complexes. Furthermore, structure-based application of the approach can also provide insight into long-lived specific water–protein interactions that escape the generic treatments of solvent entropy based simply on changes in accessible surface area. These results provide a comprehensive and unified view of the general role of entropy in high-affinity molecular recognition by proteins. PMID:28584100

An integrative model of evolutionary covariance: a symposium on body shape in fishes.

PubMed

Walker, Jeffrey A

2010-12-01

A major direction of current and future biological research is to understand how multiple, interacting functional systems coordinate in producing a body that works. This understanding is complicated by the fact that organisms need to work well in multiple environments, with both predictable and unpredictable environmental perturbations. Furthermore, organismal design reflects a history of past environments and not a plan for future environments. How complex, interacting functional systems evolve, then, is a truly grand challenge. In accepting the challenge, an integrative model of evolutionary covariance is developed. The model combines quantitative genetics, functional morphology/physiology, and functional ecology. The model is used to convene scientists ranging from geneticists, to physiologists, to ecologists, to engineers to facilitate the emergence of body shape in fishes as a model system for understanding how complex, interacting functional systems develop and evolve. Body shape of fish is a complex morphology that (1) results from many developmental paths and (2) functions in many different behaviors. Understanding the coordination and evolution of the many paths from genes to body shape, body shape to function, and function to a working fish body in a dynamic environment is now possible given new technologies from genetics to engineering and new theoretical models that integrate the different levels of biological organization (from genes to ecology).

Entropy in molecular recognition by proteins.

PubMed

Caro, José A; Harpole, Kyle W; Kasinath, Vignesh; Lim, Jackwee; Granja, Jeffrey; Valentine, Kathleen G; Sharp, Kim A; Wand, A Joshua

2017-06-20

Molecular recognition by proteins is fundamental to molecular biology. Dissection of the thermodynamic energy terms governing protein-ligand interactions has proven difficult, with determination of entropic contributions being particularly elusive. NMR relaxation measurements have suggested that changes in protein conformational entropy can be quantitatively obtained through a dynamical proxy, but the generality of this relationship has not been shown. Twenty-eight protein-ligand complexes are used to show a quantitative relationship between measures of fast side-chain motion and the underlying conformational entropy. We find that the contribution of conformational entropy can range from favorable to unfavorable, which demonstrates the potential of this thermodynamic variable to modulate protein-ligand interactions. For about one-quarter of these complexes, the absence of conformational entropy would render the resulting affinity biologically meaningless. The dynamical proxy for conformational entropy or "entropy meter" also allows for refinement of the contributions of solvent entropy and the loss in rotational-translational entropy accompanying formation of high-affinity complexes. Furthermore, structure-based application of the approach can also provide insight into long-lived specific water-protein interactions that escape the generic treatments of solvent entropy based simply on changes in accessible surface area. These results provide a comprehensive and unified view of the general role of entropy in high-affinity molecular recognition by proteins.

Multiple tipping points and optimal repairing in interacting networks

PubMed Central

Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

2016-01-01

Systems composed of many interacting dynamical networks—such as the human body with its biological networks or the global economic network consisting of regional clusters—often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two ‘forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

Evolutionary dynamics of group interactions on structured populations: a review

PubMed Central

Perc, Matjaž; Gómez-Gardeñes, Jesús; Szolnoki, Attila; Floría, Luis M.; Moreno, Yamir

2013-01-01

Interactions among living organisms, from bacteria colonies to human societies, are inherently more complex than interactions among particles and non-living matter. Group interactions are a particularly important and widespread class, representative of which is the public goods game. In addition, methods of statistical physics have proved valuable for studying pattern formation, equilibrium selection and self-organization in evolutionary games. Here, we review recent advances in the study of evolutionary dynamics of group interactions on top of structured populations, including lattices, complex networks and coevolutionary models. We also compare these results with those obtained on well-mixed populations. The review particularly highlights that the study of the dynamics of group interactions, like several other important equilibrium and non-equilibrium dynamical processes in biological, economical and social sciences, benefits from the synergy between statistical physics, network science and evolutionary game theory. PMID:23303223

Modular biological function is most effectively captured by combining molecular interaction data types.

PubMed

Ames, Ryan M; Macpherson, Jamie I; Pinney, John W; Lovell, Simon C; Robertson, David L

2013-01-01

Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

Peculiarities of hemoglobin interaction with serum proteins of mice with Ehrlich carcinoma.

PubMed

Sitdikova, S M; Amandzholov, B S; Serebryakova, M V; Zhdanovich, M Yu; Kiselevskii, M V; Donenko, F V

2006-05-01

In male C57Bl/6 mice with transplanted Ehrlich carcinoma, hemoglobin forms a complex with serum proteins characterized by a molecular weight of about 300 kDa. The complex incorporates proteins weighing 100, 68, 65, and 15 kDa identified by MALDI-TOF mass spectrometry as haptoglobin, serum albumin, gi/26341396 nameless protein Mus musculus, and alpha-hemoglobin, respectively. This complex can possess biological activity and contribute to the control of tumor growth.

Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs

PubMed Central

Yan, Koon-Kiu; Wang, Daifeng; Sethi, Anurag; Muir, Paul; Kitchen, Robert; Cheng, Chao; Gerstein, Mark

2016-01-01

Biological systems are complex. In particular, the interactions between molecular components often form dense networks that, more often than not, are criticized for being inscrutable ‘hairballs’. We argue that one way of untangling these hairballs is through cross-disciplinary network comparison—leveraging advances in other disciplines to obtain new biological insights. In some cases, such comparisons enable the direct transfer of mathematical formalism between disciplines, precisely describing the abstract associations between entities and allowing us to apply a variety of sophisticated formalisms to biology. In cases where the detailed structure of the network does not permit the transfer of complete formalisms between disciplines, comparison of mechanistic interactions in systems for which we have significant day-to-day experience can provide analogies for interpreting relatively more abstruse biological networks. Here, we illustrate how these comparisons benefit the field with a few specific examples related to network growth, organizational hierarchies, and the evolution of adaptive systems. PMID:27047991

A Comprehensive Database and Analysis Framework To Incorporate Multiscale Data Types and Enable Integrated Analysis of Bioactive Polyphenols.

PubMed

Ho, Lap; Cheng, Haoxiang; Wang, Jun; Simon, James E; Wu, Qingli; Zhao, Danyue; Carry, Eileen; Ferruzzi, Mario G; Faith, Jeremiah; Valcarcel, Breanna; Hao, Ke; Pasinetti, Giulio M

2018-03-05

The development of a given botanical preparation for eventual clinical application requires extensive, detailed characterizations of the chemical composition, as well as the biological availability, biological activity, and safety profiles of the botanical. These issues are typically addressed using diverse experimental protocols and model systems. Based on this consideration, in this study we established a comprehensive database and analysis framework for the collection, collation, and integrative analysis of diverse, multiscale data sets. Using this framework, we conducted an integrative analysis of heterogeneous data from in vivo and in vitro investigation of a complex bioactive dietary polyphenol-rich preparation (BDPP) and built an integrated network linking data sets generated from this multitude of diverse experimental paradigms. We established a comprehensive database and analysis framework as well as a systematic and logical means to catalogue and collate the diverse array of information gathered, which is securely stored and added to in a standardized manner to enable fast query. We demonstrated the utility of the database in (1) a statistical ranking scheme to prioritize response to treatments and (2) in depth reconstruction of functionality studies. By examination of these data sets, the system allows analytical querying of heterogeneous data and the access of information related to interactions, mechanism of actions, functions, etc., which ultimately provide a global overview of complex biological responses. Collectively, we present an integrative analysis framework that leads to novel insights on the biological activities of a complex botanical such as BDPP that is based on data-driven characterizations of interactions between BDPP-derived phenolic metabolites and their mechanisms of action, as well as synergism and/or potential cancellation of biological functions. Out integrative analytical approach provides novel means for a systematic integrative analysis of heterogeneous data types in the development of complex botanicals such as polyphenols for eventual clinical and translational applications.

Fort Collins Science Center Ecosystem Dynamics Branch

USGS Publications Warehouse

Wilson, Jim; Melcher, C.; Bowen, Z.

2009-01-01

Complex natural resource issues require understanding a web of interactions among ecosystem components that are (1) interdisciplinary, encompassing physical, chemical, and biological processes; (2) spatially complex, involving movements of animals, water, and airborne materials across a range of landscapes and jurisdictions; and (3) temporally complex, occurring over days, weeks, or years, sometimes involving response lags to alteration or exhibiting large natural variation. Scientists in the Ecosystem Dynamics Branch of the U.S. Geological Survey, Fort Collins Science Center, investigate a diversity of these complex natural resource questions at the landscape and systems levels. This Fact Sheet describes the work of the Ecosystems Dynamics Branch, which is focused on energy and land use, climate change and long-term integrated assessments, herbivore-ecosystem interactions, fire and post-fire restoration, and environmental flows and river restoration.

Diets and Health: How Food Decisions Are Shaped by Biology, Economics, Geography, and Social Interactions.

PubMed

Drewnowski, Adam; Kawachi, Ichiro

2015-09-01

Health is shaped by both personal choices and features of the food environment. Food-choice decisions depend on complex interactions between biology and behavior, and are further modulated by the built environment and community structure. That lower-income families have lower-quality diets is well established. Yet, diet quality also varies across small geographic neighborhoods and can be influenced by transportation, retail, and ease of access to healthy foods, as well as by attitudes, beliefs, and social interactions. The learnings from the Seattle Obesity Study (SOS II) can be usefully applied to the much larger, more complex, and far more socially and ethnically diverse urban environment of New York City. The Kavli HUMAN Project (KHP) is ideally positioned to advance the understanding of health disparities by exploring the multiple underpinnings of food decision making. By combining geo-localized food shopping and consumption data with health behaviors, diet quality measures, and biomarkers, also coded by geographic location, the KHP will create the first-of-its-kind bio-behavioral, economic, and cultural atlas of diet quality and health for New York City.

Bio-nano interactions detected by nanochannel electrophoresis.

PubMed

Luan, Binquan

2016-08-01

Engineered nanoparticles have been widely used in industry and are present in many consumer products. However, their bio-safeties especially in a long term are largely unknown. Here, a nanochannel-electrophoresis-based method is proposed for detecting the potential bio-nano interactions that may further lead to damages to human health and/or biological environment. Through proof-of-concept molecular dynamics simulations, it was demonstrated that the transport of a protein-nanoparticle complex is very different from that of a protein along. By monitoring the change of ionic currents induced by a transported analyte as well as the transport velocities of the analyte, the complex (with bio-nano interaction) can be clearly distinguished from the protein alone (with no interaction with tested nanoparticles). © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

PROPER: global protein interaction network alignment through percolation matching.

PubMed

Kazemi, Ehsan; Hassani, Hamed; Grossglauser, Matthias; Pezeshgi Modarres, Hassan

2016-12-12

The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch .

Active Interaction Mapping as a tool to elucidate hierarchical functions of biological processes.

PubMed

Farré, Jean-Claude; Kramer, Michael; Ideker, Trey; Subramani, Suresh

2017-07-03

Increasingly, various 'omics data are contributing significantly to our understanding of novel biological processes, but it has not been possible to iteratively elucidate hierarchical functions in complex phenomena. We describe a general systems biology approach called Active Interaction Mapping (AI-MAP), which elucidates the hierarchy of functions for any biological process. Existing and new 'omics data sets can be iteratively added to create and improve hierarchical models which enhance our understanding of particular biological processes. The best datatypes to further improve an AI-MAP model are predicted computationally. We applied this approach to our understanding of general and selective autophagy, which are conserved in most eukaryotes, setting the stage for the broader application to other cellular processes of interest. In the particular application to autophagy-related processes, we uncovered and validated new autophagy and autophagy-related processes, expanded known autophagy processes with new components, integrated known non-autophagic processes with autophagy and predict other unexplored connections.

Microbiological control of soil-borne phytopathogenic fungi with special emphasis on wilt-inducing Fusarium oxysporum.

PubMed

Alabouvette, Claude; Olivain, Chantal; Migheli, Quirico; Steinberg, Christian

2009-11-01

Plant diseases induced by soil-borne plant pathogens are among the most difficult to control. In the absence of effective chemical control methods, there is renewed interest in biological control based on application of populations of antagonistic micro-organisms. In addition to Pseudomonas spp. and Trichoderma spp., which are the two most widely studied groups of biological control agents, the protective strains of Fusarium oxysporum represent an original model. These protective strains of F. oxysporum can be used to control wilt induced by pathogenic strains of the same species. Exploring the mechanisms involved in the protective capability of these strains is not only necessary for their development as commercial biocontrol agents but raises many basic questions related to the determinism of pathogenicity versus biocontrol capacity in the F. oxysporum species complex. In this paper, current knowledge regarding the interaction between the plant and the protective strains is reviewed in comparison with interactions between the plant and pathogenic strains. The success of biological control depends not only on plant-microbial interactions but also on the ecological fitness of the biological control agents.

Cooperation of Deterministic Dynamics and Random Noise in Production of Complex Syntactical Avian Song Sequences: A Neural Network Model

PubMed Central

Yamashita, Yuichi; Okumura, Tetsu; Okanoya, Kazuo; Tani, Jun

2011-01-01

How the brain learns and generates temporal sequences is a fundamental issue in neuroscience. The production of birdsongs, a process which involves complex learned sequences, provides researchers with an excellent biological model for this topic. The Bengalese finch in particular learns a highly complex song with syntactical structure. The nucleus HVC (HVC), a premotor nucleus within the avian song system, plays a key role in generating the temporal structures of their songs. From lesion studies, the nucleus interfacialis (NIf) projecting to the HVC is considered one of the essential regions that contribute to the complexity of their songs. However, the types of interaction between the HVC and the NIf that can produce complex syntactical songs remain unclear. In order to investigate the function of interactions between the HVC and NIf, we have proposed a neural network model based on previous biological evidence. The HVC is modeled by a recurrent neural network (RNN) that learns to generate temporal patterns of songs. The NIf is modeled as a mechanism that provides auditory feedback to the HVC and generates random noise that feeds into the HVC. The model showed that complex syntactical songs can be replicated by simple interactions between deterministic dynamics of the RNN and random noise. In the current study, the plausibility of the model is tested by the comparison between the changes in the songs of actual birds induced by pharmacological inhibition of the NIf and the changes in the songs produced by the model resulting from modification of parameters representing NIf functions. The efficacy of the model demonstrates that the changes of songs induced by pharmacological inhibition of the NIf can be interpreted as a trade-off between the effects of noise and the effects of feedback on the dynamics of the RNN of the HVC. These facts suggest that the current model provides a convincing hypothesis for the functional role of NIf–HVC interaction. PMID:21559065

Towards a comprehensive understanding of emerging dynamics and function of pancreatic islets: A complex network approach. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

Loppini, Alessandro

2018-03-01

Complex network theory represents a comprehensive mathematical framework to investigate biological systems, ranging from sub-cellular and cellular scales up to large-scale networks describing species interactions and ecological systems. In their exhaustive and comprehensive work [1], Gosak et al. discuss several scenarios in which the network approach was able to uncover general properties and underlying mechanisms of cells organization and regulation, tissue functions and cell/tissue failure in pathology, by the study of chemical reaction networks, structural networks and functional connectivities.

Hexameric supramolecular scaffold orients carbohydrates to sense bacteria.

PubMed

Grünstein, Dan; Maglinao, Maha; Kikkeri, Raghavendra; Collot, Mayeul; Barylyuk, Konstantin; Lepenies, Bernd; Kamena, Faustin; Zenobi, Renato; Seeberger, Peter H

2011-09-07

Carbohydrates are integral to biological signaling networks and cell-cell interactions, yet the detection of discrete carbohydrate-lectin interactions remains difficult since binding is generally weak. A strategy to overcome this problem is to create multivalent sensors, where the avidity rather than the affinity of the interaction is important. Here we describe the development of a series of multivalent sensors that self-assemble via hydrophobic supramolecular interactions. The multivalent sensors are comprised of a fluorescent ruthenium(II) core surrounded by a heptamannosylated β-cyclodextrin scaffold. Two additional series of complexes were synthesized as proof-of-principle for supramolecular self-assembly, the fluorescent core alone and the core plus β-cyclodextrin. Spectroscopic analyses confirmed that the three mannosylated sensors displayed 14, 28, and 42 sugar units, respectively. Each complex adopted original and unique spatial arrangements. The sensors were used to investigate the influence of carbohydrate spatial arrangement and clustering on the mechanistic and qualitative properties of lectin binding. Simple visualization of binding between a fluorescent, multivalent mannose complex and the Escherichia coli strain ORN178 that possesses mannose-specific receptor sites illustrates the potential for these complexes as biosensors.

Illuminating the Reaction Pathways of Viromimetic Assembly.

PubMed

Cingil, Hande E; Boz, Emre B; Biondaro, Giovanni; de Vries, Renko; Cohen Stuart, Martien A; Kraft, Daniela J; van der Schoot, Paul; Sprakel, Joris

2017-04-05

The coassembly of well-defined biological nanostructures relies on a delicate balance between attractive and repulsive interactions between biomolecular building blocks. Viral capsids are a prototypical example, where coat proteins exhibit not only self-interactions but also interact with the cargo they encapsulate. In nature, the balance between antagonistic and synergistic interactions has evolved to avoid kinetic trapping and polymorphism. To date, it has remained a major challenge to experimentally disentangle the complex kinetic reaction pathways that underlie successful coassembly of biomolecular building blocks in a noninvasive approach with high temporal resolution. Here we show how macromolecular force sensors, acting as a genome proxy, allow us to probe the pathways through which a viromimetic protein forms capsids. We uncover the complex multistage process of capsid assembly, which involves recruitment and complexation, followed by allosteric growth of the proteinaceous coat. Under certain conditions, the single-genome particles condense into capsids containing multiple copies of the template. Finally, we derive a theoretical model that quantitatively describes the kinetics of recruitment and growth. These results shed new light on the origins of the pathway complexity in biomolecular coassembly.

Dynamic pathway modeling of signal transduction networks: a domain-oriented approach.

PubMed

Conzelmann, Holger; Gilles, Ernst-Dieter

2008-01-01

Mathematical models of biological processes become more and more important in biology. The aim is a holistic understanding of how processes such as cellular communication, cell division, regulation, homeostasis, or adaptation work, how they are regulated, and how they react to perturbations. The great complexity of most of these processes necessitates the generation of mathematical models in order to address these questions. In this chapter we provide an introduction to basic principles of dynamic modeling and highlight both problems and chances of dynamic modeling in biology. The main focus will be on modeling of s transduction pathways, which requires the application of a special modeling approach. A common pattern, especially in eukaryotic signaling systems, is the formation of multi protein signaling complexes. Even for a small number of interacting proteins the number of distinguishable molecular species can be extremely high. This combinatorial complexity is due to the great number of distinct binding domains of many receptors and scaffold proteins involved in signal transduction. However, these problems can be overcome using a new domain-oriented modeling approach, which makes it possible to handle complex and branched signaling pathways.

A framework for stochastic simulations and visualization of biological electron-transfer dynamics

NASA Astrophysics Data System (ADS)

Nakano, C. Masato; Byun, Hye Suk; Ma, Heng; Wei, Tao; El-Naggar, Mohamed Y.

2015-08-01

Electron transfer (ET) dictates a wide variety of energy-conversion processes in biological systems. Visualizing ET dynamics could provide key insight into understanding and possibly controlling these processes. We present a computational framework named VizBET to visualize biological ET dynamics, using an outer-membrane Mtr-Omc cytochrome complex in Shewanella oneidensis MR-1 as an example. Starting from X-ray crystal structures of the constituent cytochromes, molecular dynamics simulations are combined with homology modeling, protein docking, and binding free energy computations to sample the configuration of the complex as well as the change of the free energy associated with ET. This information, along with quantum-mechanical calculations of the electronic coupling, provides inputs to kinetic Monte Carlo (KMC) simulations of ET dynamics in a network of heme groups within the complex. Visualization of the KMC simulation results has been implemented as a plugin to the Visual Molecular Dynamics (VMD) software. VizBET has been used to reveal the nature of ET dynamics associated with novel nonequilibrium phase transitions in a candidate configuration of the Mtr-Omc complex due to electron-electron interactions.

Translational Systems Biology and Voice Pathophysiology

PubMed Central

Li, Nicole Y. K.; Abbott, Katherine Verdolini; Rosen, Clark; An, Gary; Hebda, Patricia A.; Vodovotz, Yoram

2011-01-01

Objectives/Hypothesis Personalized medicine has been called upon to tailor healthcare to an individual's needs. Evidence-based medicine (EBM) has advocated using randomized clinical trials with large populations to evaluate treatment effects. However, due to large variations across patients, the results are likely not to apply to an individual patient. We suggest that a complementary, systems biology approach using computational modeling may help tackle biological complexity in order to improve ultimate patient care. The purpose of the article is: 1) to review the pros and cons of EBM, and 2) to discuss the alternative systems biology method and present its utility in clinical voice research. Study Design Tutorial Methods Literature review and discussion. Results We propose that translational systems biology can address many of the limitations of EBM pertinent to voice and other health care domains, and thus complement current health research models. In particular, recent work using mathematical modeling suggests that systems biology has the ability to quantify the highly complex biologic processes underlying voice pathophysiology. Recent data support the premise that this approach can be applied specifically in the case of phonotrauma and surgically induced vocal fold trauma, and may have particular power to address personalized medicine. Conclusions We propose that evidence around vocal health and disease be expanded beyond a population-based method to consider more fully issues of complexity and systems interactions, especially in implementing personalized medicine in voice care and beyond. PMID:20025041

Determining the Composition and Stability of Protein Complexes Using an Integrated Label-Free and Stable Isotope Labeling Strategy

PubMed Central

Greco, Todd M.; Guise, Amanda J.; Cristea, Ileana M.

2016-01-01

In biological systems, proteins catalyze the fundamental reactions that underlie all cellular functions, including metabolic processes and cell survival and death pathways. These biochemical reactions are rarely accomplished alone. Rather, they involve a concerted effect from many proteins that may operate in a directed signaling pathway and/or may physically associate in a complex to achieve a specific enzymatic activity. Therefore, defining the composition and regulation of protein complexes is critical for understanding cellular functions. In this chapter, we describe an approach that uses quantitative mass spectrometry (MS) to assess the specificity and the relative stability of protein interactions. Isolation of protein complexes from mammalian cells is performed by rapid immunoaffinity purification, and followed by in-solution digestion and high-resolution mass spectrometry analysis. We employ complementary quantitative MS workflows to assess the specificity of protein interactions using label-free MS and statistical analysis, and the relative stability of the interactions using a metabolic labeling technique. For each candidate protein interaction, scores from the two workflows can be correlated to minimize nonspecific background and profile protein complex composition and relative stability. PMID:26867737

In Silico Analysis for the Study of Botulinum Toxin Structure

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2010-01-01

Protein-protein interactions play many important roles in biological function. Knowledge of protein-protein complex structure is required for understanding the function. The determination of protein-protein complex structure by experimental studies remains difficult, therefore computational prediction of protein structures by structure modeling and docking studies is valuable method. In addition, MD simulation is also one of the most popular methods for protein structure modeling and characteristics. Here, we attempt to predict protein-protein complex structure and property using some of bioinformatic methods, and we focus botulinum toxin complex as target structure.

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

The Envelope Glycoprotein of Friend Spleen Focus-Forming Virus Covalently Interacts with and Constitutively Activates a Truncated Form of the Receptor Tyrosine Kinase Stk

PubMed Central

Nishigaki, Kazuo; Thompson, Delores; Hanson, Charlotte; Yugawa, Takashi; Ruscetti, Sandra

2001-01-01

The Friend spleen focus-forming virus (SFFV) encodes a unique envelope glycoprotein, gp55, which allows erythroid cells to proliferate and differentiate in the absence of erythropoietin (Epo). SFFV gp55 has been shown to interact with the Epo receptor complex, causing constitutive activation of various signal-transducing molecules. When injected into adult mice, SFFV induces a rapid erythroleukemia, with susceptibility being determined by the host gene Fv-2, which was recently shown to be identical to the gene encoding the receptor tyrosine kinase Stk/Ron. Susceptible, but not resistant, mice encode not only full-length Stk but also a truncated form of the kinase, sf-Stk, which may mediate the biological effects of SFFV infection. To determine whether expression of SFFV gp55 leads to the activation of sf-Stk, we expressed sf-Stk, with or without SFFV gp55, in hematopoietic cells expressing the Epo receptor. Our data indicate that sf-Stk interacts with SFFV gp55 as well as gp55P, the biologically active form of the viral glycoprotein, forming disulfide-linked complexes. This covalent interaction, as well as noncovalent interactions with SFFV gp55, results in constitutive tyrosine phosphorylation of sf-Stk and its association with multiple tyrosine-phosphorylated signal-transducing molecules. In contrast, neither Epo stimulation in the absence of SFFV gp55 expression nor expression of a mutant of SFFV that cannot interact with sf-Stk was able to induce tyrosine phosphorylation of sf-Stk or its association with any signal-transducing molecules. Covalent interaction of sf-Stk with SFFV gp55 and constitutive tyrosine phosphorylation of sf-Stk can also be detected in an erythroleukemia cell line derived from an SFFV-infected mouse. Our results suggest that SFFV gp55 may mediate its biological effects in vivo by interacting with and activating a truncated form of the receptor tyrosine kinase Stk. PMID:11483734

Programming Morphogenesis through Systems and Synthetic Biology.

PubMed

Velazquez, Jeremy J; Su, Emily; Cahan, Patrick; Ebrahimkhani, Mo R

2018-04-01

Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids. Copyright © 2017 Elsevier Ltd. All rights reserved.

In Planta Single-Molecule Pull-Down Reveals Tetrameric Stoichiometry of HD-ZIPIII:LITTLE ZIPPER Complexes.

PubMed

Husbands, Aman Y; Aggarwal, Vasudha; Ha, Taekjip; Timmermans, Marja C P

2016-08-01

Deciphering complex biological processes markedly benefits from approaches that directly assess the underlying biomolecular interactions. Most commonly used approaches to monitor protein-protein interactions typically provide nonquantitative readouts that lack statistical power and do not yield information on the heterogeneity or stoichiometry of protein complexes. Single-molecule pull-down (SiMPull) uses single-molecule fluorescence detection to mitigate these disadvantages and can quantitatively interrogate interactions between proteins and other compounds, such as nucleic acids, small molecule ligands, and lipids. Here, we establish SiMPull in plants using the HOMEODOMAIN LEUCINE ZIPPER III (HD-ZIPIII) and LITTLE ZIPPER (ZPR) interaction as proof-of-principle. Colocalization analysis of fluorophore-tagged HD-ZIPIII and ZPR proteins provides strong statistical evidence of complex formation. In addition, we use SiMPull to directly quantify YFP and mCherry maturation probabilities, showing these differ substantially from values obtained in mammalian systems. Leveraging these probabilities, in conjunction with fluorophore photobleaching assays on over 2000 individual complexes, we determined HD-ZIPIII:ZPR stoichiometry. Intriguingly, these complexes appear as heterotetramers, comprising two HD-ZIPIII and two ZPR molecules, rather than heterodimers as described in the current model. This surprising result raises new questions about the regulation of these key developmental factors and is illustrative of the unique contribution SiMPull is poised to make to in planta protein interaction studies. © 2016 American Society of Plant Biologists. All rights reserved.

Binding affinities of Schiff base Fe(II) complex with BSA and calf-thymus DNA: Spectroscopic investigations and molecular docking analysis

NASA Astrophysics Data System (ADS)

Rudra, Suparna; Dasmandal, Somnath; Patra, Chiranjit; Kundu, Arjama; Mahapatra, Ambikesh

2016-09-01

The binding interaction of a synthesized Schiff base Fe(II) complex with biological macromolecules viz., bovine serum albumin (BSA) and calf thymus(ct)-DNA have been investigated using different spectroscopic techniques coupled with viscosity measurements at physiological pH and 298 K. Regular amendments in emission intensities of BSA upon the action of the complex indicate significant interaction between them, and the binding interaction have been characterized by Stern Volmer plots and thermodynamic binding parameters. On the basis of this quenching technique one binding site with binding constant (Kb = (7.6 ± 0.21) × 105) between complex and protein have been obtained at 298 K. Time-resolved fluorescence studies have also been encountered to understand the mechanism of quenching induced by the complex. Binding affinities of the complex to the fluorophores of BSA namely tryptophan (Trp) and tyrosine (Tyr) have been judged by synchronous fluorescence studies. Secondary structural changes of BSA rooted by the complex has been revealed by CD spectra. On the other hand, hypochromicity of absorption spectra of the complex with the addition of ct-DNA and the gradual reduction in emission intensities of ethidium bromide bound ct-DNA in presence of the complex indicate noticeable interaction between ct-DNA and the complex with the binding constant (4.2 ± 0.11) × 106 M- 1. Life-time measurements have been studied to determine the relative amplitude of binding of the complex to ct-DNA base pairs. Mode of binding interaction of the complex with ct-DNA has been deciphered by viscosity measurements. CD spectra have also been used to understand the changes in ct-DNA structure upon binding with the metal complex. Density functional theory (DFT) and molecular docking analysis have been employed in highlighting the interactive phenomenon and binding location of the complex with the macromolecules.

Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks

PubMed Central

2011-01-01

Background Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context. Findings We present Functional Genomics Assistant (FUGA) - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology. Conclusion FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga. PMID:22035155

Cancer risk and the complexity of the interactions between environmental and host factors: HENVINET interactive diagrams as simple tools for exploring and understanding the scientific evidence.

PubMed

Merlo, Domenico F; Filiberti, Rosangela; Kobernus, Michael; Bartonova, Alena; Gamulin, Marija; Ferencic, Zeljko; Dusinska, Maria; Fucic, Aleksandra

2012-06-28

Development of graphical/visual presentations of cancer etiology caused by environmental stressors is a process that requires combining the complex biological interactions between xenobiotics in living and occupational environment with genes (gene-environment interaction) and genomic and non-genomic based disease specific mechanisms in living organisms. Traditionally, presentation of causal relationships includes the statistical association between exposure to one xenobiotic and the disease corrected for the effect of potential confounders. Within the FP6 project HENVINET, we aimed at considering together all known agents and mechanisms involved in development of selected cancer types. Selection of cancer types for causal diagrams was based on the corpus of available data and reported relative risk (RR). In constructing causal diagrams the complexity of the interactions between xenobiotics was considered a priority in the interpretation of cancer risk. Additionally, gene-environment interactions were incorporated such as polymorphisms in genes for repair and for phase I and II enzymes involved in metabolism of xenobiotics and their elimination. Information on possible age or gender susceptibility is also included. Diagrams are user friendly thanks to multistep access to information packages and the possibility of referring to related literature and a glossary of terms. Diagrams cover both chemical and physical agents (ionizing and non-ionizing radiation) and provide basic information on the strength of the association between type of exposure and cancer risk reported by human studies and supported by mechanistic studies. Causal diagrams developed within HENVINET project represent a valuable source of information for professionals working in the field of environmental health and epidemiology, and as educational material for students. Cancer risk results from a complex interaction of environmental exposures with inherited gene polymorphisms, genetic burden collected during development and non genomic capacity of response to environmental insults. In order to adopt effective preventive measures and the associated regulatory actions, a comprehensive investigation of cancer etiology is crucial. Variations and fluctuations of cancer incidence in human populations do not necessarily reflect environmental pollution policies or population distribution of polymorphisms of genes known to be associated with increased cancer risk. Tools which may be used in such a comprehensive research, including molecular biology applied to field studies, require a methodological shift from the reductionism that has been used until recently as a basic axiom in interpretation of data. The complexity of the interactions between cells, genes and the environment, i.e. the resonance of the living matter with the environment, can be synthesized by systems biology. Within the HENVINET project such philosophy was followed in order to develop interactive causal diagrams for the investigation of cancers with possible etiology in environmental exposure. Causal diagrams represent integrated knowledge and seed tool for their future development and development of similar diagrams for other environmentally related diseases such as asthma or sterility. In this paper development and application of causal diagrams for cancer are presented and discussed.

Art Therapy for Chronic Pain: Applications and Future Directions

ERIC Educational Resources Information Center

Angheluta, Anne-Marie; Lee, Bonnie K.

2011-01-01

Chronic pain is acknowledged as a phenomenological experience resulting from biological, psychological, and social interactions. Consequently, treatment for this complex and debilitating health phenomenon is often approached from multidisciplinary and biopsychosocial perspectives. One approach to treating chronic pain involves implementing…

Using HexSim to simulate complex species, landscape, and stressor interactions

EPA Science Inventory

Background / Question / Methods The use of simulation models in conservation biology, landscape ecology, and other disciplines is increasing. Models are essential tools for researchers who, for example, need to forecast future conditions, weigh competing recovery and mitigation...

Learning directed acyclic graphs from large-scale genomics data.

PubMed

Nikolay, Fabio; Pesavento, Marius; Kritikos, George; Typas, Nassos

2017-09-20

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Update of KDBI: Kinetic Data of Bio-molecular Interaction database

PubMed Central

Kumar, Pankaj; Han, B. C.; Shi, Z.; Jia, J.; Wang, Y. P.; Zhang, Y. T.; Liang, L.; Liu, Q. F.; Ji, Z. L.; Chen, Y. Z.

2009-01-01

Knowledge of the kinetics of biomolecular interactions is important for facilitating the study of cellular processes and underlying molecular events, and is essential for quantitative study and simulation of biological systems. Kinetic Data of Bio-molecular Interaction database (KDBI) has been developed to provide information about experimentally determined kinetic data of protein–protein, protein–nucleic acid, protein–ligand, nucleic acid–ligand binding or reaction events described in the literature. To accommodate increasing demand for studying and simulating biological systems, numerous improvements and updates have been made to KDBI, including new ways to access data by pathway and molecule names, data file in System Biology Markup Language format, more efficient search engine, access to published parameter sets of simulation models of 63 pathways, and 2.3-fold increase of data (19 263 entries of 10 532 distinctive biomolecular binding and 11 954 interaction events, involving 2635 proteins/protein complexes, 847 nucleic acids, 1603 small molecules and 45 multi-step processes). KDBI is publically available at http://bidd.nus.edu.sg/group/kdbi/kdbi.asp. PMID:18971255

Zinc complexes developed as metallopharmaceutics for treating diabetes mellitus based on the bio-medicinal inorganic chemistry.

PubMed

Yoshikawa, Yutaka; Yasui, Hiroyuki

2012-01-01

Biological trace metals such as iron, zinc, copper, and manganese are essential to life and health of humans, and the success of platinum drugs in the cancer chemotherapy has rapidly grown interest in developing inorganic pharmaceutical agents in medicinal chemistry, that is, medicinal inorganic chemistry, using essential elements and other biological trace metals. Transition metal complexes with unique chemical structures may be useful alternatives to the drugs available to address some of the incurable diseases. In this review, we emphasize that metal complexes are an expanding of interest in the research field of treatment of diabetes mellitus. Especially, orally active anti-diabetic and anti-metabolic syndrome zinc complexes have been developed and progressed since the discovery in 2001, where several highly potent anti-diabetic zinc complexes with different coordination structures have quite recently been disclosed, using experimental diabetic animals. In all of the complexes discussed, zinc is found to be biologically active and function by interacting with some target proteins related with diabetes mellitus. The design and screening of zinc complexes with higher activity is not efficient without consideration of the translational research. For the development of a clinically useful metallopharmaceutics, the research of zinc complexes on the long-term toxicity including side effects, clear-cut evidence of target molecule for the in vivo pharmacological action, and good pharmacokinetic property are essential in the current and future studies.

Combined Biology and Bioinformatics Approaches to Breast Cancer

DTIC Science & Technology

2005-04-01

interacted with the MH 1 and linker domains in both Smad3 and Smad4; no interaction was found with the MH2 domain (Fig. 7C). These data suggest that LMO4...LMO4 interacts with these Smads. GST protein-protein interaction assays showed that LMO4 binds to the MH1 and linker domain of Smad 1, Smad3 and Smad4...by facilitating the nuclear translocation and DNA-binding of a complex composed of an R-Smad (Smad2 and/or Smad3 ) and the co-Smad, Smad4 (10). To

Time to face the fats: what can mass spectrometry reveal about the structure of lipids and their interactions with proteins?

PubMed

Brown, Simon H J; Mitchell, Todd W; Oakley, Aaron J; Pham, Huong T; Blanksby, Stephen J

2012-09-01

Since the 1950s, X-ray crystallography has been the mainstay of structural biology, providing detailed atomic-level structures that continue to revolutionize our understanding of protein function. From recent advances in this discipline, a picture has emerged of intimate and specific interactions between lipids and proteins that has driven renewed interest in the structure of lipids themselves and raised intriguing questions as to the specificity and stoichiometry in lipid-protein complexes. Herein we demonstrate some of the limitations of crystallography in resolving critical structural features of ligated lipids and thus determining how these motifs impact protein binding. As a consequence, mass spectrometry must play an important and complementary role in unraveling the complexities of lipid-protein interactions. We evaluate recent advances and highlight ongoing challenges towards the twin goals of (1) complete structure elucidation of low, abundant, and structurally diverse lipids by mass spectrometry alone, and (2) assignment of stoichiometry and specificity of lipid interactions within protein complexes.

Time to Face the Fats: What Can Mass Spectrometry Reveal about the Structure of Lipids and Their Interactions with Proteins?

NASA Astrophysics Data System (ADS)

Brown, Simon H. J.; Mitchell, Todd W.; Oakley, Aaron J.; Pham, Huong T.; Blanksby, Stephen J.

2012-09-01

Since the 1950s, X-ray crystallography has been the mainstay of structural biology, providing detailed atomic-level structures that continue to revolutionize our understanding of protein function. From recent advances in this discipline, a picture has emerged of intimate and specific interactions between lipids and proteins that has driven renewed interest in the structure of lipids themselves and raised intriguing questions as to the specificity and stoichiometry in lipid-protein complexes. Herein we demonstrate some of the limitations of crystallography in resolving critical structural features of ligated lipids and thus determining how these motifs impact protein binding. As a consequence, mass spectrometry must play an important and complementary role in unraveling the complexities of lipid-protein interactions. We evaluate recent advances and highlight ongoing challenges towards the twin goals of (1) complete structure elucidation of low, abundant, and structurally diverse lipids by mass spectrometry alone, and (2) assignment of stoichiometry and specificity of lipid interactions within protein complexes.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems.

PubMed

Williams, Richard A; Timmis, Jon; Qwarnstrom, Eva E

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems

PubMed Central

Timmis, Jon; Qwarnstrom, Eva E.

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model. PMID:27571414

Graphics processing units in bioinformatics, computational biology and systems biology.

PubMed

Nobile, Marco S; Cazzaniga, Paolo; Tangherloni, Andrea; Besozzi, Daniela

2017-09-01

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools. © The Author 2016. Published by Oxford University Press.

A Synthetic Biology Framework for Programming Eukaryotic Transcription Functions

PubMed Central

Khalil, Ahmad S.; Lu, Timothy K.; Bashor, Caleb J.; Ramirez, Cherie L.; Pyenson, Nora C.; Joung, J. Keith; Collins, James J.

2013-01-01

SUMMARY Eukaryotic transcription factors (TFs) perform complex and combinatorial functions within transcriptional networks. Here, we present a synthetic framework for systematically constructing eukaryotic transcription functions using artificial zinc fingers, modular DNA-binding domains found within many eukaryotic TFs. Utilizing this platform, we construct a library of orthogonal synthetic transcription factors (sTFs) and use these to wire synthetic transcriptional circuits in yeast. We engineer complex functions, such as tunable output strength and transcriptional cooperativity, by rationally adjusting a decomposed set of key component properties, e.g., DNA specificity, affinity, promoter design, protein-protein interactions. We show that subtle perturbations to these properties can transform an individual sTF between distinct roles (activator, cooperative factor, inhibitory factor) within a transcriptional complex, thus drastically altering the signal processing behavior of multi-input systems. This platform provides new genetic components for synthetic biology and enables bottom-up approaches to understanding the design principles of eukaryotic transcriptional complexes and networks. PMID:22863014

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology.

PubMed

Cankorur-Cetinkaya, Ayca; Dias, Joao M L; Kludas, Jana; Slater, Nigel K H; Rousu, Juho; Oliver, Stephen G; Dikicioglu, Duygu

2017-06-01

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).

Screening of Small Molecule Interactor Library by Using In-Cell NMR Spectroscopy (SMILI-NMR)

PubMed Central

Xie, Jingjing; Thapa, Rajiv; Reverdatto, Sergey; Burz, David S.; Shekhtman, Alexander

2011-01-01

We developed an in-cell NMR assay for screening small molecule interactor libraries (SMILI-NMR) for compounds capable of disrupting or enhancing specific interactions between two or more components of a biomolecular complex. The method relies on the formation of a well-defined biocomplex and utilizes in-cell NMR spectroscopy to identify the molecular surfaces involved in the interaction at atomic scale resolution. Changes in the interaction surface caused by a small molecule interfering with complex formation are used as a read-out of the assay. The in-cell nature of the experimental protocol insures that the small molecule is capable of penetrating the cell membrane and specifically engaging the target molecule(s). Utility of the method was demonstrated by screening a small dipeptide library against the FKBP–FRB protein complex involved in cell cycle arrest. The dipeptide identified by SMILI-NMR showed biological activity in a functional assay in yeast. PMID:19422228

A Novel Algorithm for Detecting Protein Complexes with the Breadth First Search

PubMed Central

Tang, Xiwei; Wang, Jianxin; Li, Min; He, Yiming; Pan, Yi

2014-01-01

Most biological processes are carried out by protein complexes. A substantial number of false positives of the protein-protein interaction (PPI) data can compromise the utility of the datasets for complexes reconstruction. In order to reduce the impact of such discrepancies, a number of data integration and affinity scoring schemes have been devised. The methods encode the reliabilities (confidence) of physical interactions between pairs of proteins. The challenge now is to identify novel and meaningful protein complexes from the weighted PPI network. To address this problem, a novel protein complex mining algorithm ClusterBFS (Cluster with Breadth-First Search) is proposed. Based on the weighted density, ClusterBFS detects protein complexes of the weighted network by the breadth first search algorithm, which originates from a given seed protein used as starting-point. The experimental results show that ClusterBFS performs significantly better than the other computational approaches in terms of the identification of protein complexes. PMID:24818139

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

Biomedically relevant chemical and physical properties of coal combustion products.

PubMed Central

Fisher, G L

1983-01-01

The evaluation of the potential public and occupational health hazards of developing and existing combustion processes requires a detailed understanding of the physical and chemical properties of effluents available for human and environmental exposures. These processes produce complex mixtures of gases and aerosols which may interact synergistically or antagonistically with biological systems. Because of the physicochemical complexity of the effluents, the biomedically relevant properties of these materials must be carefully assessed. Subsequent to release from combustion sources, environmental interactions further complicate assessment of the toxicity of combustion products. This report provides an overview of the biomedically relevant physical and chemical properties of coal fly ash. Coal fly ash is presented as a model complex mixture for health and safety evaluation of combustion processes. PMID:6337824

Cellular automata with object-oriented features for parallel molecular network modeling.

PubMed

Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

2005-06-01

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

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?

Defining the human deubiquitinating enzyme interaction landscape.

PubMed

Sowa, Mathew E; Bennett, Eric J; Gygi, Steven P; Harper, J Wade

2009-07-23

Deubiquitinating enzymes (Dubs) function to remove covalently attached ubiquitin from proteins, thereby controlling substrate activity and/or abundance. For most Dubs, their functions, targets, and regulation are poorly understood. To systematically investigate Dub function, we initiated a global proteomic analysis of Dubs and their associated protein complexes. This was accomplished through the development of a software platform called CompPASS, which uses unbiased metrics to assign confidence measurements to interactions from parallel nonreciprocal proteomic data sets. We identified 774 candidate interacting proteins associated with 75 Dubs. Using Gene Ontology, interactome topology classification, subcellular localization, and functional studies, we link Dubs to diverse processes, including protein turnover, transcription, RNA processing, DNA damage, and endoplasmic reticulum-associated degradation. This work provides the first glimpse into the Dub interaction landscape, places previously unstudied Dubs within putative biological pathways, and identifies previously unknown interactions and protein complexes involved in this increasingly important arm of the ubiquitin-proteasome pathway.

Defining the Human Deubiquitinating Enzyme Interaction Landscape

PubMed Central

Sowa, Mathew E.; Bennett, Eric J.; Gygi, Steven P.; Harper, J. Wade

2009-01-01

Summary Deubiquitinating enzymes (Dubs) function to remove covalently attached ubiquitin from proteins, thereby controlling substrate activity and/or abundance. For most Dubs, their functions, targets, and regulation are poorly understood. To systematically investigate Dub function, we initiated a global proteomic analysis of Dubs and their associated protein complexes. This was accomplished through the development of a software platform, called CompPASS, which uses unbiased metrics to assign confidence measurements to interactions from parallel non-reciprocal proteomic datasets. We identified 774 candidate interacting proteins associated with 75 Dubs. Using Gene Ontology, interactome topology classification, sub-cellular localization and functional studies, we link Dubs to diverse processes, including protein turnover, transcription, RNA processing, DNA damage, and endoplasmic reticulum-associated degradation. This work provides the first glimpse into the Dub interaction landscape, places previously unstudied Dubs within putative biological pathways, and identifies previously unknown interactions and protein complexes involved in this increasingly important arm of the ubiquitin-proteasome pathway. PMID:19615732

Dual-Color Click Beetle Luciferase Heteroprotein Fragment Complementation Assays

PubMed Central

Villalobos, Victor; Naik, Snehal; Bruinsma, Monique; Dothager, Robin S.; Pan, Mei-Hsiu; Samrakandi, Mustapha; Moss, Britney; Elhammali, Adnan; Piwnica-Worms, David

2010-01-01

Summary Understanding the functional complexity of protein interactions requires mapping biomolecular complexes within the cellular environment over biologically-relevant time scales. Herein we describe a novel set of reversible, multicolored heteroprotein complementation fragments based on various firefly and click beetle luciferases that utilize the same substrate, D-luciferin. Luciferase heteroprotein fragment complementation systems enabled dual-color quantification of two discreet pairs of interacting proteins simultaneously or two distinct proteins interacting with a third shared protein in live cells. Using real-time analysis of click beetle green and click beetle red luciferase heteroprotein fragment complementation applied to β-TrCP, an E3-ligase common to the regulation of both β-catenin and IκBα, GSK3β was identified as a novel candidate kinase regulating IκBα processing. These dual-color protein interaction switches may enable directed dynamic analysis of a variety of protein interactions in living cells. PMID:20851351

Plasma membrane--cortical cytoskeleton interactions: a cell biology approach with biophysical considerations.

PubMed

Kapus, András; Janmey, Paul

2013-07-01

From a biophysical standpoint, the interface between the cell membrane and the cytoskeleton is an intriguing site where a "two-dimensional fluid" interacts with an exceedingly complex three-dimensional protein meshwork. The membrane is a key regulator of the cytoskeleton, which not only provides docking sites for cytoskeletal elements through transmembrane proteins, lipid binding-based, and electrostatic interactions, but also serves as the source of the signaling events and molecules that control cytoskeletal organization and remolding. Conversely, the cytoskeleton is a key determinant of the biophysical and biochemical properties of the membrane, including its shape, tension, movement, composition, as well as the mobility, partitioning, and recycling of its constituents. From a cell biological standpoint, the membrane-cytoskeleton interplay underlies--as a central executor and/or regulator--a multitude of complex processes including chemical and mechanical signal transduction, motility/migration, endo-/exo-/phagocytosis, and other forms of membrane traffic, cell-cell, and cell-matrix adhesion. The aim of this article is to provide an overview of the tight structural and functional coupling between the membrane and the cytoskeleton. As biophysical approaches, both theoretical and experimental, proved to be instrumental for our understanding of the membrane/cytoskeleton interplay, this review will "oscillate" between the cell biological phenomena and the corresponding biophysical principles and considerations. After describing the types of connections between the membrane and the cytoskeleton, we will focus on a few key physical parameters and processes (force generation, curvature, tension, and surface charge) and will discuss how these contribute to a variety of fundamental cell biological functions. © 2013 American Physiological Society.

Gcn4-Mediator Specificity Is Mediated by a Large and Dynamic Fuzzy Protein-Protein Complex.

PubMed

Tuttle, Lisa M; Pacheco, Derek; Warfield, Linda; Luo, Jie; Ranish, Jeff; Hahn, Steven; Klevit, Rachel E

2018-03-20

Transcription activation domains (ADs) are inherently disordered proteins that often target multiple coactivator complexes, but the specificity of these interactions is not understood. Efficient transcription activation by yeast Gcn4 requires its tandem ADs and four activator-binding domains (ABDs) on its target, the Mediator subunit Med15. Multiple ABDs are a common feature of coactivator complexes. We find that the large Gcn4-Med15 complex is heterogeneous and contains nearly all possible AD-ABD interactions. Gcn4-Med15 forms via a dynamic fuzzy protein-protein interface, where ADs bind the ABDs in multiple orientations via hydrophobic regions that gain helicity. This combinatorial mechanism allows individual low-affinity and specificity interactions to generate a biologically functional, specific, and higher affinity complex despite lacking a defined protein-protein interface. This binding strategy is likely representative of many activators that target multiple coactivators, as it allows great flexibility in combinations of activators that can cooperate to regulate genes with variable coactivator requirements. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Classification of self-injurious behaviour across the continuum of relative environmental-biological influence.

PubMed

Hagopian, L P; Frank-Crawford, M A

2017-10-13

Self-injurious behaviour (SIB) is generally considered to be the product of interactions between dysfunction stemming from the primary developmental disability and experiences that occasion and reinforce SIB. As a result of these complex interactions, SIB presents as a heterogeneous problem. Recent research delineating subtypes of SIB that are nonsocially mediated, including one that is amenable to change and one that is highly invariant, enables classification of SIB across a broader continuum of relative environmental-biological influence. Directly examining how the functional classes of SIB differ has the potential to structure research, will improve our understanding this problem, and lead to more targeted behavioural and pharmacological interventions. Recognising that SIB is not a single entity but is composed of distinct functional classes would better align research with conceptual models that view SIB as the product of interactions between environmental and biological variables. © 2017 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

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.

The cell biology of Tobacco mosaic virus replication and movement

PubMed Central

Liu, Chengke; Nelson, Richard S.

2013-01-01

Successful systemic infection of a plant by Tobacco mosaic virus (TMV) requires three processes that repeat over time: initial establishment and accumulation in invaded cells, intercellular movement, and systemic transport. Accumulation and intercellular movement of TMV necessarily involves intracellular transport by complexes containing virus and host proteins and virus RNA during a dynamic process that can be visualized. Multiple membranes appear to assist TMV accumulation, while membranes, microfilaments and microtubules appear to assist TMV movement. Here we review cell biological studies that describe TMV-membrane, -cytoskeleton, and -other host protein interactions which influence virus accumulation and movement in leaves and callus tissue. The importance of understanding the developmental phase of the infection in relationship to the observed virus-membrane or -host protein interaction is emphasized. Utilizing the latest observations of TMV-membrane and -host protein interactions within our evolving understanding of the infection ontogeny, a model for TMV accumulation and intracellular spread in a cell biological context is provided. PMID:23403525

WE-H-BRA-04: Biological Geometries for the Monte Carlo Simulation Toolkit TOPASNBio

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

McNamara, A; Held, K; Paganetti, H

2016-06-15

Purpose: New advances in radiation therapy are most likely to come from the complex interface of physics, chemistry and biology. Computational simulations offer a powerful tool for quantitatively investigating radiation interactions with biological tissue and can thus help bridge the gap between physics and biology. The aim of TOPAS-nBio is to provide a comprehensive tool to generate advanced radiobiology simulations. Methods: TOPAS wraps and extends the Geant4 Monte Carlo (MC) simulation toolkit. TOPAS-nBio is an extension to TOPAS which utilizes the physics processes in Geant4-DNA to model biological damage from very low energy secondary electrons. Specialized cell, organelle and molecularmore » geometries were designed for the toolkit. Results: TOPAS-nBio gives the user the capability of simulating biological geometries, ranging from the micron-scale (e.g. cells and organelles) to complex nano-scale geometries (e.g. DNA and proteins). The user interacts with TOPAS-nBio through easy-to-use input parameter files. For example, in a simple cell simulation the user can specify the cell type and size as well as the type, number and size of included organelles. For more detailed nuclear simulations, the user can specify chromosome territories containing chromatin fiber loops, the later comprised of nucleosomes on a double helix. The chromatin fibers can be arranged in simple rigid geometries or within factual globules, mimicking realistic chromosome territories. TOPAS-nBio also provides users with the capability of reading protein data bank 3D structural files to simulate radiation damage to proteins or nucleic acids e.g. histones or RNA. TOPAS-nBio has been validated by comparing results to other track structure simulation software and published experimental measurements. Conclusion: TOPAS-nBio provides users with a comprehensive MC simulation tool for radiobiological simulations, giving users without advanced programming skills the ability to design and run complex simulations.« less

Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

PubMed

Yang, Lei; Zhao, Xudong; Tang, Xianglong

2014-01-01

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

HExpoChem: a systems biology resource to explore human exposure to chemicals.

PubMed

Taboureau, Olivier; Jacobsen, Ulrik Plesner; Kalhauge, Christian; Edsgärd, Daniel; Rigina, Olga; Gupta, Ramneek; Audouze, Karine

2013-05-01

Humans are exposed to diverse hazardous chemicals daily. Although an exposure to these chemicals is suspected to have adverse effects on human health, mechanistic insights into how they interact with the human body are still limited. Therefore, acquisition of curated data and development of computational biology approaches are needed to assess the health risks of chemical exposure. Here we present HExpoChem, a tool based on environmental chemicals and their bioactivities on human proteins with the objective of aiding the qualitative exploration of human exposure to chemicals. The chemical-protein interactions have been enriched with a quality-scored human protein-protein interaction network, a protein-protein association network and a chemical-chemical interaction network, thus allowing the study of environmental chemicals through formation of protein complexes and phenotypic outcomes enrichment. HExpoChem is available at http://www.cbs.dtu.dk/services/HExpoChem-1.0/.

The simulation approach to lipid-protein interactions.

PubMed

Paramo, Teresa; Garzón, Diana; Holdbrook, Daniel A; Khalid, Syma; Bond, Peter J

2013-01-01

The interactions between lipids and proteins are crucial for a range of biological processes, from the folding and stability of membrane proteins to signaling and metabolism facilitated by lipid-binding proteins. However, high-resolution structural details concerning functional lipid/protein interactions are scarce due to barriers in both experimental isolation of native lipid-bound complexes and subsequent biophysical characterization. The molecular dynamics (MD) simulation approach provides a means to complement available structural data, yielding dynamic, structural, and thermodynamic data for a protein embedded within a physiologically realistic, modelled lipid environment. In this chapter, we provide a guide to current methods for setting up and running simulations of membrane proteins and soluble, lipid-binding proteins, using standard atomistically detailed representations, as well as simplified, coarse-grained models. In addition, we outline recent studies that illustrate the power of the simulation approach in the context of biologically relevant lipid/protein interactions.

Piezoelectric tuning fork biosensors for the quantitative measurement of biomolecular interactions

NASA Astrophysics Data System (ADS)

Gonzalez, Laura; Rodrigues, Mafalda; Benito, Angel Maria; Pérez-García, Lluïsa; Puig-Vidal, Manel; Otero, Jorge

2015-12-01

The quantitative measurement of biomolecular interactions is of great interest in molecular biology. Atomic force microscopy (AFM) has proved its capacity to act as a biosensor and determine the affinity between biomolecules of interest. Nevertheless, the detection scheme presents certain limitations when it comes to developing a compact biosensor. Recently, piezoelectric quartz tuning forks (QTFs) have been used as laser-free detection sensors for AFM. However, only a few studies along these lines have considered soft biological samples, and even fewer constitute quantified molecular recognition experiments. Here, we demonstrate the capacity of QTF probes to perform specific interaction measurements between biotin-streptavidin complexes in buffer solution. We propose in this paper a variant of dynamic force spectroscopy based on representing adhesion energies E (aJ) against pulling rates v (nm s-1). Our results are compared with conventional AFM measurements and show the great potential of these sensors in molecular interaction studies.

Potential Interference of Protein-Protein Interactions by Graphyne.

PubMed

Luan, Binquan; Huynh, Tien; Zhou, Ruhong

2016-03-10

Graphyne has attracted tremendous attention recently due to its many potentially superior properties relative to those of graphene. Although extensive efforts have been devoted to explore the applicability of graphyne as an alternative nanomaterial for state-of-the-art nanotechnology (including biomedical applications), knowledge regarding its possible adverse effects to biological cells is still lacking. Here, using large-scale all-atom molecular dynamics simulations, we investigate the potential toxicity of graphyne by interfering a protein-protein interaction (ppI). We found that graphyne could indeed disrupt the ppIs by cutting through the protein-protein interface and separating the protein complex into noncontacting ones, due to graphyne's dispersive and hydrophobic interaction with the hydrophobic residues residing at the dimer interface. Our results help to elucidate the mechanism of interaction between graphyne and ppI networks within a biological cell and provide insights for its hazard reduction.

The Importance of Normalization on Large and Heterogeneous Microarray Datasets

EPA Science Inventory

DNA microarray technology is a powerful functional genomics tool increasingly used for investigating global gene expression in environmental studies. Microarrays can also be used in identifying biological networks, as they give insight on the complex gene-to-gene interactions, ne...

SYMPOSIUM SESSION PROPOSAL: INCORPORATION OF MODE OF ACTION INTO MECHANISTICALLY-BASED QUANTITATIVE MODELS

EPA Science Inventory

The biological processes by which environmental pollutants induce adverse health effects is most likely regulated by complex interactions dependent upon the route of exposure, dose, kinetics of distribution, and multiple cellular responses. To further complicate deciphering thes...

Heat Map Visualization of Complex Environmental and Biomarker Measurements

EPA Science Inventory

Over the past decade, the assessment of human systems interactions with the environment has permeated all phases of environmental and public health research. We are invoking lessons learned from the broad discipline of Systems Biology research that focuses primarily on molecular ...

Using titanium complexes to defeat cancer: the view from the shoulders of titans.

PubMed

Cini, Melchior; Bradshaw, Tracey D; Woodward, Simon

2017-02-20

When the first titanium complex with anticancer activity was identified in the 1970s, it was attractive, based on the presence of the dichloride unit in TiCl 2 Cp 2 (Cp = η-C 5 H 5 ) 2 , to assume its mode of biological action was closely aligned with cisplatin [cis-PtCl 2 (NH 3 ) 2 ]. Over the intervening 40 years however a far more complicated picture has arisen indicating multiple cellular mechanisms of cellular action can be triggered by titanium anti-cancer agents. This tutorial review aims to unpick the historical data and provide new researchers, without an explicit cancer biology background, a contemporary interpretation of both older and newer literature and to review the best techniques for attaining the identities of the biologically active titanium species and how these interact with the cancer cellular machinery.

Metal based biologically active compounds: Design, synthesis, DNA binding and antidiabetic activity of 6-methyl-3-formyl chromone derived hydrazones and their metal (II) complexes.

PubMed

Philip, Jessica Elizabeth; Shahid, Muhammad; Prathapachandra Kurup, M R; Velayudhan, Mohanan Puzhavoorparambil

2017-10-01

Two chromone hydrazone ligands HL 1 and HL 2 were synthesized and characterized by elemental analyses, IR, 1 H NMR & 13 C NMR, electronic absorption and mass spectra. The reactions of the chromone hydrazones with transition metals such as Ni, Cu, and Zn (II) salts of acetate afforded mononuclear metal complexes. Characterization and structure elucidation of the prepared chromone hydrazone metal (II) complexes were done by elemental, IR, electronic, EPR spectra and thermo gravimetric analyses as well as conductivity and magnetic susceptibility measurements. The spectroscopic data showed that the ligand acts as a mono basic bidentate with coordination sites are azomethine nitrogen and hydrazonic oxygen, and they exhibited distorted geometry. The biological studies involved antidiabetic activity i.e. enzyme inhibition of α-amylase and α-glucosidase, Calf Thymus - DNA (CT-DNA) interaction and molecular docking. Potential capacity of synthesized compounds to inhibit the α-amylase and α-glucosidase activity was assayed whereas DNA interaction studies were carried out with the help UV-Vis absorption titration and viscosity method. The docking studies of chromone hydrazones show that they are minor groove binders. Complexes were found to be good DNA - intercalates. Chromone hydrazones and its transition metal complexes have shown comparable antidiabetic activity with a standard drug acarbose. Copyright © 2017 Elsevier B.V. All rights reserved.

Multispectroscopic DNA-Binding studies and antimicrobial evaluation of new mixed-ligand Silver(I) complex and nanocomplex: A comparative study

NASA Astrophysics Data System (ADS)

Movahedi, Elaheh; Rezvani, Ali Reza

2018-05-01

A novel mixed-ligand Ag(I) complex, , has been synthesized and characterized by the elemental analysis, IR spectroscopy and 1HNMR. In the formula, dian and phen are N-(4,5-diazafluoren-9-ylidene)aniline and 1,10-phenanthroline, respectively. This complex also has been prepared at nano size by sonochemical technique and characterized by the FTIR and scanning electron microscopy (SEM). To evaluate the biological preferences of the Ag(I) complex and nanocomplex and verify the relationships between the structure and biological function, in vitro DNA binding and antibacterial experiments have been carried out. DNA-complex interaction has been pursued by electronic absorption titration, luminescence titration, competitive binding experiment, effect of ionic strength, thermodynamic studies, viscometric evaluation and circular dichroism spectroscopy in the physiological pH. Each compound displays significant binding trend to the CT-DNA. The mode of binding to the CT-DNA probably is a moderate intercalation mode with the partial insertion of the planar ligands between the base stacks of double-stranded DNA. The relative viscosities and circular dichroism spectra of the CT-DNA with the complex solutions, confirm the intense interactions of the Ag(I) complex and nanocomplex with DNA. An in vitro antibacterial test of the complex and nanocomplex on a series of the Gram-positive bacteria (Staphylococcus aureus, Enterococcus faecalis) and the Gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa) shows a remarkable antibacterial feature of the Ag(I) complex. The MIC values (minimum inhibitory concentration) of the compounds compare with silver nitrate and silver sulfadiazine. The bacterial inhibitions of the Ag(I) complex and nanocomplex are agreed to their DNA binding affinities.

Multi-level and hybrid modelling approaches for systems biology.

PubMed

Bardini, R; Politano, G; Benso, A; Di Carlo, S

2017-01-01

During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.

Numerical Methods of Computational Electromagnetics for Complex Inhomogeneous Systems

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

Cai, Wei

Understanding electromagnetic phenomena is the key in many scientific investigation and engineering designs such as solar cell designs, studying biological ion channels for diseases, and creating clean fusion energies, among other things. The objectives of the project are to develop high order numerical methods to simulate evanescent electromagnetic waves occurring in plasmon solar cells and biological ion-channels, where local field enhancement within random media in the former and long range electrostatic interactions in the latter are of major challenges for accurate and efficient numerical computations. We have accomplished these objectives by developing high order numerical methods for solving Maxwell equationsmore » such as high order finite element basis for discontinuous Galerkin methods, well-conditioned Nedelec edge element method, divergence free finite element basis for MHD, and fast integral equation methods for layered media. These methods can be used to model the complex local field enhancement in plasmon solar cells. On the other hand, to treat long range electrostatic interaction in ion channels, we have developed image charge based method for a hybrid model in combining atomistic electrostatics and continuum Poisson-Boltzmann electrostatics. Such a hybrid model will speed up the molecular dynamics simulation of transport in biological ion-channels.« less

Back to the biology in systems biology: what can we learn from biomolecular networks?

PubMed

Huang, Sui

2004-02-01

Genome-scale molecular networks, including protein interaction and gene regulatory networks, have taken centre stage in the investigation of the burgeoning disciplines of systems biology and biocomplexity. What do networks tell us? Some see in networks simply the comprehensive, detailed description of all cellular pathways, others seek in networks simple, higher-order qualities that emerge from the collective action of the individual pathways. This paper discusses networks from an encompassing category of thinking that will hopefully help readers to bridge the gap between these polarised viewpoints. Systems biology so far has emphasised the characterisation of large pathway maps. Now one has to ask: where is the actual biology in 'systems biology'? As structures midway between genome and phenome, and by serving as an 'extended genotype' or an 'elementary phenotype', molecular networks open a new window to the study of evolution and gene function in complex living systems. For the study of evolution, features in network topology offer a novel starting point for addressing the old debate on the relative contributions of natural selection versus intrinsic constraints to a particular trait. To study the function of genes, it is necessary not only to see them in the context of gene networks, but also to reach beyond describing network topology and to embrace the global dynamics of networks that will reveal higher-order, collective behaviour of the interacting genes. This will pave the way to understanding how the complexity of genome-wide molecular networks collapses to produce a robust whole-cell behaviour that manifests as tightly-regulated switching between distinct cell fates - the basis for multicellular life.

Analysis of DNA interactions using single-molecule force spectroscopy.

PubMed

Ritzefeld, Markus; Walhorn, Volker; Anselmetti, Dario; Sewald, Norbert

2013-06-01

Protein-DNA interactions are involved in many biochemical pathways and determine the fate of the corresponding cell. Qualitative and quantitative investigations on these recognition and binding processes are of key importance for an improved understanding of biochemical processes and also for systems biology. This review article focusses on atomic force microscopy (AFM)-based single-molecule force spectroscopy and its application to the quantification of forces and binding mechanisms that lead to the formation of protein-DNA complexes. AFM and dynamic force spectroscopy are exciting tools that allow for quantitative analysis of biomolecular interactions. Besides an overview on the method and the most important immobilization approaches, the physical basics of the data evaluation is described. Recent applications of AFM-based force spectroscopy to investigate DNA intercalation, complexes involving DNA aptamers and peptide- and protein-DNA interactions are given.

Complexity of generic biochemical circuits: topology versus strength of interactions.

PubMed

Tikhonov, Mikhail; Bialek, William

2016-12-06

The historical focus on network topology as a determinant of biological function is still largely maintained today, illustrated by the rise of structure-only approaches to network analysis. However, biochemical circuits and genetic regulatory networks are defined both by their topology and by a multitude of continuously adjustable parameters, such as the strength of interactions between nodes, also recognized as important. Here we present a class of simple perceptron-based Boolean models within which comparing the relative importance of topology versus interaction strengths becomes a quantitatively well-posed problem. We quantify the intuition that for generic networks, optimization of interaction strengths is a crucial ingredient of achieving high complexity, defined here as the number of fixed points the network can accommodate. We propose a new methodology for characterizing the relative role of parameter optimization for topologies of a given class.

oGNM: online computation of structural dynamics using the Gaussian Network Model

PubMed Central

Yang, Lee-Wei; Rader, A. J.; Liu, Xiong; Jursa, Cristopher Jon; Chen, Shann Ching; Karimi, Hassan A.; Bahar, Ivet

2006-01-01

An assessment of the equilibrium dynamics of biomolecular systems, and in particular their most cooperative fluctuations accessible under native state conditions, is a first step towards understanding molecular mechanisms relevant to biological function. We present a web-based system, oGNM that enables users to calculate online the shape and dispersion of normal modes of motion for proteins, oligonucleotides and their complexes, or associated biological units, using the Gaussian Network Model (GNM). Computations with the new engine are 5–6 orders of magnitude faster than those using conventional normal mode analyses. Two cases studies illustrate the utility of oGNM. The first shows that the thermal fluctuations predicted for 1250 non-homologous proteins correlate well with X-ray crystallographic data over a broad range [7.3–15 Å] of inter-residue interaction cutoff distances and the correlations improve with increasing observation temperatures. The second study, focused on 64 oligonucleotides and oligonucleotide–protein complexes, shows that good agreement with experiments is achieved by representing each nucleotide by three GNM nodes (as opposed to one-node-per-residue in proteins) along with uniform interaction ranges for all components of the complexes. These results open the way to a rapid assessment of the dynamics of DNA/RNA-containing complexes. The server can be accessed at . PMID:16845002

Decoding the complex genetic causes of heart diseases using systems biology.

PubMed

Djordjevic, Djordje; Deshpande, Vinita; Szczesnik, Tomasz; Yang, Andrian; Humphreys, David T; Giannoulatou, Eleni; Ho, Joshua W K

2015-03-01

The pace of disease gene discovery is still much slower than expected, even with the use of cost-effective DNA sequencing and genotyping technologies. It is increasingly clear that many inherited heart diseases have a more complex polygenic aetiology than previously thought. Understanding the role of gene-gene interactions, epigenetics, and non-coding regulatory regions is becoming increasingly critical in predicting the functional consequences of genetic mutations identified by genome-wide association studies and whole-genome or exome sequencing. A systems biology approach is now being widely employed to systematically discover genes that are involved in heart diseases in humans or relevant animal models through bioinformatics. The overarching premise is that the integration of high-quality causal gene regulatory networks (GRNs), genomics, epigenomics, transcriptomics and other genome-wide data will greatly accelerate the discovery of the complex genetic causes of congenital and complex heart diseases. This review summarises state-of-the-art genomic and bioinformatics techniques that are used in accelerating the pace of disease gene discovery in heart diseases. Accompanying this review, we provide an interactive web-resource for systems biology analysis of mammalian heart development and diseases, CardiacCode ( http://CardiacCode.victorchang.edu.au/ ). CardiacCode features a dataset of over 700 pieces of manually curated genetic or molecular perturbation data, which enables the inference of a cardiac-specific GRN of 280 regulatory relationships between 33 regulator genes and 129 target genes. We believe this growing resource will fill an urgent unmet need to fully realise the true potential of predictive and personalised genomic medicine in tackling human heart disease.

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

Bimolecular fluorescence complementation: visualization of molecular interactions in living cells.

PubMed

Kerppola, Tom K

2008-01-01

A variety of experimental methods have been developed for the analysis of protein interactions. The majority of these methods either require disruption of the cells to detect molecular interactions or rely on indirect detection of the protein interaction. The bimolecular fluorescence complementation (BiFC) assay provides a direct approach for the visualization of molecular interactions in living cells and organisms. The BiFC approach is based on the facilitated association between two fragments of a fluorescent protein when the fragments are brought together by an interaction between proteins fused to the fragments. The BiFC approach has been used for visualization of interactions among a variety of structurally diverse interaction partners in many different cell types. It enables detection of transient complexes as well as complexes formed by a subpopulation of the interaction partners. It is essential to include negative controls in each experiment in which the interface between the interaction partners has been mutated or deleted. The BiFC assay has been adapted for simultaneous visualization of multiple protein complexes in the same cell and the competition for shared interaction partners. A ubiquitin-mediated fluorescence complementation assay has also been developed for visualization of the covalent modification of proteins by ubiquitin family peptides. These fluorescence complementation assays have a great potential to illuminate a variety of biological interactions in the future.

IN VITRO AND IN VIVO ACTIVITY OF A LYMPHOCYTE AND IMMUNE COMPLEX-DEPENDENT CHEMOTACTIC FACTOR FOR EOSINOPHILS

PubMed Central

Cohen, Stanley; Ward, Peter A.

1971-01-01

When cultured in the presence of specific antigen, lymphocytes from delayed-hypersensitive guinea pigs release a number of biologically active substances into the culture medium. Such active supernatants can react with immune complexes in vitro to generate a factor which is chemotactic for eosinophils. The factor involved is unique, since previously described chemotactic factors for other cell types require for their generation either immune complexes or substances released into lymphocyte culture, but not both. In the case of the eosinophil chemotactic factor, the interaction between the substance elaborated by the lymphocytes and the immune complexes appears to be specific in that the immune complexes must contain the same antigen as that used to activate the lymphocyte cultures. Although this factor was generated in an in vitro system, it has been shown to possess in vivo as well as in vitro activity. It is therefore possible that this factor may be of biological significance in situations where eosinophils are participants in inflammatory or immunologic reactions. PMID:5099667

CrosstalkNet: A Visualization Tool for Differential Co-expression Networks and Communities.

PubMed

Manem, Venkata; Adam, George Alexandru; Gruosso, Tina; Gigoux, Mathieu; Bertos, Nicholas; Park, Morag; Haibe-Kains, Benjamin

2018-04-15

Variations in physiological conditions can rewire molecular interactions between biological compartments, which can yield novel insights into gain or loss of interactions specific to perturbations of interest. Networks are a promising tool to elucidate intercellular interactions, yet exploration of these large-scale networks remains a challenge due to their high dimensionality. To retrieve and mine interactions, we developed CrosstalkNet, a user friendly, web-based network visualization tool that provides a statistical framework to infer condition-specific interactions coupled with a community detection algorithm for bipartite graphs to identify significantly dense subnetworks. As a case study, we used CrosstalkNet to mine a set of 54 and 22 gene-expression profiles from breast tumor and normal samples, respectively, with epithelial and stromal compartments extracted via laser microdissection. We show how CrosstalkNet can be used to explore large-scale co-expression networks and to obtain insights into the biological processes that govern cross-talk between different tumor compartments. Significance: This web application enables researchers to mine complex networks and to decipher novel biological processes in tumor epithelial-stroma cross-talk as well as in other studies of intercompartmental interactions. Cancer Res; 78(8); 2140-3. ©2018 AACR . ©2018 American Association for Cancer Research.

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.

Biological responses to engineered nanomaterials: Needs for the next decade

DOE PAGES

Murphy, Catherine J.; Vartanian, Ariane M.; Geiger, Franz M.; ...

2015-06-09

In this study, the interaction of nanomaterials with biomolecules, cells, and organisms is an enormously vital area of current research, with applications in nanoenabled diagnostics, imaging agents, therapeutics, and contaminant removal technologies. Yet the potential for adverse biological and environmental impacts of nanomaterial exposure is considerable and needs to be addressed to ensure sustainable development of nanomaterials. In this Outlook four research needs for the next decade are outlined: (i) measurement of the chemical nature of nanomaterials in dynamic, complex aqueous environments; (ii) real-time measurements of nanomaterial-biological interactions with chemical specificity; (iii) delineation of molecular modes of action for nanomaterialmore » effects on living systems as functions of nanomaterial properties; and (iv) an integrated systems approach that includes computation and simulation across orders of magnitude in time and space.« less

Integrative, Dynamic Structural Biology at Atomic Resolution—It’s About Time

PubMed Central

van den Bedem, Henry; Fraser, James S.

2015-01-01

Biomolecules adopt a dynamic ensemble of conformations, each with the potential to interact with binding partners or perform the chemical reactions required for a multitude of cellular functions. Recent advances in X-ray crystallography, Nuclear Magnetic Resonance (NMR) spectroscopy, and other techniques are helping us realize the dream of seeing—in atomic detail—how different parts of biomolecules exchange between functional sub-states using concerted motions. Integrative structural biology has advanced our understanding of the formation of large macromolecular complexes and how their components interact in assemblies by leveraging data from many low-resolution methods. Here, we review the growing opportunities for integrative, dynamic structural biology at the atomic scale, contending there is increasing synergistic potential between X-ray crystallography, NMR, and computer simulations to reveal a structural basis for protein conformational dynamics at high resolution. PMID:25825836

Quantitative Proteomics Reveals Dynamic Interactions of the Minichromosome Maintenance Complex (MCM) in the Cellular Response to Etoposide Induced DNA Damage.

PubMed

Drissi, Romain; Dubois, Marie-Line; Douziech, Mélanie; Boisvert, François-Michel

2015-07-01

The minichromosome maintenance complex (MCM) proteins are required for processive DNA replication and are a target of S-phase checkpoints. The eukaryotic MCM complex consists of six proteins (MCM2-7) that form a heterohexameric ring with DNA helicase activity, which is loaded on chromatin to form the pre-replication complex. Upon entry in S phase, the helicase is activated and opens the DNA duplex to recruit DNA polymerases at the replication fork. The MCM complex thus plays a crucial role during DNA replication, but recent work suggests that MCM proteins could also be involved in DNA repair. Here, we employed a combination of stable isotope labeling with amino acids in cell culture (SILAC)-based quantitative proteomics with immunoprecipitation of green fluorescent protein-tagged fusion proteins to identify proteins interacting with the MCM complex, and quantify changes in interactions in response to DNA damage. Interestingly, the MCM complex showed very dynamic changes in interaction with proteins such as Importin7, the histone chaperone ASF1, and the Chromodomain helicase DNA binding protein 3 (CHD3) following DNA damage. These changes in interactions were accompanied by an increase in phosphorylation and ubiquitination on specific sites on the MCM proteins and an increase in the co-localization of the MCM complex with γ-H2AX, confirming the recruitment of these proteins to sites of DNA damage. In summary, our data indicate that the MCM proteins is involved in chromatin remodeling in response to DNA damage. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Mosaic, self-similarity logic, and biological attraction principles: three explanatory instruments in biology.

PubMed

Agnati, Luigi F; Baluska, Frantisek; Barlow, Peter W; Guidolin, Diego

2009-11-01

From a structural standpoint, living organisms are organized like a nest of Russian matryoshka dolls, in which structures are buried within one another. From a temporal point of view, this type of organization is the result of a history comprised of a set of time backcloths which have accompanied the passage of living matter from its origins up to the present day. The aim of the present paper is to indicate a possible course of this 'passage through time, and suggest how today's complexity has been reached by living organisms. This investigation will employ three conceptual tools, namely the Mosaic, Self-Similarity Logic, and the Biological Attraction principles. Self-Similarity Logic indicates the self-consistency by which elements of a living system interact, irrespective of the spatiotemporal level under consideration. The term Mosaic indicates how, from the same set of elements assembled according to different patterns, it is possible to arrive at completely different constructions: hence, each system becomes endowed with different emergent properties. The Biological Attraction principle states that there is an inherent drive for association and merging of compatible elements at all levels of biological complexity. By analogy with the gravitation law in physics, biological attraction is based on the evidence that each living organism creates an attractive field around itself. This field acts as a sphere of influence that actively attracts similar fields of other biological systems, thereby modifying salient features of the interacting organisms. Three specific organizational levels of living matter, namely the molecular, cellular, and supracellular levels, have been considered in order to analyse and illustrate the interpretative as well as the predictive roles of each of these three explanatory principles.

Profiling cellular protein complexes by proximity ligation with dual tag microarray readout.

PubMed

Hammond, Maria; Nong, Rachel Yuan; Ericsson, Olle; Pardali, Katerina; Landegren, Ulf

2012-01-01

Patterns of protein interactions provide important insights in basic biology, and their analysis plays an increasing role in drug development and diagnostics of disease. We have established a scalable technique to compare two biological samples for the levels of all pairwise interactions among a set of targeted protein molecules. The technique is a combination of the proximity ligation assay with readout via dual tag microarrays. In the proximity ligation assay protein identities are encoded as DNA sequences by attaching DNA oligonucleotides to antibodies directed against the proteins of interest. Upon binding by pairs of antibodies to proteins present in the same molecular complexes, ligation reactions give rise to reporter DNA molecules that contain the combined sequence information from the two DNA strands. The ligation reactions also serve to incorporate a sample barcode in the reporter molecules to allow for direct comparison between pairs of samples. The samples are evaluated using a dual tag microarray where information is decoded, revealing which pairs of tags that have become joined. As a proof-of-concept we demonstrate that this approach can be used to detect a set of five proteins and their pairwise interactions both in cellular lysates and in fixed tissue culture cells. This paper provides a general strategy to analyze the extent of any pairwise interactions in large sets of molecules by decoding reporter DNA strands that identify the interacting molecules.

Potentiometric and spectroscopic study of the interaction of 3d transition metal ions with inositol hexakisphosphate

NASA Astrophysics Data System (ADS)

Veiga, Nicolás; Macho, Israel; Gómez, Kerman; González, Gabriel; Kremer, Carlos; Torres, Julia

2015-10-01

Among myo-inositol phosphates, the most abundant in nature is the myo-inositol hexakisphosphate, InsP6. Although it is known to be vital to cell functioning, the biochemical research into its metabolism needs chemical and structural analysis of all the protonation, complexation and precipitation processes that it undergoes in the biological media. In view of its high negative charge at physiological level, our group has been leading a thorough research into the InsP6 chemical and structural behavior in the presence of the alkali and alkaline earth metal ions essential for life. The aim of this article is to extend these studies, dealing with the chemical and structural features of the InsP6 interaction with biologically relevant 3d transition metal ions (Fe(II), Fe(III), Mn(II), Co(II), Ni(II), Cu(II) and Zn(II)), in a non-interacting medium and under simulated physiological conditions. The metal-complex stability constants were determined by potentiometry, showing under ligand-excess conditions the formation of mononuclear species in different protonation states. Under metal ion excess, polymetallic species were detected for Fe(II), Fe(III), Zn(II) and Cu(II). Additionally, the 31P NMR and UV-vis spectroscopic studies provided interesting structural aspects of the strong metal ion-InsP6 interaction.

Online Interactive Teaching Modules Enhance Quantitative Proficiency of Introductory Biology Students

PubMed Central

Nelson, Kären C.; Marbach-Ad, Gili; Keller, Michael; Fagan, William F.

2010-01-01

There is widespread agreement within the scientific and education communities that undergraduate biology curricula fall short in providing students with the quantitative and interdisciplinary problem-solving skills they need to obtain a deep understanding of biological phenomena and be prepared fully to contribute to future scientific inquiry. MathBench Biology Modules were designed to address these needs through a series of interactive, Web-based modules that can be used to supplement existing course content across the biological sciences curriculum. The effect of the modules was assessed in an introductory biology course at the University of Maryland. Over the course of the semester, students showed significant increases in quantitative skills that were independent of previous math course work. Students also showed increased comfort with solving quantitative problems, whether or not they ultimately arrived at the correct answer. A survey of spring 2009 graduates indicated that those who had experienced MathBench in their course work had a greater appreciation for the role of mathematics in modern biology than those who had not used MathBench. MathBench modules allow students from diverse educational backgrounds to hone their quantitative skills, preparing them for more complex mathematical approaches in upper-division courses. PMID:20810959

Online interactive teaching modules enhance quantitative proficiency of introductory biology students.

PubMed

Thompson, Katerina V; Nelson, Kären C; Marbach-Ad, Gili; Keller, Michael; Fagan, William F

2010-01-01

There is widespread agreement within the scientific and education communities that undergraduate biology curricula fall short in providing students with the quantitative and interdisciplinary problem-solving skills they need to obtain a deep understanding of biological phenomena and be prepared fully to contribute to future scientific inquiry. MathBench Biology Modules were designed to address these needs through a series of interactive, Web-based modules that can be used to supplement existing course content across the biological sciences curriculum. The effect of the modules was assessed in an introductory biology course at the University of Maryland. Over the course of the semester, students showed significant increases in quantitative skills that were independent of previous math course work. Students also showed increased comfort with solving quantitative problems, whether or not they ultimately arrived at the correct answer. A survey of spring 2009 graduates indicated that those who had experienced MathBench in their course work had a greater appreciation for the role of mathematics in modern biology than those who had not used MathBench. MathBench modules allow students from diverse educational backgrounds to hone their quantitative skills, preparing them for more complex mathematical approaches in upper-division courses.

Aqueous Molecular Dynamics Simulations of the M. tuberculosis Enoyl-ACP Reductase-NADH System and Its Complex with a Substrate Mimic or Diphenyl Ethers Inhibitors

PubMed Central

da Silva Lima, Camilo Henrique; de Alencastro, Ricardo Bicca; Kaiser, Carlos Roland; de Souza, Marcus Vinícius Nora; Rodrigues, Carlos Rangel; Albuquerque, Magaly Girão

2015-01-01

Molecular dynamics (MD) simulations of 12 aqueous systems of the NADH-dependent enoyl-ACP reductase from Mycobacterium tuberculosis (InhA) were carried out for up to 20–40 ns using the GROMACS 4.5 package. Simulations of the holoenzyme, holoenzyme-substrate, and 10 holoenzyme-inhibitor complexes were conducted in order to gain more insight about the secondary structure motifs of the InhA substrate-binding pocket. We monitored the lifetime of the main intermolecular interactions: hydrogen bonds and hydrophobic contacts. Our MD simulations demonstrate the importance of evaluating the conformational changes that occur close to the active site of the enzyme-cofactor complex before and after binding of the ligand and the influence of the water molecules. Moreover, the protein-inhibitor total steric (ELJ) and electrostatic (EC) interaction energies, related to Gly96 and Tyr158, are able to explain 80% of the biological response variance according to the best linear equation, pKi = 7.772 − 0.1885 × Gly96 + 0.0517 × Tyr158 (R2 = 0.80; n = 10), where interactions with Gly96, mainly electrostatic, increase the biological response, while those with Tyr158 decrease. These results will help to understand the structure-activity relationships and to design new and more potent anti-TB drugs. PMID:26457706

Topology of molecular interaction networks.

PubMed

Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick

2013-09-16

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Topology of molecular interaction networks

PubMed Central

2013-01-01

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

Interaction with Single-stranded DNA-binding Protein Stimulates Escherichia coli Ribonuclease HI Enzymatic Activity.

PubMed

Petzold, Christine; Marceau, Aimee H; Miller, Katherine H; Marqusee, Susan; Keck, James L

2015-06-05

Single-stranded (ss) DNA-binding proteins (SSBs) bind and protect ssDNA intermediates formed during replication, recombination, and repair reactions. SSBs also directly interact with many different genome maintenance proteins to stimulate their enzymatic activities and/or mediate their proper cellular localization. We have identified an interaction formed between Escherichia coli SSB and ribonuclease HI (RNase HI), an enzyme that hydrolyzes RNA in RNA/DNA hybrids. The RNase HI·SSB complex forms by RNase HI binding the intrinsically disordered C terminus of SSB (SSB-Ct), a mode of interaction that is shared among all SSB interaction partners examined to date. Residues that comprise the SSB-Ct binding site are conserved among bacterial RNase HI enzymes, suggesting that RNase HI·SSB complexes are present in many bacterial species and that retaining the interaction is important for its cellular function. A steady-state kinetic analysis shows that interaction with SSB stimulates RNase HI activity by lowering the reaction Km. SSB or RNase HI protein variants that disrupt complex formation nullify this effect. Collectively our findings identify a direct RNase HI/SSB interaction that could play a role in targeting RNase HI activity to RNA/DNA hybrid substrates within the genome. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Biologically-Based Lumping Methodology (BBLM) To Investigate Toxicological Interactions of Complex Chemical Mixtures

EPA Science Inventory

Many cases of environmental contamination result in concurrent or sequential exposure to more than one chemical. Limitations of available resources prevent experimental toxicology from providing health risk information about all the possible mixtures to which humans or other spec...

Mechanistic Indicators of Childhood Asthma (MICA): piloting an integrative design for evaluating environmental health

EPA Science Inventory

Background: Modem methods in molecular biology and advanced computational tools show promise in elucidating complex interactions that occur between genes and environmental factors in diseases such as asthma; however appropriately designed studies are critical for these methods to...

Parent–Child Interactions, Peripheral Serotonin, and Self-Inflicted Injury in Adolescents

PubMed Central

Crowell, Sheila E.; Beauchaine, Theodore P.; McCauley, Elizabeth; Smith, Cindy J.; Vasilev, Christina A.; Stevens, Adrianne L.

2009-01-01

Self-inflicted injury in adolescence indicates significant emotional and psychological suffering. Although data on the etiology of self-injury are limited, current theories suggest that the emotional lability observed among self-injuring adolescents results from complex interactions between individual biological vulnerabilities and environmental risk. For example, deficiencies in serotonergic functioning, in conjunction with certain family interaction patterns, may contribute to the development of emotional lability and risk for self-injury. The authors explored the relation between peripheral serotonin levels and mother–child interaction patterns among typical (n = 21) and self-injuring (n = 20) adolescents. Findings revealed higher levels of negative affect and lower levels of both positive affect and cohesiveness among families of self-injuring participants. Peripheral serotonin was also correlated with the expression of positive affect within dyads. Furthermore, adolescents’ serotonin levels interacted with negativity and conflict within dyads to explain 64% of the variance in self-injury. These findings underscore the importance of considering both biological and environmental risk factors in understanding and treating self-injuring adolescents. PMID:18229978

Three-body system metaphor for the two-slit experiment and Escherichia coli lactose-glucose metabolism.

PubMed

Asano, Masanari; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2016-05-28

We compare the contextual probabilistic structures of the seminal two-slit experiment (quantum interference experiment), the system of three interacting bodies andEscherichia colilactose-glucose metabolism. We show that they have the same non-Kolmogorov probabilistic structure resulting from multi-contextuality. There are plenty of statistical data with non-Kolmogorov features; in particular, the probabilistic behaviour of neither quantum nor biological systems can be described classically. Biological systems (even cells and proteins) are macroscopic systems and one may try to present a more detailed model of interactions in such systems that lead to quantum-like probabilistic behaviour. The system of interactions between three bodies is one of the simplest metaphoric examples for such interactions. By proceeding further in this way (by playing withn-body systems) we shall be able to find metaphoric mechanical models for complex bio-interactions, e.g. signalling between cells, leading to non-Kolmogorov probabilistic data. © 2016 The Author(s).

Three-body system metaphor for the two-slit experiment and Escherichia coli lactose–glucose metabolism

PubMed Central

Asano, Masanari; Ohya, Masanori; Yamato, Ichiro

2016-01-01

We compare the contextual probabilistic structures of the seminal two-slit experiment (quantum interference experiment), the system of three interacting bodies and Escherichia coli lactose–glucose metabolism. We show that they have the same non-Kolmogorov probabilistic structure resulting from multi-contextuality. There are plenty of statistical data with non-Kolmogorov features; in particular, the probabilistic behaviour of neither quantum nor biological systems can be described classically. Biological systems (even cells and proteins) are macroscopic systems and one may try to present a more detailed model of interactions in such systems that lead to quantum-like probabilistic behaviour. The system of interactions between three bodies is one of the simplest metaphoric examples for such interactions. By proceeding further in this way (by playing with n-body systems) we shall be able to find metaphoric mechanical models for complex bio-interactions, e.g. signalling between cells, leading to non-Kolmogorov probabilistic data. PMID:27091163

Viruses and miRNAs: More Friends than Foes.

PubMed

Bruscella, Patrice; Bottini, Silvia; Baudesson, Camille; Pawlotsky, Jean-Michel; Feray, Cyrille; Trabucchi, Michele

2017-01-01

There is evidence that eukaryotic miRNAs (hereafter called host miRNAs) play a role in the replication and propagation of viruses. Expression or targeting of host miRNAs can be involved in cellular antiviral responses. Most times host miRNAs play a role in viral life-cycles and promote infection through complex regulatory pathways. miRNAs can also be encoded by a viral genome and be expressed in the host cell. Viral miRNAs can share common sequences with host miRNAs or have totally different sequences. They can regulate a variety of biological processes involved in viral infection, including apoptosis, evasion of the immune response, or modulation of viral life-cycle phases. Overall, virus/miRNA pathway interaction is defined by a plethora of complex mechanisms, though not yet fully understood. This article review summarizes recent advances and novel biological concepts related to the understanding of miRNA expression, control and function during viral infections. The article also discusses potential therapeutic applications of this particular host-pathogen interaction.

Viruses and miRNAs: More Friends than Foes

PubMed Central

Bruscella, Patrice; Bottini, Silvia; Baudesson, Camille; Pawlotsky, Jean-Michel; Feray, Cyrille; Trabucchi, Michele

2017-01-01

There is evidence that eukaryotic miRNAs (hereafter called host miRNAs) play a role in the replication and propagation of viruses. Expression or targeting of host miRNAs can be involved in cellular antiviral responses. Most times host miRNAs play a role in viral life-cycles and promote infection through complex regulatory pathways. miRNAs can also be encoded by a viral genome and be expressed in the host cell. Viral miRNAs can share common sequences with host miRNAs or have totally different sequences. They can regulate a variety of biological processes involved in viral infection, including apoptosis, evasion of the immune response, or modulation of viral life-cycle phases. Overall, virus/miRNA pathway interaction is defined by a plethora of complex mechanisms, though not yet fully understood. This article review summarizes recent advances and novel biological concepts related to the understanding of miRNA expression, control and function during viral infections. The article also discusses potential therapeutic applications of this particular host–pathogen interaction. PMID:28555130

Brownian motion of tethered nanowires.

PubMed

Ota, Sadao; Li, Tongcang; Li, Yimin; Ye, Ziliang; Labno, Anna; Yin, Xiaobo; Alam, Mohammad-Reza; Zhang, Xiang

2014-05-01

Brownian motion of slender particles near a boundary is ubiquitous in biological systems and in nanomaterial assembly, but the complex hydrodynamic interaction in those systems is still poorly understood. Here, we report experimental and computational studies of the Brownian motion of silicon nanowires tethered on a substrate. An optical interference method enabled direct observation of microscopic rotations of the slender bodies in three dimensions with high angular and temporal resolutions. This quantitative observation revealed anisotropic and angle-dependent hydrodynamic wall effects: rotational diffusivity in inclined and azimuth directions follows different power laws as a function of the length, ∼ L(-2.5) and ∼ L(-3), respectively, and is more hindered for smaller inclined angles. In parallel, we developed an implicit simulation technique that takes the complex wire-wall hydrodynamic interactions into account efficiently, the result of which agreed well with the experimentally observed angle-dependent diffusion. The demonstrated techniques provide a platform for studying the microrheology of soft condensed matters, such as colloidal and biological systems near interfaces, and exploring the optimal self-assembly conditions of nanostructures.

Integrating Epigenomics into the Understanding of Biomedical Insight.

PubMed

Han, Yixing; He, Ximiao

2016-01-01

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

Integrating Epigenomics into the Understanding of Biomedical Insight

PubMed Central

Han, Yixing; He, Ximiao

2016-01-01

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics. PMID:27980397

Virtual immunology: software for teaching basic immunology.

PubMed

Berçot, Filipe Faria; Fidalgo-Neto, Antônio Augusto; Lopes, Renato Matos; Faggioni, Thais; Alves, Luiz Anastácio

2013-01-01

As immunology continues to evolve, many educational methods have found difficulty in conveying the degree of complexity inherent in its basic principles. Today, the teaching-learning process in such areas has been improved with tools such as educational software. This article introduces "Virtual Immunology," a software program available free of charge in Portuguese and English, which can be used by teachers and students in physiology, immunology, and cellular biology classes. We discuss the development of the initial two modules: "Organs and Lymphoid Tissues" and "Inflammation" and the use of interactive activities to provide microscopic and macroscopic understanding in immunology. Students, both graduate and undergraduate, were questioned along with university level professors about the quality of the software and intuitiveness of use, facility of navigation, and aesthetic organization using a Likert scale. An overwhelmingly satisfactory result was obtained with both students and immunology teachers. Programs such as "Virtual Immunology" are offering more interactive, multimedia approaches to complex scientific principles that increase student motivation, interest, and comprehension. © 2013 by The International Union of Biochemistry and Molecular Biology.

The landscape context of cereal aphid–parasitoid interactions

PubMed Central

Thies, Carsten; Roschewitz, Indra; Tscharntke, Teja

2005-01-01

Analyses at multiple spatial scales may show how important ecosystem services such as biological control are determined by processes acting on the landscape scale. We examined cereal aphid–parasitoid interactions in wheat fields in agricultural landscapes differing in structural complexity (32–100% arable land). Complex landscapes were associated with increased aphid mortality resulting from parasitism, but also with higher aphid colonization, thereby counterbalancing possible biological control by parasitoids and lastly resulting in similar aphid densities across landscapes. Thus, undisturbed perennial habitats appeared to enhance both pests and natural enemies. Analyses at multiple spatial scales (landscape sectors of 0.5–6 km diameter) showed that correlations between parasitism and percentage of arable land were significant at scales of 0.5–2 km, whereas aphid densities responded to percentage of arable land at scales of 1–6 km diameter. Hence, the higher trophic level populations appeared to be determined by smaller landscape sectors owing to dispersal limitation, showing the ‘functional spatial scale’ for species-specific landscape management. PMID:15695212

Dynamic protein interaction networks and new structural paradigms in signaling

PubMed Central

Csizmok, Veronika; Follis, Ariele Viacava; Kriwacki, Richard W.; Forman-Kay, Julie D.

2017-01-01

Understanding signaling and other complex biological processes requires elucidating the critical roles of intrinsically disordered proteins and regions (IDPs/IDRs), which represent ~30% of the proteome and enable unique regulatory mechanisms. In this review we describe the structural heterogeneity of disordered proteins that underpins these mechanisms and the latest progress in obtaining structural descriptions of ensembles of disordered proteins that are needed for linking structure and dynamics to function. We describe the diverse interactions of IDPs that can have unusual characteristics such as “ultrasensitivity” and “regulated folding and unfolding”. We also summarize the mounting data showing that large-scale assembly and protein phase separation occurs within a variety of signaling complexes and cellular structures. In addition, we discuss efforts to therapeutically target disordered proteins with small molecules. Overall, we interpret the remodeling of disordered state ensembles due to binding and post-translational modifications within an expanded framework for allostery that provides significant insights into how disordered proteins transmit biological information. PMID:26922996

Comparative study of binding interactions between porphyrin systems and aromatic compounds of biological importance by multiple spectroscopic techniques: A review

NASA Astrophysics Data System (ADS)

Makarska-Bialokoz, Magdalena

2018-07-01

The specific spectroscopic and redox properties of porphyrins predestine them to fulfill the role of sensors during interacting with different biologically active substances. Monitoring of binding interactions in the systems porphyrin-biologically active compound is a key question not only in the field of physiological functions of living organisms, but also in environmental protection, notably in the light of the rapidly growing drug consumption and concurrently the production of drug effluents. Not always beneficial action of drugs on natural porphyrin systems induces to further studies, with commercially available porphyrins as the model systems. Therefore the binding process between several water-soluble porphyrins and a series of biologically active compounds (e.g. caffeine, guanine, theophylline, theobromine, xanthine, uric acid) has been studied in different aqueous solutions analyzing their absorption and steady-state fluorescence spectra, the porphyrin fluorescence lifetimes and their quantum yields. The magnitude of the binding and fluorescence quenching constants values for particular quenchers decreases in a series: uric acid > guanine > caffeine > theophylline > theobromine > xanthine. In all the systems studied there are characters of static quenching, as a consequence of the π-π-stacked non-covalent and non-fluorescent complexes formation between porphyrins and interacting compounds, accompanied simultaneously by the additional specific binding interactions. The porphyrin fluorescence quenching can be explain by the photoinduced intermolecular electron transfer from aromatic compound to the center of the porphyrin molecule, playing the role of the binding site. Presented results can be valuable for designing of new fluorescent porphyrin chemosensors or monitoring of drug traces in aqueous solutions. The obtained outcomes have also the toxicological and medical importance, providing insight into the interactions of the water-soluble porphyrins with biologically active substances.

Anatomy and Physiology of Multiscale Modeling and Simulation in Systems Medicine.

PubMed

Mizeranschi, Alexandru; Groen, Derek; Borgdorff, Joris; Hoekstra, Alfons G; Chopard, Bastien; Dubitzky, Werner

2016-01-01

Systems medicine is the application of systems biology concepts, methods, and tools to medical research and practice. It aims to integrate data and knowledge from different disciplines into biomedical models and simulations for the understanding, prevention, cure, and management of complex diseases. Complex diseases arise from the interactions among disease-influencing factors across multiple levels of biological organization from the environment to molecules. To tackle the enormous challenges posed by complex diseases, we need a modeling and simulation framework capable of capturing and integrating information originating from multiple spatiotemporal and organizational scales. Multiscale modeling and simulation in systems medicine is an emerging methodology and discipline that has already demonstrated its potential in becoming this framework. The aim of this chapter is to present some of the main concepts, requirements, and challenges of multiscale modeling and simulation in systems medicine.

Using phosphine ligands with a biological role to modulate reactivity in novel platinum complexes

NASA Astrophysics Data System (ADS)

Echeverri, Marcelo; Alvarez-Valdés, Amparo; Navas, Francisco; Perles, Josefina; Sánchez-Pérez, Isabel; Quiroga, A. G.

2018-02-01

Three platinum complexes with cis and trans configuration cis-[Pt(TCEP)2Cl2], cis-[Pt(tmTCEP)2Cl2] and trans-[Pt(TCEP)2Cl2], where TCEP is tris(2-carboxyethyl)phosphine, have been synthesized and fully characterized by usual techniques including single-crystal X-ray diffraction for trans-[Pt(TCEP)2Cl2] and cis-[Pt(tmTCEP)2Cl2]. Here, we also report on an esterification process of TCEP, which takes place in the presence of alcohols, leading to a platinum complex coordinated to an ester tmTCEP (2-methoxycarbonylethyl phosphine) ligand. The stability in solution of the three compounds and their interaction with biological models such as DNA (pBR322 and calf thymus DNA) and proteins (lysozyme and RNase) have also been studied.

Synthesis, characterization, DNA/protein interaction and cytotoxicity studies of Cu(II) and Co(II) complexes derived from dipyridyl triazole ligands

NASA Astrophysics Data System (ADS)

Zhang, Wei; Yao, Di; Wei, Yi; Tang, Jie; Bian, He-Dong; Huang, Fu-Ping; Liang, Hong

2016-06-01

Four different transition metal complexes containing dipyridyl triazole ligands, namely [Cu(abpt)2Cl2]·2H2O (1), [Cu(abpt)2(ClO4)2] (2), [Co2(abpt)2(H2O)2Cl2]·Cl2·4H2O (3) and [Co2(Hbpt)2(CH3OH)2(NO3)2] (4) have been designed, synthesized and further structurally characterized by X-ray crystallography, ESI-MS, elemental analysis, IR and Raman spectroscopy. In these complexes, the both ligands act as bidentate ligands with N, N donors. DNA binding interactions with calf thymus DNA (ct-DNA) of the ligand and its complexes 1 ~ 4 were investigated via electronic absorption, fluorescence quenching, circular dichroism and viscosity measurements as well as confocal Laser Raman spectroscopy. The results show these complexes are able to bind to DNA via the non-covalent mode i.e. intercalation and groove binding or electrostatic interactions. The interactions with bovine serum albumin (BSA) were also studied using UV-Vis and fluorescence spectroscopic methods which indicated that fluorescence quenching of BSA by these compounds was the presence of both static and dynamic quenching. Moreover, the in vitro cytotoxic effects of the complexes against four cell lines SK-OV-3, HL-7702, BEL7404 and NCI-H460 showed the necessity of the coordination action on the biological properties on the respective complex and that all four complexes exhibited substantial cytotoxic activity.

Detection and characterization of protein interactions in vivo by a simple live-cell imaging method.

PubMed

Gallego, Oriol; Specht, Tanja; Brach, Thorsten; Kumar, Arun; Gavin, Anne-Claude; Kaksonen, Marko

2013-01-01

Over the last decades there has been an explosion of new methodologies to study protein complexes. However, most of the approaches currently used are based on in vitro assays (e.g. nuclear magnetic resonance, X-ray, electron microscopy, isothermal titration calorimetry etc). The accurate measurement of parameters that define protein complexes in a physiological context has been largely limited due to technical constrains. Here, we present PICT (Protein interactions from Imaging of Complexes after Translocation), a new method that provides a simple fluorescence microscopy readout for the study of protein complexes in living cells. We take advantage of the inducible dimerization of FK506-binding protein (FKBP) and FKBP-rapamycin binding (FRB) domain to translocate protein assemblies to membrane associated anchoring platforms in yeast. In this assay, GFP-tagged prey proteins interacting with the FRB-tagged bait will co-translocate to the FKBP-tagged anchor sites upon addition of rapamycin. The interactions are thus encoded into localization changes and can be detected by fluorescence live-cell imaging under different physiological conditions or upon perturbations. PICT can be automated for high-throughput studies and can be used to quantify dissociation rates of protein complexes in vivo. In this work we have used PICT to analyze protein-protein interactions from three biological pathways in the yeast Saccharomyces cerevisiae: Mitogen-activated protein kinase cascade (Ste5-Ste11-Ste50), exocytosis (exocyst complex) and endocytosis (Ede1-Syp1).

Photoactive platinum(II) complexes of nonsteroidal anti-inflammatory drug naproxen: Interaction with biological targets, antioxidant activity and cytotoxicity.

PubMed

Srivastava, Payal; Singh, Khushbu; Verma, Madhu; Sivakumar, Sri; Patra, Ashis K

2018-01-20

The effect on the therapeutic efficacy of Pt(II) complexes on combining non-steroidal anti-inflammatory drugs (NSAIDs) is an attractive strategy to circumvent chronic inflammation mediated by cancer and metastasis. Two square-planar platinum(II) complexes: [Pt(dach)(nap)Cl] (1) and [Pt(dach)(nap) 2 ] (2), where dach = (1R,2R)-dichloro(cyclohexane-1,2-diamine) and NSAID drug naproxen (nap), have been designed for studying their biological activity. The naproxen bound to the Pt(II) centre get released upon photoirradiation with low-power UV-A light as confirmed by the significant enhancement in emission intensities of the complexes. The compounds were evaluated for their photophysical properties, photostability, reactivity with 5'-guanosine monophophosphate (5'-GMP), interactions with CT-DNA and BSA, antioxidant activity and reactive oxygen species mediated photo-induced DNA damage properties. ESI-MS studies demonstrated the formation of bis-adduct with 5'-GMP and the formation of Pt II -DNA crosslinks by gel electrophoretic mobility shift assay and ITC studies. The interaction of the complexes 1 and 2 with the CT-DNA exhibits potential binding affinity (K b  ∼ 10 4  M -1 , K app ∼ 10 5  M -1 ), implying intercalation to CT-DNA through planar naphthyl ring of the complexes. Both the complexes also exhibit strong binding affinity towards BSA (K BSA ∼ 10 5  M -1 ). The complexes exhibit efficient DNA damage activity on irradiation at 365 nm via formation of singlet oxygen ( 1 O 2 ) and hydroxyl radical ( • OH) under physiological conditions. Both the complexes were cytotoxic in dark and exhibit significant enhancement of cytotoxicity upon photo-exposure against HeLa and HepG2 cancer cells giving IC 50 values ranging from 8 to 12 μM for 1 and 2. The cellular internalization data showed cytosolic and nuclear localization of the complexes in the HeLa cells. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Matrix metalloproteinase-10/TIMP-2 structure and analyses define conserved core interactions and diverse exosite interactions in MMP/TIMP complexes.

PubMed

Batra, Jyotica; Soares, Alexei S; Mehner, Christine; Radisky, Evette S

2013-01-01

Matrix metalloproteinases (MMPs) play central roles in vertebrate tissue development, remodeling, and repair. The endogenous tissue inhibitors of metalloproteinases (TIMPs) regulate proteolytic activity by binding tightly to the MMP active site. While each of the four TIMPs can inhibit most MMPs, binding data reveal tremendous heterogeneity in affinities of different TIMP/MMP pairs, and the structural features that differentiate stronger from weaker complexes are poorly understood. Here we report the crystal structure of the comparatively weakly bound human MMP-10/TIMP-2 complex at 2.1 Å resolution. Comparison with previously reported structures of MMP-3/TIMP-1, MT1-MMP/TIMP-2, MMP-13/TIMP-2, and MMP-10/TIMP-1 complexes offers insights into the structural basis of binding selectivity. Our analyses identify a group of highly conserved contacts at the heart of MMP/TIMP complexes that define the conserved mechanism of inhibition, as well as a second category of diverse adventitious contacts at the periphery of the interfaces. The AB loop of the TIMP N-terminal domain and the contact loops of the TIMP C-terminal domain form highly variable peripheral contacts that can be considered as separate exosite interactions. In some complexes these exosite contacts are extensive, while in other complexes the AB loop or C-terminal domain contacts are greatly reduced and appear to contribute little to complex stability. Our data suggest that exosite interactions can enhance MMP/TIMP binding, although in the relatively weakly bound MMP-10/TIMP-2 complex they are not well optimized to do so. Formation of highly variable exosite interactions may provide a general mechanism by which TIMPs are fine-tuned for distinct regulatory roles in biology.

Preferential binding of positive nanoparticles on cell membranes is due to electrostatic interactions: A too simplistic explanation that does not take into account the nanoparticle protein corona.

PubMed

Forest, Valérie; Pourchez, Jérémie

2017-01-01

The internalization of nanoparticles by cells (and more broadly the nanoparticle/cell interaction) is a crucial issue both for biomedical applications (for the design of nanocarriers with enhanced cellular uptake to reach their intracellular therapeutic targets) and in a nanosafety context (as the internalized dose is one of the key factors in cytotoxicity). Many parameters can influence the nanoparticle/cell interaction, among them, the nanoparticle physico-chemical features, and especially the surface charge. It is generally admitted that positive nanoparticles are more uptaken by cells than neutral or negative nanoparticles. It is supposedly due to favorable electrostatic interactions with negatively charged cell membrane. However, this theory seems too simplistic as it does not consider a fundamental element: the nanoparticle protein corona. Indeed, once introduced in a biological medium nanoparticles adsorb proteins at their surface, forming a new interface defining the nanoparticle "biological identity". This adds a new level of complexity in the interactions with biological systems that cannot be any more limited to electrostatic binding. These interactions will then influence cell behavior. Based on a literature review and on an example of our own experience the parameters involved in the nanoparticle protein corona formation as well as in the nanoparticle/cell interactions are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Apical External Root Resorption and Repair in Orthodontic Tooth Movement: Biological Events.

PubMed

Feller, Liviu; Khammissa, Razia A G; Thomadakis, George; Fourie, Jeanine; Lemmer, Johan

2016-01-01

Some degree of external root resorption is a frequent, unpredictable, and unavoidable consequence of orthodontic tooth movement mediated by odontoclasts/cementoclasts originating from circulating precursor cells in the periodontal ligament. Its pathogenesis involves mechanical forces initiating complex interactions between signalling pathways activated by various biological agents. Resorption of cementum is regulated by mechanisms similar to those controlling osteoclastogenesis and bone resorption. Following root resorption there is repair by cellular cementum, but factors mediating the transition from resorption to repair are not clear. In this paper we review some of the biological events associated with orthodontically induced external root resorption.

nES GEMMA Analysis of Lectins and Their Interactions with Glycoproteins - Separation, Detection, and Sampling of Noncovalent Biospecific Complexes

NASA Astrophysics Data System (ADS)

Engel, Nicole Y.; Weiss, Victor U.; Marchetti-Deschmann, Martina; Allmaier, Günter

2017-01-01

In order to better understand biological events, lectin-glycoprotein interactions are of interest. The possibility to gather more information than the mere positive or negative response for interactions brought mass spectrometry into the center of many research fields. The presented work shows the potential of a nano-electrospray gas-phase electrophoretic mobility molecular analyzer (nES GEMMA) to detect weak, noncovalent, biospecific interactions besides still unbound glycoproteins and unreacted lectins without prior liquid phase separation. First results for Sambucus nigra agglutinin, concanavalin A, and wheat germ agglutinin and their retained noncovalent interactions with glycoproteins in the gas phase are presented. Electrophoretic mobility diameters (EMDs) were obtained by nES GEMMA for all interaction partners correlating very well with molecular masses determined by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) of the individual molecules. Moreover, EMDs measured for the lectin-glycoprotein complexes were in good accordance with theoretically calculated mass values. Special focus was laid on complex formation for different lectin concentrations and binding specificities to evaluate the method with respect to results obtained in the liquid phase. The latter was addressed by capillary electrophoresis on-a-chip (CE-on-a-chip). Of exceptional interest was the fact that the formed complexes could be sampled according to their size onto nitrocellulose membranes after gas-phase separation. Subsequent immunological investigation further proved that the collected complex actually retained its native structure throughout nES GEMMA analysis and sampling.

Glycoconjugates in New World species of Leishmania: polymorphisms in lipophosphoglycan and glycoinositolphospholipids and interaction with hosts.

PubMed

de Assis, Rafael Ramiro; Ibraim, Izabela Coimbra; Nogueira, Paula Monalisa; Soares, Rodrigo Pedro; Turco, Salvatore J

2012-09-01

Protozoan parasites of the genus Leishmania cause a number of important diseases in humans and undergo a complex life cycle, alternating between a sand fly vector and vertebrate hosts. The parasites have a remarkable capacity to avoid destruction in which surface molecules are determinant for survival. Amongst the many surface molecules of Leishmania, the glycoconjugates are known to play a central role in host-parasite interactions and are the focus of this review. The most abundant and best studied glycoconjugates are the Lipophosphoglycans (LPGs) and glycoinositolphospholipids (GIPLs). This review summarizes the main studies on structure and biological functions of these molecules in New World Leishmania species. LPG and GIPLs are complex molecules that display inter- and intraspecies polymorphisms. They are key elements for survival inside the vector and to modulate the vertebrate immune response during infection. Most of the studies on glycoconjugates focused on Old World Leishmania species. Here, it is reported some of the studies involving New World species and their biological significance on host-parasite interaction. This article is part of a Special Issue entitled Glycoproteomics. Copyright © 2011 Elsevier B.V. All rights reserved.

Modes of Interaction between Individuals Dominate the Topologies of Real World Networks

PubMed Central

Lee, Insuk; Kim, Eiru; Marcotte, Edward M.

2015-01-01

We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Specifically, a major dichotomy in previously studied networks arises from modeling networks in terms of pairwise versus group tasks. The former often intrinsically give rise to scale-free, disassortative, hierarchical networks, whereas the latter often give rise to single- or broad-scale, assortative, nonhierarchical networks. These dependencies explain contrasting observations among previous topological analyses of real world complex systems. We also observe this trend in systems with natural hierarchies, in which alternate representations of the same networks, but which capture different levels of the hierarchy, manifest these signature topological differences. For example, in both the Internet and cellular proteomes, networks of lower-level system components (routers within domains or proteins within biological processes) are assortative and nonhierarchical, whereas networks of upper-level system components (internet domains or biological processes) are disassortative and hierarchical. Our results demonstrate that network topologies of complex systems must be interpreted in light of their hierarchical natures and interaction types. PMID:25793969

Protein corona – from molecular adsorption to physiological complexity

PubMed Central

Docter, Dominic; Maskos, Michael

2015-01-01

Summary In biological environments, nanoparticles are enshrouded by a layer of biomolecules, predominantly proteins, mediating its subsequent interactions with cells. Detecting this protein corona, understanding its formation with regards to nanoparticle (NP) and protein properties, and elucidating its biological implications were central aims of bio-related nano-research throughout the past years. Here, we discuss the mechanistic parameters that are involved in the protein corona formation and the consequences of this corona formation for both, the particle, and the protein. We review consequences of corona formation for colloidal stability and discuss the role of functional groups and NP surface functionalities in shaping NP–protein interactions. We also elaborate the recent advances demonstrating the strong involvement of Coulomb-type interactions between NPs and charged patches on the protein surface. Moreover, we discuss novel aspects related to the complexity of the protein corona forming under physiological conditions in full serum. Specifically, we address the relation between particle size and corona composition and the latest findings that help to shed light on temporal evolution of the full serum corona for the first time. Finally, we discuss the most recent advances regarding the molecular-scale mechanistic role of the protein corona in cellular uptake of NPs. PMID:25977856

Protein corona - from molecular adsorption to physiological complexity.

PubMed

Treuel, Lennart; Docter, Dominic; Maskos, Michael; Stauber, Roland H

2015-01-01

In biological environments, nanoparticles are enshrouded by a layer of biomolecules, predominantly proteins, mediating its subsequent interactions with cells. Detecting this protein corona, understanding its formation with regards to nanoparticle (NP) and protein properties, and elucidating its biological implications were central aims of bio-related nano-research throughout the past years. Here, we discuss the mechanistic parameters that are involved in the protein corona formation and the consequences of this corona formation for both, the particle, and the protein. We review consequences of corona formation for colloidal stability and discuss the role of functional groups and NP surface functionalities in shaping NP-protein interactions. We also elaborate the recent advances demonstrating the strong involvement of Coulomb-type interactions between NPs and charged patches on the protein surface. Moreover, we discuss novel aspects related to the complexity of the protein corona forming under physiological conditions in full serum. Specifically, we address the relation between particle size and corona composition and the latest findings that help to shed light on temporal evolution of the full serum corona for the first time. Finally, we discuss the most recent advances regarding the molecular-scale mechanistic role of the protein corona in cellular uptake of NPs.

SNP by SNP by environment interaction network of alcoholism.

PubMed

Zollanvari, Amin; Alterovitz, Gil

2017-03-14

Alcoholism has a strong genetic component. Twin studies have demonstrated the heritability of a large proportion of phenotypic variance of alcoholism ranging from 50-80%. The search for genetic variants associated with this complex behavior has epitomized sequence-based studies for nearly a decade. The limited success of genome-wide association studies (GWAS), possibly precipitated by the polygenic nature of complex traits and behaviors, however, has demonstrated the need for novel, multivariate models capable of quantitatively capturing interactions between a host of genetic variants and their association with non-genetic factors. In this regard, capturing the network of SNP by SNP or SNP by environment interactions has recently gained much interest. Here, we assessed 3,776 individuals to construct a network capable of detecting and quantifying the interactions within and between plausible genetic and environmental factors of alcoholism. In this regard, we propose the use of first-order dependence tree of maximum weight as a potential statistical learning technique to delineate the pattern of dependencies underpinning such a complex trait. Using a predictive based analysis, we further rank the genes, demographic factors, biological pathways, and the interactions represented by our SNP [Formula: see text]SNP[Formula: see text]E network. The proposed framework is quite general and can be potentially applied to the study of other complex traits.

Interaction-based evolution: how natural selection and nonrandom mutation work together.

PubMed

Livnat, Adi

2013-10-18

The modern evolutionary synthesis leaves unresolved some of the most fundamental, long-standing questions in evolutionary biology: What is the role of sex in evolution? How does complex adaptation evolve? How can selection operate effectively on genetic interactions? More recently, the molecular biology and genomics revolutions have raised a host of critical new questions, through empirical findings that the modern synthesis fails to explain: for example, the discovery of de novo genes; the immense constructive role of transposable elements in evolution; genetic variance and biochemical activity that go far beyond what traditional natural selection can maintain; perplexing cases of molecular parallelism; and more. Here I address these questions from a unified perspective, by means of a new mechanistic view of evolution that offers a novel connection between selection on the phenotype and genetic evolutionary change (while relying, like the traditional theory, on natural selection as the only source of feedback on the fit between an organism and its environment). I hypothesize that the mutation that is of relevance for the evolution of complex adaptation-while not Lamarckian, or "directed" to increase fitness-is not random, but is instead the outcome of a complex and continually evolving biological process that combines information from multiple loci into one. This allows selection on a fleeting combination of interacting alleles at different loci to have a hereditary effect according to the combination's fitness. This proposed mechanism addresses the problem of how beneficial genetic interactions can evolve under selection, and also offers an intuitive explanation for the role of sex in evolution, which focuses on sex as the generator of genetic combinations. Importantly, it also implies that genetic variation that has appeared neutral through the lens of traditional theory can actually experience selection on interactions and thus has a much greater adaptive potential than previously considered. Empirical evidence for the proposed mechanism from both molecular evolution and evolution at the organismal level is discussed, and multiple predictions are offered by which it may be tested. This article was reviewed by Nigel Goldenfeld (nominated by Eugene V. Koonin), Jürgen Brosius and W. Ford Doolittle.

Coarse-grained molecular dynamics simulations for giant protein-DNA complexes

NASA Astrophysics Data System (ADS)

Takada, Shoji

Biomolecules are highly hierarchic and intrinsically flexible. Thus, computational modeling calls for multi-scale methodologies. We have been developing a coarse-grained biomolecular model where on-average 10-20 atoms are grouped into one coarse-grained (CG) particle. Interactions among CG particles are tuned based on atomistic interactions and the fluctuation matching algorithm. CG molecular dynamics methods enable us to simulate much longer time scale motions of much larger molecular systems than fully atomistic models. After broad sampling of structures with CG models, we can easily reconstruct atomistic models, from which one can continue conventional molecular dynamics simulations if desired. Here, we describe our CG modeling methodology for protein-DNA complexes, together with various biological applications, such as the DNA duplication initiation complex, model chromatins, and transcription factor dynamics on chromatin-like environment.

Membrane Interaction of Antimicrobial Peptides Using E. coli Lipid Extract as Model Bacterial Cell Membranes and SFG Spectroscopy

PubMed Central

Soblosky, Lauren; Ramamoorthy, Ayyalusamy; Chen, Zhan

2015-01-01

Supported lipid bilayers are used as a convenient model cell membrane system to study biologically important molecule-lipid interactions in situ. However, the lipid bilayer models are often simple and the acquired results with these models may not provide all pertinent information related to a real cell membrane. In this work, we use sum frequency generation (SFG) vibrational spectroscopy to study molecular-level interactions between the antimicrobial peptides (AMPs) MSI-594, ovispirin-1 G18, magainin 2 and a simple 1,2-dipalmitoyl-d62-sn-glycero-3-phosphoglycerol (dDPPG)-1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPG) bilayer. We compared such interactions to those between the AMPs and a more complex dDPPG/E. coli polar lipid extract bilayer. We show that to fully understand more complex aspects of peptide-bilayer interaction, such as interaction kinetics, a heterogeneous lipid composition is required, such as the E. coli polar lipid extract. The discrepancy in peptide-bilayer interaction is likely due in part to the difference in bilayer charge between the two systems since highly negative charged lipids can promote more favorable electrostatic interactions between the peptide and lipid bilayer. Results presented in this paper indicate that more complex model bilayers are needed to accurately analyze peptide-cell membrane interactions and demonstrates the importance of using an appropriate lipid composition to study AMP interaction properties. PMID:25707312

Etiologies of Obesity in Children: Nature and Nurture

PubMed Central

Skelton, Joseph A.; Irby, Megan B.; Grzywacz, Joseph; Miller, Gary

2011-01-01

Synopsis Childhood obesity is a profoundly complex problem and serves as an example of a biospychosocial issue. Scientific inquiry has provided incredible insight into the complex etiology of weight gain, but must be viewed as an interaction between a human’s propensity to conserve calories for survival in a world with an abundance of it. This chapter will provide a brief overview divided between biologic (Nature) and psychosocial and behavioral (Nurture) factors. PMID:22093854

Gold(III) complexes with hydroxyquinoline, aminoquinoline and quinoline ligands: Synthesis, cytotoxicity, DNA and protein binding studies.

PubMed

Martín-Santos, Cecilia; Michelucci, Elena; Marzo, Tiziano; Messori, Luigi; Szumlas, Piotr; Bednarski, Patrick J; Mas-Ballesté, Rubén; Navarro-Ranninger, Carmen; Cabrera, Silvia; Alemán, José

2015-12-01

In this article, we report on the synthesis and the chemical and biological characterization of novel gold(III) complexes based on hydroxyl- or amino-quinoline ligands that are evaluated as prospective anticancer agents. To gain further insight into their reactivity and possible mode of action, their interactions with model proteins and standard nucleic acid molecules were investigated. Copyright © 2015 Elsevier Inc. All rights reserved.

The ubiquitin conjugating enzyme UbcH10 competes with UbcH3 for binding to the SCF complex, a ubiquitin ligase involved in cell cycle progression

USDA-ARS?s Scientific Manuscript database

Ubiquitylation, which regulates most biological pathways, occurs through an enzymatic cascade involving a ubiquitin (ub) activating enzyme (E1), a ub conjugating enzyme (E2) and a ub ligase (E3). UbcH3 is the E2 that interacts with SCF (Skp1/Cul1/F-box protein) complex and ubiquitylates many protein...

Strongly exchange-coupled triplet pairs in an organic semiconductor

NASA Astrophysics Data System (ADS)

Weiss, Leah R.; Bayliss, Sam L.; Kraffert, Felix; Thorley, Karl J.; Anthony, John E.; Bittl, Robert; Friend, Richard H.; Rao, Akshay; Greenham, Neil C.; Behrends, Jan

2017-02-01

From biological complexes to devices based on organic semiconductors, spin interactions play a key role in the function of molecular systems. For instance, triplet-pair reactions impact operation of organic light-emitting diodes as well as photovoltaic devices. Conventional models for triplet pairs assume they interact only weakly. Here, using electron spin resonance, we observe long-lived, strongly interacting triplet pairs in an organic semiconductor, generated via singlet fission. Using coherent spin manipulation of these two-triplet states, we identify exchange-coupled (spin-2) quintet complexes coexisting with weakly coupled (spin-1) triplets. We measure strongly coupled pairs with a lifetime approaching 3 μs and a spin coherence time approaching 1 μs, at 10 K. Our results pave the way for the utilization of high-spin systems in organic semiconductors.

Synthesis, characterization and deepening in the comprehension of the biological action mechanisms of a new nickel complex with antiproliferative activity.

PubMed

Buschini, Annamaria; Pinelli, Silvana; Pellacani, Claudia; Giordani, Federica; Ferrari, Marisa Belicchi; Bisceglie, Franco; Giannetto, Marco; Pelosi, Giorgio; Tarasconi, Pieralberto

2009-05-01

Thiosemicarbazones are versatile organic compounds that present considerable pharmaceutical interest because of a wide range of properties. In our laboratory we synthesised some new metal-complexes with thiosemicarbazones derived from natural aldehydes which showed peculiar biological activities. In particular, a nickel complex [Ni(S-tcitr)(2)] (S-tcitr=S-citronellalthiosemicarbazonate) was observed to induce an antiproliferative effect on U937, a human histiocytic lymphoma cell line, at low concentrations (IC(50)=14.4microM). Therefore, we decided to study the interactions of this molecule with various cellular components and to characterise the induced apoptotic pathway. Results showed that [Ni(S-tcitr)(2)] causes programmed cell death via down-regulation of Bcl-2, alteration of mitochondrial membrane potential and caspase-3 activity, regardless of p53 function. The metal complex is not active on G(0) cells (i.e. fresh leukocytes) but is able to induce perturbation of the cell cycle on stimulated lymphocytes and U937 cells, in which a G(2)/M block was detected. It reaches the nucleus where it induces, at low concentrations (2.5-5.0microM), DNA damage, which could be partially ascribed to oxidative stress. [Ni(S-tcitr)(2)] is moreover able to strongly reduce the telomerase activity. Although the biological target of this metal complex is still unknown, the reported data suggest that [Ni(S-tcitr)(2)] could be a good model for the synthesis of new metal thiosemicarbazones with specific biological activity.

Integrated Analysis of Genetic and Proteomic Data Identifies Biomarkers Associated with Adverse Events Following Smallpox Vaccination

EPA Science Inventory

Complex clinical outcomes, such as adverse reaction to vaccination, arise from the concerted interactions among the myriad components of a biological system. Therefore, comprehensive etiological models can be developed only through the integrated study of multiple types of experi...

The Role of Pictures in Learning Biology: Part 2, Picture-Text Processing.

ERIC Educational Resources Information Center

Reid, David

1990-01-01

The complex interactions between picture, text, and learner are examined, based on a 3-D model which describes the context of the learning task. The different strategies that children of various ability levels use in reading from illustrated texts are described. (KR)

Genetic control of root growth: from genes to networks

PubMed Central

Slovak, Radka; Ogura, Takehiko; Satbhai, Santosh B.; Ristova, Daniela; Busch, Wolfgang

2016-01-01

Background Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. Scope This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. Conclusions While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics. PMID:26558398

DNA Polymerase α Subunit Residues and Interactions Required for Efficient Initiation Complex Formation Identified by a Genetic Selection.

PubMed

Lindow, Janet C; Dohrmann, Paul R; McHenry, Charles S

2015-07-03

Biophysical and structural studies have defined many of the interactions that occur between individual components or subassemblies of the bacterial replicase, DNA polymerase III holoenzyme (Pol III HE). Here, we extended our knowledge of residues and interactions that are important for the first step of the replicase reaction: the ATP-dependent formation of an initiation complex between the Pol III HE and primed DNA. We exploited a genetic selection using a dominant negative variant of the polymerase catalytic subunit that can effectively compete with wild-type Pol III α and form initiation complexes, but cannot elongate. Suppression of the dominant negative phenotype was achieved by secondary mutations that were ineffective in initiation complex formation. The corresponding proteins were purified and characterized. One class of mutant mapped to the PHP domain of Pol III α, ablating interaction with the ϵ proofreading subunit and distorting the polymerase active site in the adjacent polymerase domain. Another class of mutation, found near the C terminus, interfered with τ binding. A third class mapped within the known β-binding domain, decreasing interaction with the β2 processivity factor. Surprisingly, mutations within the β binding domain also ablated interaction with τ, suggesting a larger τ binding site than previously recognized. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Interspecies Systems Biology Uncovers Metabolites Affecting C. elegans Gene Expression and Life History Traits

PubMed Central

Watson, Emma; MacNeil, Lesley T.; Ritter, Ashlyn D.; Yilmaz, L. Safak; Rosebrock, Adam P.; Caudy, Amy A.; Walhout, Albertha J. M.

2014-01-01

SUMMARY Diet greatly influences gene expression and physiology. In mammals, elucidating the effects and mechanisms of individual nutrients is challenging due to the complexity of both the animal and its diet. Here we used an interspecies systems biology approach with Caenorhabditis elegans and two if its bacterial diets, Escherichia coli and Comamonas aquatica, to identify metabolites that affect the animal’s gene expression and physiology. We identify vitamin B12 as the major dilutable metabolite provided by Comamonas aq. that regulates gene expression, accelerates development and reduces fertility, but does not affect lifespan. We find that vitamin B12 has a dual role in the animal: it affects development and fertility via the methionine/S-Adenosylmethionine (SAM) cycle and breaks down the short-chain fatty acid propionic acid preventing its toxic buildup. Our interspecies systems biology approach provides a paradigm for understanding complex interactions between diet and physiology. PMID:24529378

Concise Review: Organ Engineering: Design, Technology, and Integration.

PubMed

Kaushik, Gaurav; Leijten, Jeroen; Khademhosseini, Ali

2017-01-01

Engineering complex tissues and whole organs has the potential to dramatically impact translational medicine in several avenues. Organ engineering is a discipline that integrates biological knowledge of embryological development, anatomy, physiology, and cellular interactions with enabling technologies including biocompatible biomaterials and biofabrication platforms such as three-dimensional bioprinting. When engineering complex tissues and organs, core design principles must be taken into account, such as the structure-function relationship, biochemical signaling, mechanics, gradients, and spatial constraints. Technological advances in biomaterials, biofabrication, and biomedical imaging allow for in vitro control of these factors to recreate in vivo phenomena. Finally, organ engineering emerges as an integration of biological design and technical rigor. An overall workflow for organ engineering and guiding technology to advance biology as well as a perspective on necessary future iterations in the field is discussed. Stem Cells 2017;35:51-60. © 2016 AlphaMed Press.

The complexity of silk under the spotlight of synthetic biology.

PubMed

Vollrath, Fritz

2016-08-15

For centuries silkworm filaments have been the focus of R&D innovation centred on textile manufacture with high added value. Most recently, silk research has focused on more fundamental issues concerning bio-polymer structure-property-function relationships. This essay outlines the complexity and fundamentals of silk spinning, and presents arguments for establishing this substance as an interesting and important subject at the interface of systems biology (discovery) and synthetic biology (translation). It is argued that silk is a generic class of materials where each type of silk presents a different embodiment of emergent properties that combine genetically determined (anticipatory) and environmentally responsive components. In spiders' webs the various silks have evolved to form the interactive components of an intricate fabric that provides an extended phenotype to the spider's body morphology. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

Systems Proteomics for Translational Network Medicine

PubMed Central

Arrell, D. Kent; Terzic, Andre

2012-01-01

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

Reverse Ecology: from systems to environments and back.

PubMed

Levy, Roie; Borenstein, Elhanan

2012-01-01

The structure of complex biological systems reflects not only their function but also the environments in which they evolved and are adapted to. Reverse Ecology-an emerging new frontier in Evolutionary Systems Biology-aims to extract this information and to obtain novel insights into an organism's ecology. The Reverse Ecology framework facilitates the translation of high-throughput genomic data into large-scale ecological data, and has the potential to transform ecology into a high-throughput field. In this chapter, we describe some of the pioneering work in Reverse Ecology, demonstrating how system-level analysis of complex biological networks can be used to predict the natural habitats of poorly characterized microbial species, their interactions with other species, and universal patterns governing the adaptation of organisms to their environments. We further present several studies that applied Reverse Ecology to elucidate various aspects of microbial ecology, and lay out exciting future directions and potential future applications in biotechnology, biomedicine, and ecological engineering.

Harnessing glycomics technologies: integrating structure with function for glycan characterization

PubMed Central

Robinson, Luke N.; Artpradit, Charlermchai; Raman, Rahul; Shriver, Zachary H.; Ruchirawat, Mathuros; Sasisekharan, Ram

2013-01-01

Glycans, or complex carbohydrates, are a ubiquitous class of biological molecules which impinge on a variety of physiological processes ranging from signal transduction to tissue development and microbial pathogenesis. In comparison to DNA and proteins, glycans present unique challenges to the study of their structure and function owing to their complex and heterogeneous structures and the dominant role played by multivalency in their sequence-specific biological interactions. Arising from these challenges, there is a need to integrate information from multiple complementary methods to decode structure-function relationships. Focusing on acidic glycans, we describe here key glycomics technologies for characterizing their structural attributes, including linkage, modifications, and topology, as well as for elucidating their role in biological processes. Two cases studies, one involving sialylated branched glycans and the other sulfated glycosaminoglycans, are used to highlight how integration of orthogonal information from diverse datasets enables rapid convergence of glycan characterization for development of robust structure-function relationships. PMID:22522536

Shadows of complexity: what biological networks reveal about epistasis and pleiotropy

PubMed Central

Tyler, Anna L.; Asselbergs, Folkert W.; Williams, Scott M.; Moore, Jason H.

2011-01-01

Pleiotropy, in which one mutation causes multiple phenotypes, has traditionally been seen as a deviation from the conventional observation in which one gene affects one phenotype. Epistasis, or gene-gene interaction, has also been treated as an exception to the Mendelian one gene-one phenotype paradigm. This simplified perspective belies the pervasive complexity of biology and hinders progress toward a deeper understanding of biological systems. We assert that epistasis and pleiotropy are not isolated occurrences, but ubiquitous and inherent properties of biomolecular networks. These phenomena should not be treated as exceptions, but rather as fundamental components of genetic analyses. A systems level understanding of epistasis and pleiotropy is, therefore, critical to furthering our understanding of human genetics and its contribution to common human disease. Finally, graph theory offers an intuitive and powerful set of tools with which to study the network bases of these important genetic phenomena. PMID:19204994

Kirigami artificial muscles with complex biologically inspired morphologies

NASA Astrophysics Data System (ADS)

Sareh, Sina; Rossiter, Jonathan

2013-01-01

In this paper we present bio-inspired smart structures which exploit the actuation of flexible ionic polymer composites and the kirigami design principle. Kirigami design is used to convert planar actuators into active 3D structures capable of large out-of-plane displacement and that replicate biological mechanisms. Here we present the burstbot, a fluid control and propulsion mechanism based on the atrioventricular cuspid valve, and the vortibot, a spiral actuator based on Vorticella campanula, a ciliate protozoa. Models derived from biological counterparts are used as a platform for design optimization and actuator performance measurement. The symmetric and asymmetric fluid interactions of the burstbot are investigated and the effectiveness in fluid transport applications is demonstrated. The vortibot actuator is geometrically optimized as a camera positioner capable of 360° scanning. Experimental results for a one-turn spiral actuator show complex actuation derived from a single degree of freedom control signal.

C. elegans network biology: a beginning.

PubMed Central

Piano, Fabio; Gunsalus, Kristin C; Hill, David E; Vidal, Marc

2006-01-01

The architecture and dynamics of molecular networks can provide an understanding of complex biological processes complementary to that obtained from the in-depth study of single genes and proteins. With a completely sequenced and well-annotated genome, a fully characterized cell lineage, and powerful tools available to dissect development, Caenorhabditis elegans, among metazoans, provides an optimal system to bridge cellular and organismal biology with the global properties of macromolecular networks. This chapter considers omic technologies available for C. elegans to describe molecular networks--encompassing transcriptional and phenotypic profiling as well as physical interaction mapping--and discusses how their individual and integrated applications are paving the way for a network-level understanding of C. elegans biology. PMID:18050437

Nanoscale Structure and Interaction of Compact Assemblies of Carbon Nano-Materials

NASA Astrophysics Data System (ADS)

Timsina, Raju; Qiu, Xiangyun

Carbon-based nano-materials (CNM) are a diverse family of multi-functional materials under research and development world wide. Our work is further motivated by the predictive power of the physical understanding of the underlying structure-interaction-function relationships. Here we present results form recent studies of the condensed phases of several model CNMs in complexation with biologically derived molecules. Specifically, we employ X-ray diffraction (XRD) to determine nanoscale structures and use the osmotic stress method to quantify their interactions. The systems under investigation are dsDNA-dispersed carbon nanotubes (dsDNA-CNT), bile-salt-dispersed carbon nanotubes, and surfactant-assisted assemblies of graphene oxides. We found that salt and molecular crowding are both effective in condensing CNMs but the resultant structures show disparate phase behaviors. The molecular interactions driving the condensation/assembly sensitively depend on the nature of CNM complex surface chemistry and range from hydrophobic to electrostatic to entropic forces.

Trapping of the Enoyl-Acyl Carrier Protein Reductase–Acyl Carrier Protein Interaction

PubMed Central

Tallorin, Lorillee; Finzel, Kara; Nguyen, Quynh G.; Beld, Joris; La Clair, James J.; Burkart, Michael D.

2016-01-01

An ideal target for metabolic engineering, fatty acid biosynthesis remains poorly understood on a molecular level. These carrier protein-dependent pathways require fundamental protein–protein interactions to guide reactivity and processivity, and their control has become one of the major hurdles in successfully adapting these biological machines. Our laboratory has developed methods to prepare acyl carrier proteins (ACPs) loaded with substrate mimetics and cross-linkers to visualize and trap interactions with partner enzymes, and we continue to expand the tools for studying these pathways. We now describe application of the slow-onset, tight-binding inhibitor triclosan to explore the interactions between the type II fatty acid ACP from Escherichia coli, AcpP, and its corresponding enoyl-ACP reductase, FabI. We show that the AcpP–triclosan complex demonstrates nM binding, inhibits in vitro activity, and can be used to isolate FabI in complex proteomes. PMID:26938266

SSB as an organizer/mobilizer of genome maintenance complexes

PubMed Central

Shereda, Robert D.; Kozlov, Alexander G.; Lohman, Timothy M.; Cox, Michael M.; Keck, James L.

2008-01-01

When duplex DNA is altered in almost any way (replicated, recombined, or repaired), single strands of DNA are usually intermediates, and single-stranded DNA binding (SSB) proteins are present. These proteins have often been described as inert, protective DNA coatings. Continuing research is demonstrating a far more complex role of SSB that includes the organization and/or mobilization of all aspects of DNA metabolism. Escherichia coli SSB is now known to interact with at least 14 other proteins that include key components of the elaborate systems involved in every aspect of DNA metabolism. Most, if not all, of these interactions are mediated by the amphipathic C-terminus of SSB. In this review, we summarize the extent of the eubacterial SSB interaction network, describe the energetics of interactions with SSB, and highlight the roles of SSB in the process of recombination. Similar themes to those highlighted in this review are evident in all biological systems. PMID:18937104

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

PubMed Central

Reguly, Teresa; Breitkreutz, Ashton; Boucher, Lorrie; Breitkreutz, Bobby-Joe; Hon, Gary C; Myers, Chad L; Parsons, Ainslie; Friesen, Helena; Oughtred, Rose; Tong, Amy; Stark, Chris; Ho, Yuen; Botstein, David; Andrews, Brenda; Boone, Charles; Troyanskya, Olga G; Ideker, Trey; Dolinski, Kara; Batada, Nizar N; Tyers, Mike

2006-01-01

Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID () and SGD () databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. PMID:16762047

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology

PubMed Central

Cankorur-Cetinkaya, Ayca; Dias, Joao M. L.; Kludas, Jana; Slater, Nigel K. H.; Rousu, Juho; Dikicioglu, Duygu

2017-01-01

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple‐to‐use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257). PMID:28635591

On the binding affinity of macromolecular interactions: daring to ask why proteins interact

PubMed Central

Kastritis, Panagiotis L.; Bonvin, Alexandre M. J. J.

2013-01-01

Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (Kd), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition. PMID:23235262

Multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction

DOE PAGES

Larsen, Peter E.; Sreedasyam, Avinash; Trivedi, Geetika; ...

2016-01-19

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root – mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensormore » systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with fifteen transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and, jasmonic acid. Lastly, this multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.« less

Multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction

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

Larsen, Peter E.; Sreedasyam, Avinash; Trivedi, Geetika

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root – mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensormore » systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with fifteen transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and, jasmonic acid. Lastly, this multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.« less

Systems biology-based approaches toward understanding drought tolerance in food crops.

PubMed

Jogaiah, Sudisha; Govind, Sharathchandra Ramsandra; Tran, Lam-Son Phan

2013-03-01

Economically important crops, such as maize, wheat, rice, barley, and other food crops are affected by even small changes in water potential at important growth stages. Developing a comprehensive understanding of host response to drought requires a global view of the complex mechanisms involved. Research on drought tolerance has generally been conducted using discipline-specific approaches. However, plant stress response is complex and interlinked to a point where discipline-specific approaches do not give a complete global analysis of all the interlinked mechanisms. Systems biology perspective is needed to understand genome-scale networks required for building long-lasting drought resistance. Network maps have been constructed by integrating multiple functional genomics data with both model plants, such as Arabidopsis thaliana, Lotus japonicus, and Medicago truncatula, and various food crops, such as rice and soybean. Useful functional genomics data have been obtained from genome-wide comparative transcriptome and proteome analyses of drought responses from different crops. This integrative approach used by many groups has led to identification of commonly regulated signaling pathways and genes following exposure to drought. Combination of functional genomics and systems biology is very useful for comparative analysis of other food crops and has the ability to develop stable food systems worldwide. In addition, studying desiccation tolerance in resurrection plants will unravel how combination of molecular genetic and metabolic processes interacts to produce a resurrection phenotype. Systems biology-based approaches have helped in understanding how these individual factors and mechanisms (biochemical, molecular, and metabolic) "interact" spatially and temporally. Signaling network maps of such interactions are needed that can be used to design better engineering strategies for improving drought tolerance of important crop species.

Revealing networks from dynamics: an introduction

NASA Astrophysics Data System (ADS)

Timme, Marc; Casadiego, Jose

2014-08-01

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.

Systems biology of personalized nutrition

PubMed Central

van Ommen, Ben; van den Broek, Tim; de Hoogh, Iris; van Erk, Marjan; van Someren, Eugene; Rouhani-Rankouhi, Tanja; Anthony, Joshua C; Hogenelst, Koen; Pasman, Wilrike; Boorsma, André; Wopereis, Suzan

2017-01-01

Abstract Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person’s health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology–based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of “systems flexibility” is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed. PMID:28969366

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

Protein bio-corona: critical issue in immune nanotoxicology.

PubMed

Neagu, Monica; Piperigkou, Zoi; Karamanou, Konstantina; Engin, Ayse Basak; Docea, Anca Oana; Constantin, Carolina; Negrei, Carolina; Nikitovic, Dragana; Tsatsakis, Aristidis

2017-03-01

With the expansion of the nanomedicine field, the knowledge focusing on the behavior of nanoparticles in the biological milieu has rapidly escalated. Upon introduction to a complex biological system, nanomaterials dynamically interact with all the encountered biomolecules and form the protein "bio-corona." The decoration with these surface biomolecules endows nanoparticles with new properties. The present review will address updates of the protein bio-corona characteristics as influenced by nanoparticle's physicochemical properties and by the particularities of the encountered biological milieu. Undeniably, bio-corona generation influences the efficacy of the nanodrug and guides the actions of innate and adaptive immunity. Exploiting the dynamic process of protein bio-corona development in combination with the new engineered horizons of drugs linked to nanoparticles could lead to innovative functional nanotherapies. Therefore, bio-medical nanotechnologies should focus on the interactions of nanoparticles with the immune system for both safety and efficacy reasons.

A proteomics method using immunoaffinity fluorogenic derivatization-liquid chromatography/tandem mass spectrometry (FD-LC-MS/MS) to identify a set of interacting proteins.

PubMed

Nakata, Katsunori; Saitoh, Ryoichi; Ishigai, Masaki; Imai, Kazuhiro

2018-02-01

Biological functions in organisms are usually controlled by a set of interacting proteins, and identifying the proteins that interact is useful for understanding the mechanism of the functions. Immunoprecipitation is a method that utilizes the affinity of an antibody to isolate and identify the proteins that have interacted in a biological sample. In this study, the FD-LC-MS/MS method, which involves fluorogenic derivatization followed by separation and quantification by HPLC and finally identification of proteins by HPLC-tandem mass spectrometry, was used to identify proteins in immunoprecipitated samples, using heat shock protein 90 (HSP90) as a model of an interacting protein in HepaRG cells. As a result, HSC70 protein, which was known to form a complex with HSP90, was isolated, together with three different types of HSP90-beta. The results demonstrated that the proposed immunoaffinity-FD-LC-MS/MS method could be useful for simultaneously detecting and identifying the proteins that interact with a certain protein. Copyright © 2017 John Wiley & Sons, Ltd.

Can we beat the biotin-avidin pair?: cucurbit[7]uril-based ultrahigh affinity host-guest complexes and their applications.

PubMed

Shetty, Dinesh; Khedkar, Jayshree K; Park, Kyeng Min; Kim, Kimoon

2015-12-07

The design of synthetic, monovalent host-guest molecular recognition pairs is still challenging and of particular interest to inquire into the limits of the affinity that can be achieved with designed systems. In this regard, cucurbit[7]uril (CB[7]), an important member of the host family cucurbit[n]uril (CB[n], n = 5-8, 10, 14), has attracted much attention because of its ability to form ultra-stable complexes with multiple guests. The strong hydrophobic effect between the host cavity and guests, ion-dipole and dipole-dipole interactions of guests with CB portals helps in cooperative and multiple noncovalent interactions that are essential for realizing such strong complexations. These highly selective, strong yet dynamic interactions can be exploited in many applications including affinity chromatography, biomolecule immobilization, protein isolation, biological catalysis, and sensor technologies. In this review, we summarize the progress in the development of high affinity guests for CB[7], factors affecting the stability of complexes, theoretical insights, and the utility of these high affinity pairs in different challenging applications.

Dynamic Primitives of Motor Behavior

PubMed Central

Hogan, Neville; Sternad, Dagmar

2013-01-01

We present in outline a theory of sensorimotor control based on dynamic primitives, which we define as attractors. To account for the broad class of human interactive behaviors—especially tool use—we propose three distinct primitives: submovements, oscillations and mechanical impedances, the latter necessary for interaction with objects. Due to fundamental features of the neuromuscular system, most notably its slow response, we argue that encoding in terms of parameterized primitives may be an essential simplification required for learning, performance, and retention of complex skills. Primitives may simultaneously and sequentially be combined to produce observable forces and motions. This may be achieved by defining a virtual trajectory composed of submovements and/or oscillations interacting with impedances. Identifying primitives requires care: in principle, overlapping submovements would be sufficient to compose all observed movements but biological evidence shows that oscillations are a distinct primitive. Conversely, we suggest that kinematic synergies, frequently discussed as primitives of complex actions, may be an emergent consequence of neuromuscular impedance. To illustrate how these dynamic primitives may account for complex actions, we briefly review three types of interactive behaviors: constrained motion, impact tasks, and manipulation of dynamic objects. PMID:23124919

Biology of Nanobots

NASA Astrophysics Data System (ADS)

Duan, Wentao; Pavlick, Ryan; Sen, Ayusman

2013-12-01

One of the more interesting recent discoveries has been the ability to design nano/microbots which catalytically harness the chemical energy in their environment to move autonomously. Their potential applications include delivery of materials, self-assembly of superstructures, and roving sensors. One emergent area of research is the study of their collective behavior and how they emulate living systems. The aim of this chapter is to describe the "biology" of nanobots, summarizing the fundamentals physics behind their motion and how the bots interact with each other to initiate complex emergent behavior.

Mosaic, Self-Similarity Logic, and Biological Attraction principles

PubMed Central

Baluška, František; Barlow, Peter W; Guidolin, Diego

2009-01-01

From a structural standpoint, living organisms are organized like a nest of Russian matryoshka dolls, in which structures are buried within one another. From a temporal point of view, this type of organization is the result of a history comprised of a set of time backcloths which have accompanied the passage of living matter from its origins up to the present day. The aim of the present paper is to indicate a possible course of this ‘passage through time, and suggest how today’s complexity has been reached by living organisms. This investigation will employ three conceptual tools, namely the Mosaic, Self-Similarity Logic, and the Biological Attraction principles. Self-Similarity Logic indicates the self-consistency by which elements of a living system interact, irrespective of the spatiotemporal level under consideration. The term Mosaic indicates how, from the same set of elements assembled according to different patterns, it is possible to arrive at completely different constructions: hence, each system becomes endowed with different emergent properties. The Biological Attraction principle states that there is an inherent drive for association and merging of compatible elements at all levels of biological complexity. By analogy with the gravitation law in physics, biological attraction is based on the evidence that each living organism creates an attractive field around itself. This field acts as a sphere of influence that actively attracts similar fields of other biological systems, thereby modifying salient features of the interacting organisms. Three specific organizational levels of living matter, namely the molecular, cellular, and supracellular levels, have been considered in order to analyse and illustrate the interpretative as well as the predictive roles of each of these three explanatory principles. PMID:20195461

Two-dimensional blue native/SDS-PAGE analysis of whole cell lysate protein complexes of rice in response to salt stress.

PubMed

Hashemi, Amenehsadat; Gharechahi, Javad; Nematzadeh, Ghorbanali; Shekari, Faezeh; Hosseini, Seyed Abdollah; Salekdeh, Ghasem Hosseini

2016-08-01

To understand the biology of a plant in response to stress, insight into protein-protein interactions, which almost define cell behavior, is thought to be crucial. Here, we provide a comparative complexomics analysis of leaf whole cell lysate of two rice genotypes with contrasting responses to salt using two-dimensional blue native/SDS-PAGE (2D-BN/SDS-PAGE). We aimed to identify changes in subunit composition and stoichiometry of protein complexes elicited by salt. Using mild detergent for protein complex solubilization, we were able to identify 9 protein assemblies as hetero-oligomeric and 30 as homo-oligomeric complexes. A total of 20 proteins were identified as monomers in the 2D-BN/SDS-PAGE gels. In addition to identifying known protein complexes that confirm the technical validity of our analysis, we were also able to discover novel protein-protein interactions. Interestingly, an interaction was detected for glycolytic enzymes enolase (ENO1) and triosephosphate isomerase (TPI) and also for a chlorophyll a-b binding protein and RuBisCo small subunit. To show changes in subunit composition and stoichiometry of protein assemblies during salt stress, the differential abundance of interacting proteins was compared between salt-treated and control plants. A detailed exploration of some of the protein complexes provided novel insight into the function, composition, stoichiometry and dynamics of known and previously uncharacterized protein complexes in response to salt stress. Copyright © 2016 Elsevier GmbH. All rights reserved.

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

Synthesis of mononuclear copper(II) complexes of N3O2 and N4O2 donors containing Schiff base ligands: Theoretical and biological observations

NASA Astrophysics Data System (ADS)

Mancha Madha, K.; Gurumoorthy, P.; Arul Antony, S.; Ramalakshmi, N.

2017-09-01

A new series of six mononuclear copper(II) complexes were synthesized from N3O2 and N4O2 donors containing Schiff base ligands, and characterized by various spectral methods. The geometry of the complexes was determined using UV-Vis, EPR and DFT calculations. The complexes of N3O2 donors (1-3) adopted square pyramidal geometry and the remaining complexes of N4O2 donors (4-6) show distorted octahedral geometry around copper(II) nuclei. Redox properties of the complexes show a one-electron irreversible reduction process in the cathodic potential (Epc) region from -0.74 to -0.98 V. The complexes show potent antioxidant activity against DPPH radicals. Molecular docking studies of complexes showed σ-π interaction, hydrogen bonding, electrostatic and van der Waals interactions with VEGFR2 kinase receptor. In vitro cytotoxicity of the complexes was tested against human breast cancer (MDA-MB-231) cell lines and one normal human dermal fibroblasts (NHDF) cell line through MTT assay. The morphological assessment data obtained by Hoechst 33258 and AO/EB staining revealed that the complexes induce apoptosis pathway of cell death.

Template-Based Modeling of Protein-RNA Interactions.

PubMed

Zheng, Jinfang; Kundrotas, Petras J; Vakser, Ilya A; Liu, Shiyong

2016-09-01

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.

Living Nanomachines

NASA Astrophysics Data System (ADS)

Carlier, M.-F.; Helfer, E.; Wade, R.; Haraux, F.

The living cell is a kind of factory on the microscopic scale, in which an assembly of modular machines carries out, in a spatially and temporally coordinated way, a whole range of activities internal to the cell, including the synthesis of substances essential to its survival, intracellular traffic, waste disposal, and cell division, but also activities related to intercellular communication and exchanges with the outside world, i.e., the ability of the cell to change shape, to move within a tissue, or to organise its own defence against attack by pathogens, injury, and so on. These nanomachines are made up of macromolecular assemblies with varying degrees of complexity, forged by evolution, within which work is done as a result of changes in interactions between proteins, or between proteins and nucleic acids, or between proteins and membrane components. All these cell components measure a few nanometers across, so the mechanical activity of these nanomachines all happens on the nanometric scale. The directional nature of the work carried out by biological nanomachines is associated with a dissipation of energy. As examples of protein assemblies, one could mention the proteasome, which is responsible for the degradation of proteins, and linear molecular motors such as actomyosin, responsible for muscle contraction, the dynein-microtubule system, responsible for flagellar motility, and the kinesin-microtubule system, responsible for transport of vesicles, which transform chemical energy into motion. Nucleic acid-protein assemblies include the ribosome, responsible for synthesising proteins, polymerases, helicases, elongation factors, and the machinery of DNA replication and repair; the mitotic spindle is an integrated system involving several of these activities which drive chromosome segregation. The machinery coupling membranes and proteins includes systems involved in the energy metabolism, such as the ATP synthase rotary motor, signalling cascades, endocytosis and phagocytosis complexes, and also dynamic membrane-cytoskeleton complexes which generate protrusion forces involved in cell adhesion and migration. The ideas of molecular recognition and controlled interfaces between biological components provide the underlying mechanisms for biological machinery and networks [1]. Many proteins illustrate this principle by their modular organisation into domains. The juxtaposition of catalytic domains of known function and domains of interaction with different partners leads to the emergence of new biological functions. It can also create threshold mechanisms, or biological switches, by triggering the activity of a given domain only when several partners interact with the regulatory domains. Many of these interaction domains are well understood. They exist inside different proteins, in particular, in cell signaling networks, and could potentially be used as building blocks in the construction of new proteins.

Structural insights of the MLF1/14-3-3 interaction.

PubMed

Molzan, Manuela; Weyand, Michael; Rose, Rolf; Ottmann, Christian

2012-02-01

Myeloid leukaemia factor 1 (MLF1) binds to 14-3-3 adapter proteins by a sequence surrounding Ser34 with the functional consequences of this interaction largely unknown. We present here the high-resolution crystal structure of this binding motif [MLF1(29-42)pSer34] in complex with 14-3-3ε and analyse the interaction with isothermal titration calorimetry. Fragment-based ligand discovery employing crystals of the binary 14-3-3ε/MLF1(29-42)pSer34 complex was used to identify a molecule that binds to the interface rim of the two proteins, potentially representing the starting point for the development of a small molecule that stabilizes the MLF1/14-3-3 protein-protein interaction. Such a compound might be used as a chemical biology tool to further analyse the 14-3-3/MLF1 interaction without the use of genetic methods. Database Structural data are available in the Protein Data Bank under the accession number(s) 3UAL [14-3-3ε/MLF1(29-42)pSer34 complex] and 3UBW [14-3-3ε/MLF1(29-42)pSer34/3-pyrrolidinol complex] Structured digital abstract •  14-3-3 epsilon and MLF1 bind by x-ray crystallography (View interaction) •  14-3-3 epsilon and MLF1 bind by isothermal titration calorimetry (View Interaction: 1, 2). © 2011 The Authors Journal compilation © 2011 FEBS.

Affinity purification and mass spectrometry: an attractive choice to investigate protein-protein interactions in plant immunity

USDA-ARS?s Scientific Manuscript database

Affinity purification of protein complexes from biological tissues, followed by liquid chromatography- tandem mass spectrometry (AP-MS/MS), has ballooned in recent years due to sizeable increases in nucleic acid sequence data essential for interpreting mass spectra, improvements in affinity purifica...

Postdoctoral Fellow | Center for Cancer Research

Cancer.gov

The Khare lab in the Laboratory of Molecular Biology, NCI Center for Cancer Research, NIH, is looking to recruit highly motivated researchers interested in a postdoctoral fellowship to study the molecular and genetic basis of complex microbial behaviors. Our lab is focused on multiple research avenues including interspecies interactions, antibiotic persistence, and adaptation

Book Review: "The Rhizosphere: Biochemistry and Organic Substances at the Soil-Plant Interface, Second Edition"

USDA-ARS?s Scientific Manuscript database

The complexity of the biological, chemical, and physical interactions occurring in the volume of soil surrounding the root of a growing plant dictates that a multidisciplinary approach must be taken to improve our understanding of this rhizosphere. Hence, "The Rhizosphere: Biochemistry and Organic S...

What Does Culture Have to Do with Teaching Science?

ERIC Educational Resources Information Center

Madden, Lauren; Joshi, Arti

2013-01-01

In nearly every elementary school, plants are an important part of the science curriculum. Understanding basic ideas about plants prepares children to study more complicated scientific concepts including cell biology, genetics and heredity, complex ecosystem interactions, and evolution. It is especially important that teachers of children at the…

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

Relevance Rank Platform (RRP) for Functional Filtering of High Content Protein-Protein Interaction Data.

PubMed

Pokharel, Yuba Raj; Saarela, Jani; Szwajda, Agnieszka; Rupp, Christian; Rokka, Anne; Lal Kumar Karna, Shibendra; Teittinen, Kaisa; Corthals, Garry; Kallioniemi, Olli; Wennerberg, Krister; Aittokallio, Tero; Westermarck, Jukka

2015-12-01

High content protein interaction screens have revolutionized our understanding of protein complex assembly. However, one of the major challenges in translation of high content protein interaction data is identification of those interactions that are functionally relevant for a particular biological question. To address this challenge, we developed a relevance ranking platform (RRP), which consist of modular functional and bioinformatic filters to provide relevance rank among the interactome proteins. We demonstrate the versatility of RRP to enable a systematic prioritization of the most relevant interaction partners from high content data, highlighted by the analysis of cancer relevant protein interactions for oncoproteins Pin1 and PME-1. We validated the importance of selected interactions by demonstration of PTOV1 and CSKN2B as novel regulators of Pin1 target c-Jun phosphorylation and reveal previously unknown interacting proteins that may mediate PME-1 effects via PP2A-inhibition. The RRP framework is modular and can be modified to answer versatile research problems depending on the nature of the biological question under study. Based on comparison of RRP to other existing filtering tools, the presented data indicate that RRP offers added value especially for the analysis of interacting proteins for which there is no sufficient prior knowledge available. Finally, we encourage the use of RRP in combination with either SAINT or CRAPome computational tools for selecting the candidate interactors that fulfill the both important requirements, functional relevance, and high confidence interaction detection. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

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

Structures of Adnectin/Protein Complexes Reveal an Expanded Binding Footprint

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

Ramamurthy, Vidhyashankar; Krystek, Jr., Stanley R.; Bush, Alexander

2014-10-02

Adnectins are targeted biologics derived from the tenth type III domain of human fibronectin ({sup 10}Fn3), a member of the immunoglobulin superfamily. Target-specific binders are selected from libraries generated by diversifying the three {sup 10}Fn3 loops that are analogous to the complementarity determining regions of antibodies. The crystal structures of two Adnectins were determined, each in complex with its therapeutic target, EGFR or IL-23. Both Adnectins bind different epitopes than those bound by known monoclonal antibodies. Molecular modeling suggests that some of these epitopes might not be accessible to antibodies because of the size and concave shape of the antibodymore » combining site. In addition to interactions from the Adnectin diversified loops, residues from the N terminus and/or the {beta} strands interact with the target proteins in both complexes. Alanine-scanning mutagenesis confirmed the calculated binding energies of these {beta} strand interactions, indicating that these nonloop residues can expand the available binding footprint.« less

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.

Generation of Diverse Biological Forms through Combinatorial Interactions between Tissue Polarity and Growth

PubMed Central

Kennaway, Richard; Coen, Enrico; Green, Amelia; Bangham, Andrew

2011-01-01

A major problem in biology is to understand how complex tissue shapes may arise through growth. In many cases this process involves preferential growth along particular orientations raising the question of how these orientations are specified. One view is that orientations are specified through stresses in the tissue (axiality-based system). Another possibility is that orientations can be specified independently of stresses through molecular signalling (polarity-based system). The axiality-based system has recently been explored through computational modelling. Here we develop and apply a polarity-based system which we call the Growing Polarised Tissue (GPT) framework. Tissue is treated as a continuous material within which regionally expressed factors under genetic control may interact and propagate. Polarity is established by signals that propagate through the tissue and is anchored in regions termed tissue polarity organisers that are also under genetic control. Rates of growth parallel or perpendicular to the local polarity may then be specified through a regulatory network. The resulting growth depends on how specified growth patterns interact within the constraints of mechanically connected tissue. This constraint leads to the emergence of features such as curvature that were not directly specified by the regulatory networks. Resultant growth feeds back to influence spatial arrangements and local orientations of tissue, allowing complex shapes to emerge from simple rules. Moreover, asymmetries may emerge through interactions between polarity fields. We illustrate the value of the GPT-framework for understanding morphogenesis by applying it to a growing Snapdragon flower and indicate how the underlying hypotheses may be tested by computational simulation. We propose that combinatorial intractions between orientations and rates of growth, which are a key feature of polarity-based systems, have been exploited during evolution to generate a range of observed biological shapes. PMID:21698124

Iron, ferritin, and nutrition.

PubMed

Theil, Elizabeth C

2004-01-01

Ferritin, a major form of endogenous iron in food legumes such as soybeans, is a novel and natural alternative for iron supplementation strategies where effectiveness is limited by acceptability, cost, or undesirable side effects. A member of the nonheme iron group of dietary iron sources, ferritin is a complex with Fe3+ iron in a mineral (thousands of iron atoms inside a protein cage) protected from complexation. Ferritin illustrates the wide range of chemical and biological properties among nonheme iron sources. The wide range of nonheme iron receptors matched to the structure of the iron complexes that occurs in microorganisms may, by analogy, exist in humans. An understanding of the chemistry and biology of each type of dietary iron source (ferritin, heme, Fe2+ ion, etc.), and of the interactions dependent on food sources, genes, and gender, is required to design diets that will eradicate global iron deficiency in the twenty-first century.

Dynamic interactions between cells and their extracellular matrix mediate embryonic development.

PubMed

Goody, Michelle F; Henry, Clarissa A

2010-06-01

Cells and their surrounding extracellular matrix microenvironment interact throughout all stages of life. Understanding the continuously changing scope of cell-matrix interactions in vivo is crucial to garner insights into both congenital birth defects and disease progression. A current challenge in the field of developmental biology is to adapt in vitro tools and rapidly evolving imaging technology to study cell-matrix interactions in a complex 4-D environment. In this review, we highlight the dynamic modulation of cell-matrix interactions during development. We propose that individual cell-matrix adhesion proteins are best considered as complex proteins that can play multiple, often seemingly contradictory roles, depending upon the context of the microenvironment. In addition, cell-matrix proteins can also exert different short versus long term effects. It is thus important to consider cell behavior in light of the microenvironment because of the constant and dynamic reciprocal interactions occurring between them. Finally, we suggest that analysis of cell-matrix interactions at multiple levels (molecules, cells, tissues) in vivo is critical for an integrated understanding because different information can be acquired from all size scales. Copyright 2010 Wiley-Liss, Inc.

Bioprinting the Cancer Microenvironment.

PubMed

Zhang, Yu Shrike; Duchamp, Margaux; Oklu, Rahmi; Ellisen, Leif W; Langer, Robert; Khademhosseini, Ali

2016-10-10

Cancer is intrinsically complex, comprising both heterogeneous cellular compositions and microenvironmental cues. During the various stages of cancer initiation, development, and metastasis, cell-cell interactions (involving vascular and immune cells besides cancerous cells) as well as cell-extracellular matrix (ECM) interactions (e.g., alteration in stiffness and composition of the surrounding matrix) play major roles. Conventional cancer models both two- and three-dimensional (2D and 3D) present numerous limitations as they lack good vascularization and cannot mimic the complexity of tumors, thereby restricting their use as biomimetic models for applications such as drug screening and fundamental cancer biology studies. Bioprinting as an emerging biofabrication platform enables the creation of high-resolution 3D structures and has been extensively used in the past decade to model multiple organs and diseases. More recently, this versatile technique has further found its application in studying cancer genesis, growth, metastasis, and drug responses through creation of accurate models that recreate the complexity of the cancer microenvironment. In this review we will focus first on cancer biology and limitations with current cancer models. We then detail the current bioprinting strategies including the selection of bioinks for capturing the properties of the tumor matrices, after which we discuss bioprinting of vascular structures that are critical toward construction of complex 3D cancer organoids. We finally conclude with current literature on bioprinted cancer models and propose future perspectives.

The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature.

PubMed

Özgür, Arzucan; Hur, Junguk; He, Yongqun

2016-01-01

The Interaction Network Ontology (INO) logically represents biological interactions, pathways, and networks. INO has been demonstrated to be valuable in providing a set of structured ontological terms and associated keywords to support literature mining of gene-gene interactions from biomedical literature. However, previous work using INO focused on single keyword matching, while many interactions are represented with two or more interaction keywords used in combination. This paper reports our extension of INO to include combinatory patterns of two or more literature mining keywords co-existing in one sentence to represent specific INO interaction classes. Such keyword combinations and related INO interaction type information could be automatically obtained via SPARQL queries, formatted in Excel format, and used in an INO-supported SciMiner, an in-house literature mining program. We studied the gene interaction sentences from the commonly used benchmark Learning Logic in Language (LLL) dataset and one internally generated vaccine-related dataset to identify and analyze interaction types containing multiple keywords. Patterns obtained from the dependency parse trees of the sentences were used to identify the interaction keywords that are related to each other and collectively represent an interaction type. The INO ontology currently has 575 terms including 202 terms under the interaction branch. The relations between the INO interaction types and associated keywords are represented using the INO annotation relations: 'has literature mining keywords' and 'has keyword dependency pattern'. The keyword dependency patterns were generated via running the Stanford Parser to obtain dependency relation types. Out of the 107 interactions in the LLL dataset represented with two-keyword interaction types, 86 were identified by using the direct dependency relations. The LLL dataset contained 34 gene regulation interaction types, each of which associated with multiple keywords. A hierarchical display of these 34 interaction types and their ancestor terms in INO resulted in the identification of specific gene-gene interaction patterns from the LLL dataset. The phenomenon of having multi-keyword interaction types was also frequently observed in the vaccine dataset. By modeling and representing multiple textual keywords for interaction types, the extended INO enabled the identification of complex biological gene-gene interactions represented with multiple keywords.

Unlocking the secrets to protein–protein interface drug targets using structural mass spectrometry techniques

PubMed Central

Dailing, Angela; Luchini, Alessandra; Liotta, Lance

2016-01-01

Protein–protein interactions (PPIs) drive all biologic systems at the subcellular and extracellular level. Changes in the specificity and affinity of these interactions can lead to cellular malfunctions and disease. Consequently, the binding interfaces between interacting protein partners are important drug targets for the next generation of therapies that block such interactions. Unfortunately, protein–protein contact points have proven to be very difficult pharmacological targets because they are hidden within complex 3D interfaces. For the vast majority of characterized binary PPIs, the specific amino acid sequence of their close contact regions remains unknown. There has been an important need for an experimental technology that can rapidly reveal the functionally important contact points of native protein complexes in solution. In this review, experimental techniques employing mass spectrometry to explore protein interaction binding sites are discussed. Hydrogen–deuterium exchange, hydroxyl radical footprinting, crosslinking and the newest technology protein painting, are compared and contrasted. PMID:26400464

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.

Nutrimetabonomics:applications for nutritional sciences, with specific reference to gut microbial interactions.

PubMed

Claus, Sandrine P; Swann, Jonathan R

2013-01-01

Understanding the role of the diet in determining human health and disease is one major objective of modern nutrition. Mammalian biocomplexity necessitates the incorporation of systems biology technologies into contemporary nutritional research. Metabonomics is a powerful approach that simultaneously measures the low-molecular-weight compounds in a biological sample, enabling the metabolic status of a biological system to be characterized. Such biochemical profiles contain latent information relating to inherent parameters, such as the genotype, and environmental factors, including the diet and gut microbiota. Nutritional metabonomics, or nutrimetabonomics, is being increasingly applied to study molecular interactions between the diet and the global metabolic system. This review discusses three primary areas in which nutrimetabonomics has enjoyed successful application in nutritional research: the illumination of molecular relationships between nutrition and biochemical processes; elucidation of biomarker signatures of food components for use in dietary surveillance; and the study of complex trans-genomic interactions between the mammalian host and its resident gut microbiome. Finally, this review illustrates the potential for nutrimetabonomics in nutritional science as an indispensable tool to achieve personalized nutrition.

QM/MM hybrid calculation of biological macromolecules using a new interface program connecting QM and MM engines

NASA Astrophysics Data System (ADS)

Hagiwara, Yohsuke; Ohta, Takehiro; Tateno, Masaru

2009-02-01

An interface program connecting a quantum mechanics (QM) calculation engine, GAMESS, and a molecular mechanics (MM) calculation engine, AMBER, has been developed for QM/MM hybrid calculations. A protein-DNA complex is used as a test system to investigate the following two types of QM/MM schemes. In a 'subtractive' scheme, electrostatic interactions between QM/MM regions are truncated in QM calculations; in an 'additive' scheme, long-range electrostatic interactions within a cut-off distance from QM regions are introduced into one-electron integration terms of a QM Hamiltonian. In these calculations, 338 atoms are assigned as QM atoms using Hartree-Fock (HF)/density functional theory (DFT) hybrid all-electron calculations. By comparing the results of the additive and subtractive schemes, it is found that electronic structures are perturbed significantly by the introduction of MM partial charges surrounding QM regions, suggesting that biological processes occurring in functional sites are modulated by the surrounding structures. This also indicates that the effects of long-range electrostatic interactions involved in the QM Hamiltonian are crucial for accurate descriptions of electronic structures of biological macromolecules.

Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods

PubMed Central

Du, Xing; Li, Yi; Xia, Yuan-Ling; Ai, Shi-Meng; Liang, Jing; Sang, Peng; Ji, Xing-Lai; Liu, Shu-Qun

2016-01-01

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed. PMID:26821017

Multidisciplinary approaches to understanding collective cell migration in developmental biology.

PubMed

Schumacher, Linus J; Kulesa, Paul M; McLennan, Rebecca; Baker, Ruth E; Maini, Philip K

2016-06-01

Mathematical models are becoming increasingly integrated with experimental efforts in the study of biological systems. Collective cell migration in developmental biology is a particularly fruitful application area for the development of theoretical models to predict the behaviour of complex multicellular systems with many interacting parts. In this context, mathematical models provide a tool to assess the consistency of experimental observations with testable mechanistic hypotheses. In this review, we showcase examples from recent years of multidisciplinary investigations of neural crest cell migration. The neural crest model system has been used to study how collective migration of cell populations is shaped by cell-cell interactions, cell-environmental interactions and heterogeneity between cells. The wide range of emergent behaviours exhibited by neural crest cells in different embryonal locations and in different organisms helps us chart out the spectrum of collective cell migration. At the same time, this diversity in migratory characteristics highlights the need to reconcile or unify the array of currently hypothesized mechanisms through the next generation of experimental data and generalized theoretical descriptions. © 2016 The Authors.

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

Ecological Complexity in a Coffee Agroecosystem: Spatial Heterogeneity, Population Persistence and Biological Control

PubMed Central

Liere, Heidi; Jackson, Doug; Vandermeer, John

2012-01-01

Background Spatial heterogeneity is essential for the persistence of many inherently unstable systems such as predator-prey and parasitoid-host interactions. Since biological interactions themselves can create heterogeneity in space, the heterogeneity necessary for the persistence of an unstable system could be the result of local interactions involving elements of the unstable system itself. Methodology/Principal Findings Here we report on a predatory ladybird beetle whose natural history suggests that the beetle requires the patchy distribution of the mutualism between its prey, the green coffee scale, and the arboreal ant, Azteca instabilis. Based on known ecological interactions and the natural history of the system, we constructed a spatially-explicit model and showed that the clustered spatial pattern of ant nests facilitates the persistence of the beetle populations. Furthermore, we show that the dynamics of the beetle consuming the scale insects can cause the clustered distribution of the mutualistic ants in the first place. Conclusions/Significance From a theoretical point of view, our model represents a novel situation in which a predator indirectly causes a spatial pattern of an organism other than its prey, and in doing so facilitates its own persistence. From a practical point of view, it is noteworthy that one of the elements in the system is a persistent pest of coffee, an important world commodity. This pest, we argue, is kept within limits of control through a complex web of ecological interactions that involves the emergent spatial pattern. PMID:23029061

Mammalian synthetic biology for studying the cell

PubMed Central

Mathur, Melina; Xiang, Joy S.

2017-01-01

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology. PMID:27932576

At a glance: cellular biology for engineers.

PubMed

Khoshmanesh, K; Kouzani, A Z; Nahavandi, S; Baratchi, S; Kanwar, J R

2008-10-01

Engineering contributions have played an important role in the rise and evolution of cellular biology. Engineering technologies have helped biologists to explore the living organisms at cellular and molecular levels, and have created new opportunities to tackle the unsolved biological problems. There is now a growing demand to further expand the role of engineering in cellular biology research. For an engineer to play an effective role in cellular biology, the first essential step is to understand the cells and their components. However, the stumbling block of this step is to comprehend the information given in the cellular biology literature because it best suits the readers with a biological background. This paper aims to overcome this bottleneck by describing the human cell components as micro-plants that form cells as micro-bio-factories. This concept can accelerate the engineers' comprehension of the subject. In this paper, first the structure and function of different cell components are described. In addition, the engineering attempts to mimic various cell components through numerical modelling or physical implementation are highlighted. Next, the interaction of different cell components that facilitate complicated chemical processes, such as energy generation and protein synthesis, are described. These complex interactions are translated into simple flow diagrams, generally used by engineers to represent multi-component processes.

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT.

PubMed

Bown, James L; Shovman, Mark; Robertson, Paul; Boiko, Andrei; Goltsov, Alexey; Mullen, Peter; Harrison, David J

2017-05-02

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.

Development of hydrogels for regenerative engineering.

PubMed

Guan, Xiaofei; Avci-Adali, Meltem; Alarçin, Emine; Cheng, Hao; Kashaf, Sara Saheb; Li, Yuxiao; Chawla, Aditya; Jang, Hae Lin; Khademhosseini, Ali

2017-05-01

The aim of regenerative engineering is to restore complex tissues and biological systems through convergence in the fields of advanced biomaterials, stem cell science, and developmental biology. Hydrogels are one of the most attractive biomaterials for regenerative engineering, since they can be engineered into tissue mimetic 3D scaffolds to support cell growth due to their similarity to native extracellular matrix. Advanced nano- and micro-technologies have dramatically increased the ability to control properties and functionalities of hydrogel materials by facilitating biomimetic fabrication of more sophisticated compositions and architectures, thus extending our understanding of cell-matrix interactions at the nanoscale. With this perspective, this review discusses the most commonly used hydrogel materials and their fabrication strategies for regenerative engineering. We highlight the physical, chemical, and functional modulation of hydrogels to design and engineer biomimetic tissues based on recent achievements in nano- and micro-technologies. In addition, current hydrogel-based regenerative engineering strategies for treating multiple tissues, such as musculoskeletal, nervous and cardiac tissue, are also covered in this review. The interaction of multiple disciplines including materials science, cell biology, and chemistry, will further play an important role in the design of functional hydrogels for the regeneration of complex tissues. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linking Microbiota to Human Diseases: A Systems Biology Perspective.

PubMed

Wu, Hao; Tremaroli, Valentina; Bäckhed, Fredrik

2015-12-01

The human gut microbiota encompasses a densely populated ecosystem that provides essential functions for host development, immune maturation, and metabolism. Alterations to the gut microbiota have been observed in numerous diseases, including human metabolic diseases such as obesity, type 2 diabetes (T2D), and irritable bowel syndrome, and some animal experiments have suggested causality. However, few studies have validated causality in humans and the underlying mechanisms remain largely to be elucidated. We discuss how systems biology approaches combined with new experimental technologies may disentangle some of the mechanistic details in the complex interactions of diet, microbiota, and host metabolism and may provide testable hypotheses for advancing our current understanding of human-microbiota interaction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Teaching Electrostatics and Entropy in Introductory Physics

NASA Astrophysics Data System (ADS)

Reeves, Mark

Entropy changes underlie the physics that dominates biological interactions. Indeed, introductory biology courses often begin with an exploration of the qualities of water that are important to living systems. However, one idea that is not explicitly addressed in most introductory physics or biology courses is important contribution of the entropy in driving fundamental biological processes towards equilibrium. I will present material developed to teach electrostatic screening in solutions and the function of nerve cells where entropic effects act to counterbalance electrostatic attraction. These ideas are taught in an introductory, calculus-based physics course to biomedical engineers using SCALEUP pedagogy. Results of student mastering of complex problems that cross disciplinary boundaries between biology and physics, as well as the challenges that they face in learning this material will be presented.

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

A computational study of anion-modulated cation-π interactions.

PubMed

Carrazana-García, Jorge A; Rodríguez-Otero, Jesús; Cabaleiro-Lago, Enrique M

2012-05-24

The interaction of anions with cation-π complexes formed by the guanidinium cation and benzene was thoroughly studied by means of computational methods. Potential energy surface scans were performed in order to evaluate the effect of the anion coming closer to the cation-π pair. Several structures of guanidinium-benzene complexes and anion approaching directions were examined. Supermolecule calculations were performed on ternary complexes formed by guanidinium, benzene, and one anion and the interaction energy was decomposed into its different two- and three-body contributions. The interaction energies were further dissected into their electrostatic, exchange, repulsion, polarization and dispersion contributions by means of local molecular orbital energy decomposition analysis. The results confirm that, besides the electrostatic cation-anion attraction, the effect of the anion over the cation-π interaction is mainly due to polarization and can be rationalized following the changes in the anion-π and the nonadditive (three-body) terms of the interaction. When the cation and the anion are on the same side of the π system, the three-body interaction is anticooperative, but when the anion and the cation are on opposite sides of the π system, the three-body interaction is cooperative. As far as we know, this is the first study where this kind of analysis is carried out with a structured cation as guanidinium with a significant biological interest.

Synthesis, characterization, DNA/protein interaction and cytotoxicity studies of Cu(II) and Co(II) complexes derived from dipyridyl triazole ligands.

PubMed

Zhang, Wei; Yao, Di; Wei, Yi; Tang, Jie; Bian, He-Dong; Huang, Fu-Ping; Liang, Hong

2016-06-15

Four different transition metal complexes containing dipyridyl triazole ligands, namely [Cu(abpt)2Cl2]·2H2O (1), [Cu(abpt)2(ClO4)2] (2), [Co2(abpt)2(H2O)2Cl2]·Cl2·4H2O (3) and [Co2(Hbpt)2(CH3OH)2(NO3)2] (4) have been designed, synthesized and further structurally characterized by X-ray crystallography, ESI-MS, elemental analysis, IR and Raman spectroscopy. In these complexes, the both ligands act as bidentate ligands with N, N donors. DNA binding interactions with calf thymus DNA (ct-DNA) of the ligand and its complexes 1~4 were investigated via electronic absorption, fluorescence quenching, circular dichroism and viscosity measurements as well as confocal Laser Raman spectroscopy. The results show these complexes are able to bind to DNA via the non-covalent mode i.e. intercalation and groove binding or electrostatic interactions. The interactions with bovine serum albumin (BSA) were also studied using UV-Vis and fluorescence spectroscopic methods which indicated that fluorescence quenching of BSA by these compounds was the presence of both static and dynamic quenching. Moreover, the in vitro cytotoxic effects of the complexes against four cell lines SK-OV-3, HL-7702, BEL7404 and NCI-H460 showed the necessity of the coordination action on the biological properties on the respective complex and that all four complexes exhibited substantial cytotoxic activity. Copyright © 2016. Published by Elsevier B.V.

Conservation of hot regions in protein-protein interaction in evolution.

PubMed

Hu, Jing; Li, Jiarui; Chen, Nansheng; Zhang, Xiaolong

2016-11-01

The hot regions of protein-protein interactions refer to the active area which formed by those most important residues to protein combination process. With the research development on protein interactions, lots of predicted hot regions can be discovered efficiently by intelligent computing methods, while performing biology experiments to verify each every prediction is hardly to be done due to the time-cost and the complexity of the experiment. This study based on the research of hot spot residue conservations, the proposed method is used to verify authenticity of predicted hot regions that using machine learning algorithm combined with protein's biological features and sequence conservation, though multiple sequence alignment, module substitute matrix and sequence similarity to create conservation scoring algorithm, and then using threshold module to verify the conservation tendency of hot regions in evolution. This research work gives an effective method to verify predicted hot regions in protein-protein interactions, which also provides a useful way to deeply investigate the functional activities of protein hot regions. Copyright © 2016. Published by Elsevier Inc.

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

Identification and super-resolution imaging of ligand-activated receptor dimers in live cells

NASA Astrophysics Data System (ADS)

Winckler, Pascale; Lartigue, Lydia; Giannone, Gregory; de Giorgi, Francesca; Ichas, François; Sibarita, Jean-Baptiste; Lounis, Brahim; Cognet, Laurent

2013-08-01

Molecular interactions are key to many chemical and biological processes like protein function. In many signaling processes they occur in sub-cellular areas displaying nanoscale organizations and involving molecular assemblies. The nanometric dimensions and the dynamic nature of the interactions make their investigations complex in live cells. While super-resolution fluorescence microscopies offer live-cell molecular imaging with sub-wavelength resolutions, they lack specificity for distinguishing interacting molecule populations. Here we combine super-resolution microscopy and single-molecule Förster Resonance Energy Transfer (FRET) to identify dimers of receptors induced by ligand binding and provide super-resolved images of their membrane distribution in live cells. By developing a two-color universal-Point-Accumulation-In-the-Nanoscale-Topography (uPAINT) method, dimers of epidermal growth factor receptors (EGFR) activated by EGF are studied at ultra-high densities, revealing preferential cell-edge sub-localization. This methodology which is specifically devoted to the study of molecules in interaction, may find other applications in biological systems where understanding of molecular organization is crucial.

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

Exploring host–microbiota interactions in animal models and humans

PubMed Central

Kostic, Aleksandar D.; Howitt, Michael R.; Garrett, Wendy S.

2013-01-01

The animal and bacterial kingdoms have coevolved and coadapted in response to environmental selective pressures over hundreds of millions of years. The meta'omics revolution in both sequencing and its analytic pipelines is fostering an explosion of interest in how the gut microbiome impacts physiology and propensity to disease. Gut microbiome studies are inherently interdisciplinary, drawing on approaches and technical skill sets from the biomedical sciences, ecology, and computational biology. Central to unraveling the complex biology of environment, genetics, and microbiome interaction in human health and disease is a deeper understanding of the symbiosis between animals and bacteria. Experimental model systems, including mice, fish, insects, and the Hawaiian bobtail squid, continue to provide critical insight into how host–microbiota homeostasis is constructed and maintained. Here we consider how model systems are influencing current understanding of host–microbiota interactions and explore recent human microbiome studies. PMID:23592793

Driftcretions: The legacy impacts of driftwood on shoreline morphology

NASA Astrophysics Data System (ADS)

Kramer, Natalie; Wohl, Ellen

2015-07-01

This research demonstrates how vegetation interacts with physical processes to govern landscape development. We quantify and describe interactions among driftwood, sedimentation, and vegetation for Great Slave Lake, which is used as proxy for shoreline dynamics and landforms before deforestation and wood removal along major waterways. We introduce driftcretion to describe large, persistent concentrations of driftwood that interact with vegetation and sedimentation to influence shoreline evolution. We report the volume and distribution of driftwood along shorelines, the morphological impacts of driftwood delivery throughout the Holocene, and rates of driftwood accretion. Driftcretions facilitate the formation of complex, diverse morphologies that increase biological productivity and organic carbon capture and buffer against erosion. Driftcretions should be common on shorelines receiving a large wood supply and with processes which store wood permanently. We encourage others to work in these depositional zones to understand the physical and biological impacts of large wood export from river basins.