Science.gov

Sample records for expression-based network biology

  1. Expression-based network biology identifies immune-related functional modules involved in plant defense.

    PubMed

    Tully, Joel P; Hill, Aubrey E; Ahmed, Hadia M R; Whitley, Ryan; Skjellum, Anthony; Mukhtar, M Shahid

    2014-06-03

    Plants respond to diverse environmental cues including microbial perturbations by coordinated regulation of thousands of genes. These intricate transcriptional regulatory interactions depend on the recognition of specific promoter sequences by regulatory transcription factors. The combinatorial and cooperative action of multiple transcription factors defines a regulatory network that enables plant cells to respond to distinct biological signals. The identification of immune-related modules in large-scale transcriptional regulatory networks can reveal the mechanisms by which exposure to a pathogen elicits a precise phenotypic immune response. We have generated a large-scale immune co-expression network using a comprehensive set of Arabidopsis thaliana (hereafter Arabidopsis) transcriptomic data, which consists of a wide spectrum of immune responses to pathogens or pathogen-mimicking stimuli treatments. We employed both linear and non-linear models to generate Arabidopsis immune co-expression regulatory (AICR) network. We computed network topological properties and ascertained that this newly constructed immune network is densely connected, possesses hubs, exhibits high modularity, and displays hallmarks of a "real" biological network. We partitioned the network and identified 156 novel modules related to immune functions. Gene Ontology (GO) enrichment analyses provided insight into the key biological processes involved in determining finely tuned immune responses. We also developed novel software called OCCEAN (One Click Cis-regulatory Elements ANalysis) to discover statistically enriched promoter elements in the upstream regulatory regions of Arabidopsis at a whole genome level. We demonstrated that OCCEAN exhibits higher precision than the existing promoter element discovery tools. In light of known and newly discovered cis-regulatory elements, we evaluated biological significance of two key immune-related functional modules and proposed mechanism(s) to explain

  2. Significant Deregulated Pathways in Diabetes Type II Complications Identified through Expression Based Network Biology

    NASA Astrophysics Data System (ADS)

    Ukil, Sanchaita; Sinha, Meenakshee; Varshney, Lavneesh; Agrawal, Shipra

    Type 2 Diabetes is a complex multifactorial disease, which alters several signaling cascades giving rise to serious complications. It is one of the major risk factors for cardiovascular diseases. The present research work describes an integrated functional network biology approach to identify pathways that get transcriptionally altered and lead to complex complications thereby amplifying the phenotypic effect of the impaired disease state. We have identified two sub-network modules, which could be activated under abnormal circumstances in diabetes. Present work describes key proteins such as P85A and SRC serving as important nodes to mediate alternate signaling routes during diseased condition. P85A has been shown to be an important link between stress responsive MAPK and CVD markers involved in fibrosis. MAPK8 has been shown to interact with P85A and further activate CTGF through VEGF signaling. We have traced a novel and unique route correlating inflammation and fibrosis by considering P85A as a key mediator of signals. The next sub-network module shows SRC as a junction for various signaling processes, which results in interaction between NF-kB and beta catenin to cause cell death. The powerful interaction between these important genes in response to transcriptionally altered lipid metabolism and impaired inflammatory response via SRC causes apoptosis of cells. The crosstalk between inflammation, lipid homeostasis and stress, and their serious effects downstream have been explained in the present analyses.

  3. Research on Individualized Product Requirement Expression Based on Semantic Network

    NASA Astrophysics Data System (ADS)

    Yang, Qin; Pan, Xiuqin; Wei, Daozhu; Wu, Ke

    In order to establish an effective platform for individualized product development, the individualized product requirement expression forms were put forward. The diversity Semantic Network of product knowledge representation was researched based on the dualistic semantic network, and the product requirement framework model was established. Thereby the validity, reliability and consistency of the requirement expression process were ensured. Finally an example of customer requirement expression model about differential mechanism based on semantic network was described to satisfy with the individualized product design system.

  4. Synthetic biological networks

    NASA Astrophysics Data System (ADS)

    Archer, Eric; Süel, Gürol M.

    2013-09-01

    Despite their obvious relationship and overlap, the field of physics is blessed with many insightful laws, while such laws are sadly absent in biology. Here we aim to discuss how the rise of a more recent field known as synthetic biology may allow us to more directly test hypotheses regarding the possible design principles of natural biological networks and systems. In particular, this review focuses on synthetic gene regulatory networks engineered to perform specific functions or exhibit particular dynamic behaviors. Advances in synthetic biology may set the stage to uncover the relationship of potential biological principles to those developed in physics.

  5. Networks in Cell Biology

    NASA Astrophysics Data System (ADS)

    Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

    2010-05-01

    Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

  6. Structurally robust biological networks

    PubMed Central

    2011-01-01

    Background The molecular circuitry of living organisms performs remarkably robust regulatory tasks, despite the often intrinsic variability of its components. A large body of research has in fact highlighted that robustness is often a structural property of biological systems. However, there are few systematic methods to mathematically model and describe structural robustness. With a few exceptions, numerical studies are often the preferred approach to this type of investigation. Results In this paper, we propose a framework to analyze robust stability of equilibria in biological networks. We employ Lyapunov and invariant sets theory, focusing on the structure of ordinary differential equation models. Without resorting to extensive numerical simulations, often necessary to explore the behavior of a model in its parameter space, we provide rigorous proofs of robust stability of known bio-molecular networks. Our results are in line with existing literature. Conclusions The impact of our results is twofold: on the one hand, we highlight that classical and simple control theory methods are extremely useful to characterize the behavior of biological networks analytically. On the other hand, we are able to demonstrate that some biological networks are robust thanks to their structure and some qualitative properties of the interactions, regardless of the specific values of their parameters. PMID:21586168

  7. Integrating Biological Covariates into Gene Expression-Based Predictors of Radiation Sensitivity

    PubMed Central

    Kamath, Vidya P.; Torres-Roca, Javier F.

    2017-01-01

    The use of gene expression-based classifiers has resulted in a number of promising potential signatures of patient diagnosis, prognosis, and response to therapy. However, these approaches have also created difficulties in trying to use gene expression alone to predict a complex trait. A practical approach to this problem is to integrate existing biological knowledge with gene expression to build a composite predictor. We studied the problem of predicting radiation sensitivity within human cancer cell lines from gene expression. First, we present evidence for the need to integrate known biological conditions (tissue of origin, RAS, and p53 mutational status) into a gene expression prediction problem involving radiation sensitivity. Next, we demonstrate using linear regression, a technique for incorporating this knowledge. The resulting correlations between gene expression and radiation sensitivity improved through the use of this technique (best-fit adjusted R2 increased from 0.3 to 0.84). Overfitting of data was examined through the use of simulation. The results reinforce the concept that radiation sensitivity is not driven solely by gene expression, but rather by a combination of distinct parameters. We show that accounting for biological heterogeneity significantly improves the ability of the model to identify genes that are associated with radiosensitivity.

  8. Querying Large Biological Network Datasets

    ERIC Educational Resources Information Center

    Gulsoy, Gunhan

    2013-01-01

    New experimental methods has resulted in increasing amount of genetic interaction data to be generated every day. Biological networks are used to store genetic interaction data gathered. Increasing amount of data available requires fast large scale analysis methods. Therefore, we address the problem of querying large biological network datasets.…

  9. Querying Large Biological Network Datasets

    ERIC Educational Resources Information Center

    Gulsoy, Gunhan

    2013-01-01

    New experimental methods has resulted in increasing amount of genetic interaction data to be generated every day. Biological networks are used to store genetic interaction data gathered. Increasing amount of data available requires fast large scale analysis methods. Therefore, we address the problem of querying large biological network datasets.…

  10. Modular analysis of biological networks.

    PubMed

    Kaltenbach, Hans-Michael; Stelling, Jörg

    2012-01-01

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

  11. [Network structures in biological systems].

    PubMed

    Oleskin, A V

    2013-01-01

    Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

  12. Functional Aspects of Biological Networks

    NASA Astrophysics Data System (ADS)

    Sneppen, Kim

    2007-03-01

    We discuss biological networks with respect to 1) relative positioning and importance of high degree nodes, 2) function and signaling, 3) logic and dynamics of regulation. Visually the soft modularity of many real world networks can be characterized in terms of number of high and low degrees nodes positioned relative to each other in a landscape analogue with mountains (high-degree nodes) and valleys (low-degree nodes). In these terms biological networks looks like rugged landscapes with separated peaks, hub proteins, which each are roughly as essential as any of the individual proteins on the periphery of the hub. Within each sup-domain of a molecular network one can often identify dynamical feedback mechanisms that falls into combinations of positive and negative feedback circuits. We will illustrate this with examples taken from phage regulation and bacterial uptake and regulation of small molecules. In particular we find that a double negative regulation often are replaced by a single positive link in unrelated organisms with same functional requirements. Overall we argue that network topology primarily reflects functional constraints. References: S. Maslov and K. Sneppen. ``Computational architecture of the yeast regulatory network." Phys. Biol. 2:94 (2005) A. Trusina et al. ``Functional alignment of regulatory networks: A study of temerate phages". Plos Computational Biology 1:7 (2005). J.B. Axelsen et al. ``Degree Landscapes in Scale-Free Networks" physics/0512075 (2005). A. Trusina et al. ``Hierarchy and Anti-Hierarchy in Real and Scale Free networks." PRL 92:178702 (2004) S. Semsey et al. ``Genetic Regulation of Fluxes: Iron Homeostasis of Escherichia coli". (2006) q-bio.MN/0609042

  13. Social networks to biological networks: systems biology of Mycobacterium tuberculosis.

    PubMed

    Vashisht, Rohit; Bhardwaj, Anshu; Osdd Consortium; Brahmachari, Samir K

    2013-07-01

    Contextualizing relevant information to construct a network that represents a given biological process presents a fundamental challenge in the network science of biology. The quality of network for the organism of interest is critically dependent on the extent of functional annotation of its genome. Mostly the automated annotation pipelines do not account for unstructured information present in volumes of literature and hence large fraction of genome remains poorly annotated. However, if used, this information could substantially enhance the functional annotation of a genome, aiding the development of a more comprehensive network. Mining unstructured information buried in volumes of literature often requires manual intervention to a great extent and thus becomes a bottleneck for most of the automated pipelines. In this review, we discuss the potential of scientific social networking as a solution for systematic manual mining of data. Focusing on Mycobacterium tuberculosis, as a case study, we discuss our open innovative approach for the functional annotation of its genome. Furthermore, we highlight the strength of such collated structured data in the context of drug target prediction based on systems level analysis of pathogen.

  14. Reputation-based collaborative network biology.

    PubMed

    Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Fields, R Brett; Hayes, William; Hoeng, Julia; Park, Jennifer S; Peitsch, Manuel C

    2015-01-01

    A pilot reputation-based collaborative network biology platform, Bionet, was developed for use in the sbv IMPROVER Network Verification Challenge to verify and enhance previously developed networks describing key aspects of lung biology. Bionet was successful in capturing a more comprehensive view of the biology associated with each network using the collective intelligence and knowledge of the crowd. One key learning point from the pilot was that using a standardized biological knowledge representation language such as BEL is critical to the success of a collaborative network biology platform. Overall, Bionet demonstrated that this approach to collaborative network biology is highly viable. Improving this platform for de novo creation of biological networks and network curation with the suggested enhancements for scalability will serve both academic and industry systems biology communities.

  15. Network dynamics and systems biology

    NASA Astrophysics Data System (ADS)

    Norrell, Johannes A.

    The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior. In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects---an asymmetry between on and off states, and a decaying memory of events in each element's inputs---that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors. Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the

  16. Efficient, sparse biological network determination

    PubMed Central

    August, Elias; Papachristodoulou, Antonis

    2009-01-01

    Background Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open. Results We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections) while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have – something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters. Conclusion The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational) and uncertainties in the data are taken into account. It also produces a network structure that can explain the real experimental

  17. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach

    PubMed Central

    Li, Jun; Zhao, Patrick X.

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/. PMID:27446133

  18. Gene expression-based biological test for major depressive disorder: an advanced study

    PubMed Central

    Watanabe, Shin-ya; Numata, Shusuke; Iga, Jun-ichi; Kinoshita, Makoto; Umehara, Hidehiro; Ishii, Kazuo; Ohmori, Tetsuro

    2017-01-01

    Purpose Recently, we could distinguished patients with major depressive disorder (MDD) from nonpsychiatric controls with high accuracy using a panel of five gene expression markers (ARHGAP24, HDAC5, PDGFC, PRNP, and SLC6A4) in leukocyte. In the present study, we examined whether this biological test is able to discriminate patients with MDD from those without MDD, including those with schizophrenia and bipolar disorder. Patients and methods We measured messenger ribonucleic acid expression levels of the aforementioned five genes in peripheral leukocytes in 17 patients with schizophrenia and 36 patients with bipolar disorder using quantitative real-time polymerase chain reaction (PCR), and we combined these expression data with our previous expression data of 25 patients with MDD and 25 controls. Subsequently, a linear discriminant function was developed for use in discriminating between patients with MDD and without MDD. Results This expression panel was able to segregate patients with MDD from those without MDD with a sensitivity and specificity of 64% and 67.9%, respectively. Conclusion Further research to identify MDD-specific markers is needed to improve the performance of this biological test. PMID:28260899

  19. Invasion exponents in biological networks

    NASA Astrophysics Data System (ADS)

    Demetrius, Lloyd; Gundlach, Volker Matthias; Ochs, Gunter

    2009-03-01

    This article is concerned with the characterization of invasion exponents in biological networks defined by a population of replicating elements: molecules, cells, higher organisms. We show that the outcome of competition between an invader and a resident population is a stochastic process, determined by the rate at which a population returns to its steady state after a random perturbation in the parameters that characterize the replicating elements. This return rate is defined by the macroscopic parameter evolutionary entropy, a measure of the diversity of the interaction between the individuals in the population. We also show that the evolutionary stability of a population, that is the invulnerability of a resident to the introduction of an invader competing for the available resources, are given by extremal states of entropy. These results which pertain to networks of interacting molecules, cells and higher organisms, are generalizations of results established for demographic networks, that is populations of replicating organisms parametrized by the ages at which they reproduce and die.

  20. Measuring the Evolutionary Rewiring of Biological Networks

    PubMed Central

    Shou, Chong; Bhardwaj, Nitin; Lam, Hugo Y. K.; Yan, Koon-Kiu; Kim, Philip M.; Snyder, Michael; Gerstein, Mark B.

    2011-01-01

    We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or “rewire”, at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of “commonplace” networks such as family trees, co-authorships and linux-kernel function dependencies. PMID:21253555

  1. Optimizing Nutrient Uptake in Biological Transport Networks

    NASA Astrophysics Data System (ADS)

    Ronellenfitsch, Henrik; Katifori, Eleni

    2013-03-01

    Many biological systems employ complex networks of vascular tubes to facilitate transport of solute nutrients, examples include the vascular system of plants (phloem), some fungi, and the slime-mold Physarum. It is believed that such networks are optimized through evolution for carrying out their designated task. We propose a set of hydrodynamic governing equations for solute transport in a complex network, and obtain the optimal network architecture for various classes of optimizing functionals. We finally discuss the topological properties and statistical mechanics of the resulting complex networks, and examine correspondence of the obtained networks to those found in actual biological systems.

  2. Comparing biological networks via graph compression

    PubMed Central

    2010-01-01

    Background Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks, it is reasonable to try to apply data compression methods to comparison of biological networks. In existing compression methods, the uniqueness of compression results is not guaranteed because there is some ambiguity in selection of overlapping edges. Results This paper proposes novel efficient methods, CompressEdge and CompressVertices, for comparing large biological networks. In the proposed methods, an original network structure is compressed by iteratively contracting identical edges and sets of connected edges. Then, the similarity of two networks is measured by a compression ratio of the concatenated networks. The proposed methods are applied to comparison of metabolic networks of several organisms, H. sapiens, M. musculus, A. thaliana, D. melanogaster, C. elegans, E. coli, S. cerevisiae, and B. subtilis, and are compared with an existing method. These results suggest that our methods can efficiently measure the similarities between metabolic networks. Conclusions Our proposed algorithms, which compress node-labeled networks, are useful for measuring the similarity of large biological networks. PMID:20840727

  3. A model of gene expression based on random dynamical systems reveals modularity properties of gene regulatory networks.

    PubMed

    Antoneli, Fernando; Ferreira, Renata C; Briones, Marcelo R S

    2016-06-01

    Here we propose a new approach to modeling gene expression based on the theory of random dynamical systems (RDS) that provides a general coupling prescription between the nodes of any given regulatory network given the dynamics of each node is modeled by a RDS. The main virtues of this approach are the following: (i) it provides a natural way to obtain arbitrarily large networks by coupling together simple basic pieces, thus revealing the modularity of regulatory networks; (ii) the assumptions about the stochastic processes used in the modeling are fairly general, in the sense that the only requirement is stationarity; (iii) there is a well developed mathematical theory, which is a blend of smooth dynamical systems theory, ergodic theory and stochastic analysis that allows one to extract relevant dynamical and statistical information without solving the system; (iv) one may obtain the classical rate equations form the corresponding stochastic version by averaging the dynamic random variables (small noise limit). It is important to emphasize that unlike the deterministic case, where coupling two equations is a trivial matter, coupling two RDS is non-trivial, specially in our case, where the coupling is performed between a state variable of one gene and the switching stochastic process of another gene and, hence, it is not a priori true that the resulting coupled system will satisfy the definition of a random dynamical system. We shall provide the necessary arguments that ensure that our coupling prescription does indeed furnish a coupled regulatory network of random dynamical systems. Finally, the fact that classical rate equations are the small noise limit of our stochastic model ensures that any validation or prediction made on the basis of the classical theory is also a validation or prediction of our model. We illustrate our framework with some simple examples of single-gene system and network motifs.

  4. On Crowd-verification of Biological Networks

    PubMed Central

    Ansari, Sam; Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Hayes, William; Hoeng, Julia; Iskandar, Anita; Kleiman, Robin; Norel, Raquel; O’Neel, Bruce; Peitsch, Manuel C.; Poussin, Carine; Pratt, Dexter; Rhrissorrakrai, Kahn; Schlage, Walter K.; Stolovitzky, Gustavo; Talikka, Marja

    2013-01-01

    Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community. PMID:24151423

  5. Complex Networks: from Graph Theory to Biology

    NASA Astrophysics Data System (ADS)

    Lesne, Annick

    2006-12-01

    The aim of this text is to show the central role played by networks in complex system science. A remarkable feature of network studies is to lie at the crossroads of different disciplines, from mathematics (graph theory, combinatorics, probability theory) to physics (statistical physics of networks) to computer science (network generating algorithms, combinatorial optimization) to biological issues (regulatory networks). New paradigms recently appeared, like that of ‘scale-free networks’ providing an alternative to the random graph model introduced long ago by Erdös and Renyi. With the notion of statistical ensemble and methods originally introduced for percolation networks, statistical physics is of high relevance to get a deep account of topological and statistical properties of a network. Then their consequences on the dynamics taking place in the network should be investigated. Impact of network theory is huge in all natural sciences, especially in biology with gene networks, metabolic networks, neural networks or food webs. I illustrate this brief overview with a recent work on the influence of network topology on the dynamics of coupled excitable units, and the insights it provides about network emerging features, robustness of network behaviors, and the notion of static or dynamic motif.

  6. Biological Networks for Cancer Candidate Biomarkers Discovery

    PubMed Central

    Yan, Wenying; Xue, Wenjin; Chen, Jiajia; Hu, Guang

    2016-01-01

    Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. PMID:27625573

  7. Evolution of Complex Modular Biological Networks

    PubMed Central

    Hintze, Arend; Adami, Christoph

    2008-01-01

    Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein–protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks. PMID:18266463

  8. Biological networks: the tinkerer as an engineer.

    PubMed

    Alon, U

    2003-09-26

    This viewpoint comments on recent advances in understanding the design principles of biological networks. It highlights the surprising discovery of "good-engineering" principles in biochemical circuitry that evolved by random tinkering.

  9. Complex networks and simple models in biology

    PubMed Central

    de Silva, Eric; Stumpf, Michael P.H

    2005-01-01

    The analysis of molecular networks, such as transcriptional, metabolic and protein interaction networks, has progressed substantially because of the power of models from statistical physics. Increasingly, the data are becoming so detailed—though not always complete or correct—that the simple models are reaching the limits of their usefulness. Here, we will discuss how network information can be described and to some extent quantified. In particular statistics offers a range of tools, such as model selection, which have not yet been widely applied in the analysis of biological networks. We will also outline a number of present challenges posed by biological network data in systems biology, and the extent to which these can be addressed by new developments in statistics, physics and applied mathematics. PMID:16849202

  10. Asian Network for Biological Sciences (ANBS).

    ERIC Educational Resources Information Center

    Asian Network for Biological Sciences.

    The Asian Network for Biological Sciences (ANBS) is a group of institutions, laboratories, research centers, and scholars who are willing to cooperate in programs and activities aimed at improving teaching and research in the biological sciences. This publication: (1) outlines ANBS aims and objectives; (2) describes major activities in the past;…

  11. Asian Network for Biological Sciences (ANBS).

    ERIC Educational Resources Information Center

    Asian Network for Biological Sciences.

    The Asian Network for Biological Sciences (ANBS) is a group of institutions, laboratories, research centers, and scholars who are willing to cooperate in programs and activities aimed at improving teaching and research in the biological sciences. This publication: (1) outlines ANBS aims and objectives; (2) describes major activities in the past;…

  12. Network biology: a direct approach to study biological function.

    PubMed

    Emmert-Streib, Frank; Glazko, Galina V

    2011-01-01

    In this paper we discuss the dualism of gene networks and their role in systems biology. We argue that gene networks (1) can serve as a conceptual framework, forming a fundamental level of a phenomenological description, and (2) are a means to represent and analyze data. The latter point does not only allow a systems analysis but is even amenable for a direct approach to study biological function. Here we focus on the clarity of our main arguments and conceptual meaning of gene networks, rather than the causal inference of gene networks from data. WIREs Syst Biol Med 2011 3 379-391 DOI: 10.1002/wsbm.134 For further resources related to this article, please visit the WIREs website. Copyright © 2010 John Wiley & Sons, Inc.

  13. 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.

  14. 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

  15. Quantifying evolvability in small biological networks

    SciTech Connect

    Nemenman, Ilya; Mugler, Andrew; Ziv, Etay; Wiggins, Chris H

    2008-01-01

    The authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. (Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without completely losing it along the way. This property further underscores the evolvability of the networks.

  16. Qualitative networks: a symbolic approach to analyze biological signaling networks

    PubMed Central

    Schaub, Marc A; Henzinger, Thomas A; Fisher, Jasmin

    2007-01-01

    Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology. PMID:17408511

  17. Network biology methods integrating biological data for translational science.

    PubMed

    Bebek, Gurkan; Koyutürk, Mehmet; Price, Nathan D; Chance, Mark R

    2012-07-01

    The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

  18. Network Reverse Engineering Approach in Synthetic Biology

    NASA Astrophysics Data System (ADS)

    Zhang, Haoqian; Liu, Ao; Lu, Yuheng; Sheng, Ying; Wu, Qianzhu; Yin, Zhenzhen; Chen, Yiwei; Liu, Zairan; Pan, Heng; Ouyang, Qi

    2013-12-01

    Synthetic biology is a new branch of interdisciplinary science that has been developed in recent years. The main purpose of synthetic biology is to apply successful principles that have been developed in electronic and chemical engineering to develop basic biological functional modules, and through rational design, develop man-made biological systems that have predicted useful functions. Here, we discuss an important principle in rational design of functional biological circuits: the reverse engineering design. We will use a research project that was conducted at Peking University for the International Genetic Engineering Machine Competition (iGEM) to illustrate the principle: synthesis a cell which has a semi-log dose-response to the environment. Through this work we try to demonstrate the potential application of network engineering in synthetic biology.

  19. Biological and Environmental Research Network Requirements

    SciTech Connect

    Balaji, V.; Boden, Tom; Cowley, Dave; Dart, Eli; Dattoria, Vince; Desai, Narayan; Egan, Rob; Foster, Ian; Goldstone, Robin; Gregurick, Susan; Houghton, John; Izaurralde, Cesar; Johnston, Bill; Joseph, Renu; Kleese-van Dam, Kerstin; Lipton, Mary; Monga, Inder; Pritchard, Matt; Rotman, Lauren; Strand, Gary; Stuart, Cory; Tatusova, Tatiana; Tierney, Brian; Thomas, Brian; Williams, Dean N.; Zurawski, Jason

    2013-09-01

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet be a highly successful enabler of scientific discovery for over 25 years. In November 2012, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the BER program office. Several key findings resulted from the review. Among them: 1) The scale of data sets available to science collaborations continues to increase exponentially. This has broad impact, both on the network and on the computational and storage systems connected to the network. 2) Many science collaborations require assistance to cope with the systems and network engineering challenges inherent in managing the rapid growth in data scale. 3) Several science domains operate distributed facilities that rely on high-performance networking for success. Key examples illustrated in this report include the Earth System Grid Federation (ESGF) and the Systems Biology Knowledgebase (KBase). This report expands on these points, and addresses others as well. The report contains a findings section as well as the text of the case studies discussed at the review.

  20. Dense module enumeration in biological networks

    NASA Astrophysics Data System (ADS)

    Tsuda, Koji; Georgii, Elisabeth

    2009-12-01

    Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

  1. Structural determinants of criticality in biological networks

    PubMed Central

    Valverde, Sergi; Ohse, Sebastian; Turalska, Malgorzata; West, Bruce J.; Garcia-Ojalvo, Jordi

    2015-01-01

    Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness, and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behavior in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organization can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system toward criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality. PMID:26005422

  2. Using graph theory to analyze biological networks

    PubMed Central

    2011-01-01

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

  3. Discovery of Chemical Toxicity via Biological Networks and Systems Biology

    SciTech Connect

    Perkins, Edward; Habib, Tanwir; Guan, Xin; Escalon, Barbara; Falciani, Francesco; Chipman, J.K.; Antczak, Philipp; Edwards, Stephen; Taylor, Ronald C.; Vulpe, Chris; Loguinov, Alexandre; Van Aggelen, Graham; Villeneuve, Daniel L.; Garcia-Reyero, Natalia

    2010-09-30

    Both soldiers and animals are exposed to many chemicals as the result of military activities. Tools are needed to understand the hazards and risks that chemicals and new materials pose to soldiers and the environment. We have investigated the potential of global gene regulatory networks in understanding the impact of chemicals on reproduction. We characterized effects of chemicals on ovaries of the model animal system, the Fathead minnow (Pimopheles promelas) connecting chemical impacts on gene expression to circulating blood levels of the hormones testosterone and estradiol in addition to the egg yolk protein vitellogenin. We describe the application of reverse engineering complex interaction networks from high dimensional gene expression data to characterize chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis that governs reproduction in fathead minnows. The construction of global gene regulatory networks provides deep insights into how drugs and chemicals effect key organs and biological pathways.

  4. Learning biological networks: from modules to dynamics.

    PubMed

    Bonneau, Richard

    2008-11-01

    Learning regulatory networks from genomics data is an important problem with applications spanning all of biology and biomedicine. Functional genomics projects offer a cost-effective means of greatly expanding the completeness of our regulatory models, and for some prokaryotic organisms they offer a means of learning accurate models that incorporate the majority of the genome. There are, however, several reasons to believe that regulatory network inference is beyond our current reach, such as (i) the combinatorics of the problem, (ii) factors we can't (or don't often) collect genome-wide measurements for and (iii) dynamics that elude cost-effective experimental designs. Recent works have demonstrated the ability to reconstruct large fractions of prokaryotic regulatory networks from compendiums of genomics data; they have also demonstrated that these global regulatory models can be used to predict the dynamics of the transcriptome. We review an overall strategy for the reconstruction of global networks based on these results in microbial systems.

  5. Some physics problems in biological networks

    NASA Astrophysics Data System (ADS)

    Bialek, William

    2007-03-01

    Most of the interesting things that happen in living organisms require interactions among many components, and it is convenient to think of these as a ``network'' of interactions. We use this language at the level of single molecules (the network of interactions among amino acids that determine protein structure), single cells (the network of protein-DNA interactions responsible for the regulation of gene expression) and complex multicellular organisms (the networks of neurons in our brain). In this talk I'll try to look at two very different kinds of theoretical physics problems that arise in thinking about such networks. The first problems are phenomenological: Given what our experimentalists friends can measure, can we generate a global view of network function and dynamics? I'll argue that maximum entropy methods can be useful here, and show how such methods have been used in very recent work on networks of neurons, enzymes, genes and (in disguise) amino acids. In this line of reasoning there are of course interesting connections to statistical mechanics, and we'll see that natural statistical mechanics questions about the underlying models actually teach us something about how the real biological system works, in ways that will be tested through new experiments. In the second half of the talk I'll ask if there are principles from which we might actually be able to predict the structure and dynamics of biological networks. I'll focus on optimization principles, in particular the optimization of information flow in transcriptional regulation. Even setting up these arguments forces us to think critically about our understanding of the signals, specificity and noise in these systems, all current topics of research. Although we don't know if we have the right principles, trying to work out the consequences of such optimization again suggests new experiments.

  6. Functional model of biological neural networks

    PubMed Central

    2010-01-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks. PMID:22132040

  7. Invariance kernel of biological regulatory networks.

    PubMed

    Ahmad, Jamil; Roux, Olivier

    2010-01-01

    The analysis of Biological Regulatory Network (BRN) leads to the computing of the set of the possible behaviours of the biological components. These behaviours are seen as trajectories and we are specifically interested in cyclic trajectories since they stand for stability. The set of cycles is given by the so-called invariance kernel of a BRN. This paper presents a method for deriving symbolic formulae for the length, volume and diameter of a cylindrical invariance kernel. These formulae are expressed in terms of delay parameters expressions and give the existence of an invariance kernel and a hint of the number of cyclic trajectories.

  8. Bayesian Network Webserver: a comprehensive tool for biological network modeling.

    PubMed

    Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

    2013-11-01

    The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

  9. Adaptation and optimization of biological transport networks.

    PubMed

    Hu, Dan; Cai, David

    2013-09-27

    It has been hypothesized that topological structures of biological transport networks are consequences of energy optimization. Motivated by experimental observation, we propose that adaptation dynamics may underlie this optimization. In contrast to the global nature of optimization, our adaptation dynamics responds only to local information and can naturally incorporate fluctuations in flow distributions. The adaptation dynamics minimizes the global energy consumption to produce optimal networks, which may possess hierarchical loop structures in the presence of strong fluctuations in flow distribution. We further show that there may exist a new phase transition as there is a critical open probability of sinks, above which there are only trees for network structures whereas below which loops begin to emerge.

  10. Learning and coding in biological neural networks

    NASA Astrophysics Data System (ADS)

    Fiete, Ila Rani

    How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

  11. PREMER: a Tool to Infer Biological Networks.

    PubMed

    Villaverde, Alejandro F; Becker, Kolja; Banga, Julio R

    2017-10-04

    Inferring the structure of unknown cellular networks is a main challenge in computational biology. Data-driven approaches based on information theory can determine the existence of interactions among network nodes automatically. However, the elucidation of certain features - such as distinguishing between direct and indirect interactions or determining the direction of a causal link - requires estimating information-theoretic quantities in a multidimensional space. This can be a computationally demanding task, which acts as a bottleneck for the application of elaborate algorithms to large-scale network inference problems. The computational cost of such calculations can be alleviated by the use of compiled programs and parallelization. To this end we have developed PREMER (Parallel Reverse Engineering with Mutual information & Entropy Reduction), a software toolbox that can run in parallel and sequential environments. It uses information theoretic criteria to recover network topology and determine the strength and causality of interactions, and allows incorporating prior knowledge, imputing missing data, and correcting outliers. PREMER is a free, open source software tool that does not require any commercial software. Its core algorithms are programmed in FORTRAN 90 and implement OpenMP directives. It has user interfaces in Python and MATLAB/Octave, and runs on Windows, Linux and OSX (https://sites.google.com/site/premertoolbox/).

  12. Rescue Interventions in Biological and Physical Networks

    NASA Astrophysics Data System (ADS)

    Cornelius, Sean

    2011-03-01

    Gene knockout experiments on single cells have established that expression of most genes is not needed for optimal growth. Yet, environmental and genetic perturbations to these organisms are known to be accompanied by the transient activation of a large number of latent metabolic pathways, suggesting that the temporarily activated reactions increase growth in the presence of perturbations. We have tested this hypothesis computationally and found, surprisingly, that the availability of latent pathways tends in fact to inhibit growth after genetic perturbations. This adverse effect indicates that latent pathway activation is derivative of a suboptimal response and that consequently, growth can actually be improved by removing these pathways from the network. In this talk, I will relate this counterintuitive effect to very recent research showing that a loss in network performance inflicted by an external perturbation can be mitigated by the application of additional perturbations. The challenge is to identify such ``rescues'' under constraints that limit the type of perturbations that can be made. I will present an approach to identify such eligible rescues for general networks modeled as dynamical systems, and present computational examples for biological and physical networks.

  13. New scaling relation for information transfer in biological networks

    PubMed Central

    Kim, Hyunju; Davies, Paul; Walker, Sara Imari

    2015-01-01

    We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883

  14. Local versus global biological network alignment

    PubMed Central

    Meng, Lei; Striegel, Aaron; Milenković, Tijana

    2016-01-01

    Motivation: Network alignment (NA) aims to find regions of similarities between species’ molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories. Results: We introduce new measures of alignment quality that allow for fair comparison of the different LNA and GNA outputs, as such measures do not exist. We provide user-friendly software for efficient alignment evaluation that implements the new and existing measures. We evaluate prominent LNA and GNA methods on synthetic and real-world biological networks. We study the effect on alignment quality of using different interaction types and confidence levels. We find that the superiority of one NA category over the other is context-dependent. Further, when we contrast LNA and GNA in the application of learning novel protein functional knowledge, the two produce very different predictions, indicating their complementarity. Our results and software provide guidelines for future NA method development and evaluation. Availability and implementation: Software: http://www.nd.edu/~cone/LNA_GNA Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27357169

  15. Organization of Excitable Dynamics in Hierarchical Biological Networks

    PubMed Central

    Müller-Linow, Mark; Hilgetag, Claus C.; Hütt, Marc-Thorsten

    2008-01-01

    This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks. PMID:18818769

  16. Study of the structure and dynamics of complex biological networks

    NASA Astrophysics Data System (ADS)

    Samal, Areejit

    2008-12-01

    In this thesis, we have studied the large scale structure and system level dynamics of certain biological networks using tools from graph theory, computational biology and dynamical systems. We study the structure and dynamics of large scale metabolic networks inside three organisms, Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus. We also study the dynamics of the large scale genetic network controlling E. coli metabolism. We have tried to explain the observed system level dynamical properties of these networks in terms of their underlying structure. Our studies of the system level dynamics of these large scale biological networks provide a different perspective on their functioning compared to that obtained from purely structural studies. Our study also leads to some new insights on features such as robustness, fragility and modularity of these large scale biological networks. We also shed light on how different networks inside the cell such as metabolic networks and genetic networks are interrelated to each other.

  17. Modeling and Analysis of Modular Structure in Diverse Biological Networks.

    PubMed

    Bader, Al-Anzi; Sherif, Gerges; Noah, Olsman; Christopher, Ormerod; Georgios, Piliouras; John, Ormerod; Kai, Zinn

    2017-04-07

    Biological networks, like most engineered networks, are not the product of a singular design but rather are the result of a long process of refinement and optimization. Many large real-world networks are comprised of well-defined and meaningful smaller modules. While engineered networks are designed and refined by humans with particular goals in mind, biological networks are created by the selective pressures of evolution. In this paper, we seek to define aspects of network architecture that are shared among different types of evolved biological networks. First, we developed a new mathematical model, the Stochastic Block Model with Path Selection (SBM-PS) that simulates biological network formation based on the selection of edges that increase clustering. SBM-PS can produce modular networks whose properties resemble those of real networks. Second, we analyzed three real networks of very different types, and showed that all three can be fit well by the SBM-PS model. Third, we showed that modular elements within the three networks correspond to meaningful biological structures. The networks chosen for analysis were a proteomic network composed of all proteins required for mitochondrial function in budding yeast, a mesoscale anatomical network composed of axonal connections among regions of the mouse brain, and the connectome of individual neurons in the nematode C. elegans. We find that the three networks have common architectural features, and each can be divided into subnetworks with characteristic topologies that control specific phenotypic outputs.

  18. Noncommutative Biology: Sequential Regulation of Complex Networks

    PubMed Central

    Letsou, William; Cai, Long

    2016-01-01

    Single-cell variability in gene expression is important for generating distinct cell types, but it is unclear how cells use the same set of regulatory molecules to specifically control similarly regulated genes. While combinatorial binding of transcription factors at promoters has been proposed as a solution for cell-type specific gene expression, we found that such models resulted in substantial information bottlenecks. We sought to understand the consequences of adopting sequential logic wherein the time-ordering of factors informs the final outcome. We showed that with noncommutative control, it is possible to independently control targets that would otherwise be activated simultaneously using combinatorial logic. Consequently, sequential logic overcomes the information bottleneck inherent in complex networks. We derived scaling laws for two noncommutative models of regulation, motivated by phosphorylation/neural networks and chromosome folding, respectively, and showed that they scale super-exponentially in the number of regulators. We also showed that specificity in control is robust to the loss of a regulator. Lastly, we connected these theoretical results to real biological networks that demonstrate specificity in the context of promiscuity. These results show that achieving a desired outcome often necessitates roundabout steps. PMID:27560383

  19. Optimizing information flow in biological networks

    NASA Astrophysics Data System (ADS)

    Bialek, William

    2009-03-01

    The generation of physicists who turned to the phenomena of life in the 1930s realized that to understand these phenomena one would need to track not just the flow of energy (as in inanimate systems) but also the flow of information. It would take more than a decade before Shannon provided the tools to formalize this intuition, making precise the connection between entropy and information. Since Shannon, many investigators have explored the possibility that biological mechanisms are selected to maximize the efficiency with which information is transmitted or represented, subject to fundamental physical constraints. I will survey these efforts, emphasizing that the same principles are being used in thinking about biological systems at very different levels of organization, from bacteria to brains. Although sometimes submerged under concerns about particular systems, the idea that information flow is optimized provides us with a candidate for a real theory of biological networks, rather than just a collection of parameterized models. I will try to explain why I think the time is right to focus on this grand theoretical goal, pointing to some key open problems and opportunities for connection to emerging experiments.

  20. Analyzing large biological datasets with association networks.

    PubMed

    Karpinets, Tatiana V; Park, Byung H; Uberbacher, Edward C

    2012-09-01

    Due to advances in high-throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches that process collected data into new knowledge in a timely manner. In this study, we propose a computational framework for discovering modular structure, relationships and regularities in complex data. The framework utilizes a semantic-preserving vocabulary to convert records of biological annotations of an object, such as an organism, gene, chemical or sequence, into networks (Anets) of the associated annotations. An association between a pair of annotations in an Anet is determined by the similarity of their co-occurrence pattern with all other annotations in the data. This feature captures associations between annotations that do not necessarily co-occur with each other and facilitates discovery of the most significant relationships in the collected data through clustering and visualization of the Anet. To demonstrate this approach, we applied the framework to the analysis of metadata from the Genomes OnLine Database and produced a biological map of sequenced prokaryotic organisms with three major clusters of metadata that represent pathogens, environmental isolates and plant symbionts.

  1. Predicting disease associations via biological network analysis.

    PubMed

    Sun, Kai; Gonçalves, Joana P; Larminie, Chris; Przulj, Nataša

    2014-09-17

    Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment. We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships. We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.

  2. Energy coding in biological neural networks

    PubMed Central

    Zhang, Zhikang

    2007-01-01

    According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, we present a brand new scientific theory that offers an unique mechanism for brain information processing. We demonstrate that the neural coding produced by the activity of the brain is well described by our theory of energy coding. Due to the energy coding model’s ability to reveal mechanisms of brain information processing based upon known biophysical properties, we can not only reproduce various experimental results of neuro-electrophysiology, but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, we estimate that the theory has very important consequences for quantitative research of cognitive function. PMID:19003513

  3. Energy coding in biological neural networks.

    PubMed

    Wang, Rubin; Zhang, Zhikang

    2007-09-01

    According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, we present a brand new scientific theory that offers an unique mechanism for brain information processing. We demonstrate that the neural coding produced by the activity of the brain is well described by our theory of energy coding. Due to the energy coding model's ability to reveal mechanisms of brain information processing based upon known biophysical properties, we can not only reproduce various experimental results of neuro-electrophysiology, but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, we estimate that the theory has very important consequences for quantitative research of cognitive function.

  4. Structural Measures for Network Biology Using QuACN

    PubMed Central

    2011-01-01

    Background Structural measures for networks have been extensively developed, but many of them have not yet demonstrated their sustainably. That means, it remains often unclear whether a particular measure is useful and feasible to solve a particular problem in network biology. Exemplarily, the classification of complex biological networks can be named, for which structural measures are used leading to a minimal classification error. Hence, there is a strong need to provide freely available software packages to calculate and demonstrate the appropriate usage of structural graph measures in network biology. Results Here, we discuss topological network descriptors that are implemented in the R-package QuACN and demonstrate their behavior and characteristics by applying them to a set of example graphs. Moreover, we show a representative application to illustrate their capabilities for classifying biological networks. In particular, we infer gene regulatory networks from microarray data and classify them by methods provided by QuACN. Note that QuACN is the first freely available software written in R containing a large number of structural graph measures. Conclusion The R package QuACN is under ongoing development and we add promising groups of topological network descriptors continuously. The package can be used to answer intriguing research questions in network biology, e.g., classifying biological data or identifying meaningful biological features, by analyzing the topology of biological networks. PMID:22195644

  5. Topological implications of negative curvature for biological and social networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; DasGupta, Bhaskar; Mobasheri, Nasim

    2014-03-01

    Network measures that reflect the most salient properties of complex large-scale networks are in high demand in the network research community. In this paper we adapt a combinatorial measure of negative curvature (also called hyperbolicity) to parametrized finite networks, and show that a variety of biological and social networks are hyperbolic. This hyperbolicity property has strong implications on the higher-order connectivity and other topological properties of these networks. Specifically, we derive and prove bounds on the distance among shortest or approximately shortest paths in hyperbolic networks. We describe two implications of these bounds to crosstalk in biological networks, and to the existence of central, influential neighborhoods in both biological and social networks.

  6. The Dichotomy in Degree Correlation of Biological Networks

    PubMed Central

    Hao, Dapeng; Li, Chuanxing

    2011-01-01

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

  7. Application of random matrix theory to biological networks

    NASA Astrophysics Data System (ADS)

    Luo, Feng; Zhong, Jianxin; Yang, Yunfeng; Scheuermann, Richard H.; Zhou, Jizhong

    2006-09-01

    We show that spectral fluctuation of interaction matrices of a yeast protein protein interaction network and a yeast metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson distribution. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at network scale. The transition point provides a new objective approach for the identification of functional modules.

  8. Asymmetric Branching in Biological Resource Distribution Networks

    NASA Astrophysics Data System (ADS)

    Brummer, Alexander Byers

    There is a remarkable relationship between an organism's metabolic rate (resting power consumption) and the organism's mass. It may be a universal law of nature that an organism's resting metabolic rate is proportional to its mass to the power of 3/4. This relationship, known as Kleiber's Law, appears to be valid for both plants and animals. This law is important because it implies that larger organisms are more efficient than smaller organisms, and knowledge regarding metabolic rates are essential to a multitude of other fields in ecology and biology. This includes modeling the interactions of many species across multiple trophic levels, distributions of species abundances across large spatial landscapes, and even medical diagnostics for respiratory and cardiovascular pathologies. Previous models of vascular networks that seek to identify the origin of metabolic scaling have all been based on the unrealistic assumption of perfectly symmetric branching. In this dissertation I will present a theory of asymmetric branching in self-similar vascular networks (published by Brummer et al. in [9]). The theory shows that there can exist a suite of vascular forms that result in the often observed 3/4 metabolic scaling exponent of Kleiber's Law. Furthermore, the theory makes predictions regarding major morphological features related to vascular branching patterns and their relationships to metabolic scaling. These predictions are suggestive of evolutionary convergence in vascular branching. To test these predictions, I will present an analysis of real mammalian and plant vascular data that shows: (i) broad patterns in vascular networks across entire animal kingdoms and (ii) within these patterns, plant and mammalian vascular networks can be uniquely distinguished from one another (publication in preparation by Brummer et al.). I will also present results from a computational study in support of point (i). Namely, that asymmetric branching may be the optimal strategy to

  9. OWL reasoning framework over big biological knowledge network.

    PubMed

    Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

    2014-01-01

    Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

  10. OWL Reasoning Framework over Big Biological Knowledge Network

    PubMed Central

    Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

    2014-01-01

    Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity. PMID:24877076

  11. Computer-Based Semantic Network in Molecular Biology: A Demonstration.

    ERIC Educational Resources Information Center

    Callman, Joshua L.; And Others

    This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

  12. Computer-Based Semantic Network in Molecular Biology: A Demonstration.

    ERIC Educational Resources Information Center

    Callman, Joshua L.; And Others

    This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

  13. Biology Question Generation from a Semantic Network

    NASA Astrophysics Data System (ADS)

    Zhang, Lishan

    Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions. To boost students' learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student's current competence so that a suitable question could be selected based on the student's previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group. To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators. A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from

  14. Analyzing large biological datasets with association networks

    SciTech Connect

    Karpinets, T. V.; Park, B. H.; Uberbacher, E. C.

    2012-05-25

    Due to advances in high throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches for timely processing of the collected data into new knowledge. In this study we address this problem by developing a new approach for discovering modular structure, relationships and regularities in complex data. These goals are achieved by converting records of biological annotations of an object, like organism, gene, chemical, sequence, into networks (Anets) and rules (Arules) of the associated annotations. Anets are based on similarity of annotation profiles of objects and can be further analyzed and visualized providing a compact birds-eye view of most significant relationships in the collected data and a way of their clustering and classification. Arules are generated by Apriori considering each record of annotations as a transaction and augmenting each annotation item by its type. Arules provide a way to validate relationships discovered by Anets producing comprehensive statistics on frequently associated annotations and specific confident relationships among them. A combination of Anets and Arules represents condensed information on associations among the collected data, helping to discover new knowledge and generate hypothesis. As an example we have applied the approach to analyze bacterial metadata from the Genomes OnLine Database. The analysis allowed us to produce a map of sequenced bacterial and archaeal organisms based on their genomic, metabolic and physiological characteristics with three major clusters of metadata representing bacterial pathogens, environmental isolates, and plant symbionts. A signature profile of clustered annotations of environmental bacteria if compared with pathogens linked the aerobic respiration, the high GC content and the large genome size to diversity of metabolic activities and physiological features of the organisms.

  15. Systems Biology in the Context of Big Data and Networks

    PubMed Central

    Altaf-Ul-Amin, Md.; Afendi, Farit Mochamad; Kiboi, Samuel Kuria; Kanaya, Shigehiko

    2014-01-01

    Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology. PMID:24982882

  16. Systems biology in the context of big data and networks.

    PubMed

    Altaf-Ul-Amin, Md; Afendi, Farit Mochamad; Kiboi, Samuel Kuria; Kanaya, Shigehiko

    2014-01-01

    Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.

  17. Discovery of biological networks from diverse functional genomic data

    PubMed Central

    Myers, Chad L; Robson, Drew; Wible, Adam; Hibbs, Matthew A; Chiriac, Camelia; Theesfeld, Chandra L; Dolinski, Kara; Troyanskaya, Olga G

    2005-01-01

    We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web. PMID:16420673

  18. Topological properties of robust biological and computational networks

    PubMed Central

    Navlakha, Saket; He, Xin; Faloutsos, Christos; Bar-Joseph, Ziv

    2014-01-01

    Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields. PMID:24789562

  19. Mining biological networks from full-text articles.

    PubMed

    Czarnecki, Jan; Shepherd, Adrian J

    2014-01-01

    The study of biological networks is playing an increasingly important role in the life sciences. Many different kinds of biological system can be modelled as networks; perhaps the most important examples are protein-protein interaction (PPI) networks, metabolic pathways, gene regulatory networks, and signalling networks. Although much useful information is easily accessible in publicly databases, a lot of extra relevant data lies scattered in numerous published papers. Hence there is a pressing need for automated text-mining methods capable of extracting such information from full-text articles. Here we present practical guidelines for constructing a text-mining pipeline from existing code and software components capable of extracting PPI networks from full-text articles. This approach can be adapted to tackle other types of biological network.

  20. Topological properties of robust biological and computational networks.

    PubMed

    Navlakha, Saket; He, Xin; Faloutsos, Christos; Bar-Joseph, Ziv

    2014-07-06

    Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields.

  1. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

    PubMed Central

    Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K.; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C.; Hoeng, Julia

    2015-01-01

    With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com PMID:25887162

  2. On the Interplay between the Evolvability and Network Robustness in an Evolutionary Biological Network: A Systems Biology Approach

    PubMed Central

    Chen, Bor-Sen; Lin, Ying-Po

    2011-01-01

    In the evolutionary process, the random transmission and mutation of genes provide biological diversities for natural selection. In order to preserve functional phenotypes between generations, gene networks need to evolve robustly under the influence of random perturbations. Therefore, the robustness of the phenotype, in the evolutionary process, exerts a selection force on gene networks to keep network functions. However, gene networks need to adjust, by variations in genetic content, to generate phenotypes for new challenges in the network’s evolution, ie, the evolvability. Hence, there should be some interplay between the evolvability and network robustness in evolutionary gene networks. In this study, the interplay between the evolvability and network robustness of a gene network and a biochemical network is discussed from a nonlinear stochastic system point of view. It was found that if the genetic robustness plus environmental robustness is less than the network robustness, the phenotype of the biological network is robust in evolution. The tradeoff between the genetic robustness and environmental robustness in evolution is discussed from the stochastic stability robustness and sensitivity of the nonlinear stochastic biological network, which may be relevant to the statistical tradeoff between bias and variance, the so-called bias/variance dilemma. Further, the tradeoff could be considered as an antagonistic pleiotropic action of a gene network and discussed from the systems biology perspective. PMID:22084563

  3. Duplication: a Mechanism Producing Disassortative Mixing Networks in Biology

    NASA Astrophysics Data System (ADS)

    Zhao, Dan; Liu, Zeng-Rong; Wang, Jia-Zeng

    2007-10-01

    Assortative/disassortative mixing is an important topological property of a network. A network is called assortative mixing if the nodes in the network tend to connect to their connectivity peers, or disassortative mixing if nodes with low degrees are more likely to connect with high-degree nodes. We have known that biological networks such as protein-protein interaction networks (PPI), gene regulatory networks, and metabolic networks tend to be disassortative. On the other hand, in biological evolution, duplication and divergence are two fundamental processes. In order to make the relationship between the property of disassortative mixing and the two basic biological principles clear and to study the cause of the disassortative mixing property in biological networks, we present a random duplication model and an anti-preference duplication model. Our results show that disassortative mixing networks can be obtained by both kinds of models from uncorrelated initial networks. Moreover, with the growth of the network size, the disassortative mixing property becomes more obvious.

  4. Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach

    PubMed Central

    Wang, Pei; Lü, Jinhu; Yu, Xinghuo

    2014-01-01

    Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine. PMID:25170616

  5. Identification of important nodes in directed biological networks: a network motif approach.

    PubMed

    Wang, Pei; Lü, Jinhu; Yu, Xinghuo

    2014-01-01

    Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

  6. A unified biological modeling and simulation system for analyzing biological reaction networks

    NASA Astrophysics Data System (ADS)

    Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo

    2013-12-01

    In order to understand the biological response in a cell, a researcher has to create a biological network and design an experiment to prove it. Although biological knowledge has been accumulated, we still don't have enough biological models to explain complex biological phenomena. If a new biological network is to be created, integrated modeling software supporting various biological models is required. In this research, we design and implement a unified biological modeling and simulation system, called ezBioNet, for analyzing biological reaction networks. ezBioNet designs kinetic and Boolean network models and simulates the biological networks using a server-side simulation system with Object Oriented Parallel Accelerator Library framework. The main advantage of ezBioNet is that a user can create a biological network by using unified modeling canvas of kinetic and Boolean models and perform massive simulations, including Ordinary Differential Equation analyses, sensitivity analyses, parameter estimates and Boolean network analysis. ezBioNet integrates useful biological databases, including the BioModels database, by connecting European Bioinformatics Institute servers through Web services Application Programming Interfaces. In addition, we employ Eclipse Rich Client Platform, which is a powerful modularity framework to allow various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool and a simulation system for understanding the control mechanism by monitoring the change of each component in a biological network. The simulation result can be managed and visualized on ezBioNet, which is available free of charge at http://ezbionet.sourceforge.net or http://ezbionet.cbnu.ac.kr.

  7. Organization principles of biological networks: An explorative study.

    PubMed

    Kohestani, Havva; Giuliani, Alessandro

    2016-03-01

    The definition of general topological principles allowing for graph characterization is an important pre-requisite for investigating structure-function relationships in biological networks. Here we approached the problem by means of an explorative, data-driven strategy, building upon a size-balanced data set made of around 200 distinct biological networks from seven functional classes and simulated networks coming from three mathematical graph models. A clear link between topological structure and biological function did emerge in terms of class membership prediction (average 67% of correct predictions, p<0.0001) with a varying degree of 'peculiarity' across classes going from a very low (25%) recognition efficiency for neural and brain networks to the extremely high (80%) peculiarity of amino acid-amino acid interaction (AAI) networks. We recognized four main dimensions (principal components) as main organization principles of biological networks. These components allowed for an efficient description of network architectures and for the identification of 'not-physiological' (in this case cancer metabolic networks acting as test set) wiring patterns. We highlighted as well the need of developing new theoretical generative models for biological networks overcoming the limitations of present mathematical graph idealizations. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  8. Parameter identifiability-based optimal observation remedy for biological networks.

    PubMed

    Wang, Yulin; Miao, Hongyu

    2017-05-04

    To systematically understand the interactions between numerous biological components, a variety of biological networks on different levels and scales have been constructed and made available in public databases or knowledge repositories. Graphical models such as structural equation models have long been used to describe biological networks for various quantitative analysis tasks, especially key biological parameter estimation. However, limited by resources or technical capacities, partial observation is a common problem in experimental observations of biological networks, and it thus becomes an important problem how to select unobserved nodes for additional measurements such that all unknown model parameters become identifiable. To the best knowledge of our authors, a solution to this problem does not exist until this study. The identifiability-based observation problem for biological networks is mathematically formulated for the first time based on linear recursive structural equation models, and then a dynamic programming strategy is developed to obtain the optimal observation strategies. The efficiency of the dynamic programming algorithm is achieved by avoiding both symbolic computation and matrix operations as used in other studies. We also provided necessary theoretical justifications to the proposed method. Finally, we verified the algorithm using synthetic network structures and illustrated the application of the proposed method in practice using a real biological network related to influenza A virus infection. The proposed approach is the first solution to the structural identifiability-based optimal observation remedy problem. It is applicable to an arbitrary directed acyclic biological network (recursive SEMs) without bidirectional edges, and it is a computerizable method. Observation remedy is an important issue in experiment design for biological networks, and we believe that this study provides a solid basis for dealing with more challenging design

  9. Systems biology of molecular chaperone networks.

    PubMed

    Csermely, Péter; Korcsmáros, Tamás; Kovács, István A; Szalay, Máté S; Soti, Csaba

    2008-01-01

    Molecular chaperones are not only fascinating molecular machines that help the folding, refolding, activation or assembly of other proteins, but also have a number of functions. These functions can be understood only by considering the emergent properties of cellular networks--and that of chaperones as special network constituents. As a notable example for the network-related roles of chaperones they may act as genetic buffers stabilizing the phenotype of various cells and organisms, and may serve as potential regulators of evolvability. Why are chaperones special in the context of cellular networks? Chaperones: (1) have weak links, i.e. low affinity, transient interactions with most of their partners; (2) connect hubs, i.e. act as 'masterminds' of the cell being close to several centre proteins with a lot of neighbours; and (3) are in the overlaps of network modules, which confers upon them a special regulatory role. Importantly, chaperones may uncouple or even quarantine modules of protein-protein interaction networks, signalling networks, genetic regulatory networks and membrane organelle networks during stress, which gives an additional chaperone-mediated protection for the cell at the network-level. Moreover, chaperones are essential to rebuild inter-modular contacts after stress by their low affinity, 'quasi-random' sampling of the potential interaction partners in different cellular modules. This opens the way to the chaperone-regulated modular evolution of cellular networks, and helps us to design novel therapeutic and anti-ageing strategies.

  10. A Biologically Inspired Network Design Model

    PubMed Central

    Zhang, Xiaoge; Adamatzky, Andrew; Chan, Felix T.S.; Deng, Yong; Yang, Hai; Yang, Xin-She; Tsompanas, Michail-Antisthenis I.; Sirakoulis, Georgios Ch.; Mahadevan, Sankaran

    2015-01-01

    A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach. PMID:26041508

  11. SBEToolbox: A Matlab Toolbox for Biological Network Analysis.

    PubMed

    Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J

    2013-01-01

    We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases.

  12. Biological solutions to transport network design.

    PubMed

    Bebber, Daniel P; Hynes, Juliet; Darrah, Peter R; Boddy, Lynne; Fricker, Mark D

    2007-09-22

    Transport networks are vital components of multicellular organisms, distributing nutrients and removing waste products. Animal and plant transport systems are branching trees whose architecture is linked to universal scaling laws in these organisms. In contrast, many fungi form reticulated mycelia via the branching and fusion of thread-like hyphae that continuously adapt to the environment. Fungal networks have evolved to explore and exploit a patchy environment, rather than ramify through a three-dimensional organism. However, there has been no explicit analysis of the network structures formed, their dynamic behaviour nor how either impact on their ecological function. Using the woodland saprotroph Phanerochaete velutina, we show that fungal networks can display both high transport capacity and robustness to damage. These properties are enhanced as the network grows, while the relative cost of building the network decreases. Thus, mycelia achieve the seemingly competing goals of efficient transport and robustness, with decreasing relative investment, by selective reinforcement and recycling of transport pathways. Fungal networks demonstrate that indeterminate, decentralized systems can yield highly adaptive networks. Understanding how these relatively simple organisms have found effective transport networks through a process of natural selection may inform the design of man-made networks.

  13. Controllability and observability of Boolean networks arising from biology.

    PubMed

    Li, Rui; Yang, Meng; Chu, Tianguang

    2015-02-01

    Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

  14. Controllability and observability of Boolean networks arising from biology

    NASA Astrophysics Data System (ADS)

    Li, Rui; Yang, Meng; Chu, Tianguang

    2015-02-01

    Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

  15. Modeling information flow in biological networks.

    PubMed

    Kim, Yoo-Ah; Przytycki, Jozef H; Wuchty, Stefan; Przytycka, Teresa M

    2011-06-01

    Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method.

  16. Exploring community structure in biological networks with random graphs

    PubMed Central

    2014-01-01

    Background Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system’s functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Results Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Conclusion Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems. PMID:24965130

  17. GraphSpace: stimulating interdisciplinary collaborations in network biology.

    PubMed

    Bharadwaj, Aditya; Singh, Divit P; Ritz, Anna; Tegge, Allison N; Poirel, Christopher L; Kraikivski, Pavel; Adames, Neil; Luther, Kurt; Kale, Shiv D; Peccoud, Jean; Tyson, John J; Murali, T M

    2017-10-01

    Networks have become ubiquitous in systems biology. Visualization is a crucial component in their analysis. However, collaborations within research teams in network biology are hampered by software systems that are either specific to a computational algorithm, create visualizations that are not biologically meaningful, or have limited features for sharing networks and visualizations. We present GraphSpace, a web-based platform that fosters team science by allowing collaborating research groups to easily store, interact with, layout and share networks. Anyone can upload and share networks at http://graphspace.org. In addition, the GraphSpace code is available at http://github.com/Murali-group/graphspace if a user wants to run his or her own server. murali@cs.vt.edu. Supplementary data are available at Bioinformatics online.

  18. Controlling biological networks by time-delayed signals.

    PubMed

    Orosz, Gábor; Moehlis, Jeff; Murray, Richard M

    2010-01-28

    This paper describes the use of time-delayed feedback to regulate the behaviour of biological networks. The general ideas on specific transcriptional regulatory and neural networks are demonstrated. It is shown that robust yet tunable controllers can be constructed that provide the biological systems with model-engineered inputs. The results indicate that time delay modulation may serve as an efficient biocompatible control tool.

  19. Identification of the connections in biologically inspired neural networks

    NASA Technical Reports Server (NTRS)

    Demuth, H.; Leung, K.; Beale, M.; Hicklin, J.

    1990-01-01

    We developed an identification method to find the strength of the connections between neurons from their behavior in small biologically-inspired artificial neural networks. That is, given the network external inputs and the temporal firing pattern of the neurons, we can calculate a solution for the strengths of the connections between neurons and the initial neuron activations if a solution exists. The method determines directly if there is a solution to a particular neural network problem. No training of the network is required. It should be noted that this is a first pass at the solution of a difficult problem. The neuron and network models chosen are related to biology but do not contain all of its complexities, some of which we hope to add to the model in future work. A variety of new results have been obtained. First, the method has been tailored to produce connection weight matrix solutions for networks with important features of biological neural (bioneural) networks. Second, a computationally efficient method of finding a robust central solution has been developed. This later method also enables us to find the most consistent solution in the presence of noisy data. Prospects of applying our method to identify bioneural network connections are exciting because such connections are almost impossible to measure in the laboratory. Knowledge of such connections would facilitate an understanding of bioneural networks and would allow the construction of the electronic counterparts of bioneural networks on very large scale integrated (VLSI) circuits.

  20. Structural comparison of biological networks based on dominant vertices.

    PubMed

    Luna, Beatriz; Galán-Vásquez, Edgardo; Ugalde, Edgardo; Martínez-Antonio, Agustino

    2013-07-01

    It is a current practice to organize biological data in a network structure where vertices represent biological components and arrows represent their interactions. A great diversity of graph theoretical notions, such as clustering coefficient, network motifs, centrality, degree distribution, etc., have been developed in order to characterize the structure of these networks. However, none of the existent characterizations allow us to determine global similarity among networks of different sizes. It is the aim of the present paper to introduce a mathematical tool to compare networks not only with regard to their topological structure, but also in their dynamical capabilities. For this reason we aim to propose a pseudo-distance between networks, built around the notions of determination and dominancy, concepts recently introduced in the context of regulatory dynamics on networks. We use our proposed pseudo-distance to compare networks from the following bacteria: E. coli, B. subtilis, P. aeruginosa, M. tuberculosis, S. aureus and C. glutamicum. We also use this pseudo-distance to compare these real bacterial networks with equivalent homogeneous, scale-free and geometric three dimensional random networks. We found that even when bacterial networks are characterized with different levels of detail, have different sizes and represent different aspects of the organisms, the proposed pseudo-distance captures all these characteristics, and indicates how similar they are or not from random networks.

  1. The Structure and Function of Biological Networks

    ERIC Educational Resources Information Center

    Wu, Daniel Duanqing

    2010-01-01

    Biology has been revolutionized in recent years by an explosion in the availability of data. Transforming this new wealth of data into meaningful biological insights and clinical breakthroughs requires a complete overhaul both in the questions being asked and the methodologies used to answer them. A major challenge in organizing and understanding…

  2. The Structure and Function of Biological Networks

    ERIC Educational Resources Information Center

    Wu, Daniel Duanqing

    2010-01-01

    Biology has been revolutionized in recent years by an explosion in the availability of data. Transforming this new wealth of data into meaningful biological insights and clinical breakthroughs requires a complete overhaul both in the questions being asked and the methodologies used to answer them. A major challenge in organizing and understanding…

  3. The effect of network biology on drug toxicology.

    PubMed

    Gautier, Laurent; Taboureau, Olivier; Audouze, Karine

    2013-11-01

    The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity. This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity. There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.

  4. Epigenetics and Why Biological Networks are More Controllable than Expected

    NASA Astrophysics Data System (ADS)

    Motter, Adilson

    2013-03-01

    A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. In this talk, I will show that it is possible to exploit this same principle to control network behavior. This approach takes advantage of the nonlinear dynamics inherent to real networks, and allows bringing the system to a desired target state even when this state is not directly accessible or the linear counterpart is not controllable. Applications show that this framework permits both reprogramming a network to a desired task as well as rescuing networks from the brink of failure, which I will illustrate through various biological problems. I will also briefly review the progress our group has made over the past 5 years on related control of complex networks in non-biological domains.

  5. Stability of biological networks as represented in Random Boolean Nets.

    SciTech Connect

    Slepoy, Alexander; Thompson, Marshall

    2004-09-01

    We explore stability of Random Boolean Networks as a model of biological interaction networks. We introduce surface-to-volume ratio as a measure of stability of the network. Surface is defined as the set of states within a basin of attraction that maps outside the basin by a bit-flip operation. Volume is defined as the total number of states in the basin. We report development of an object-oriented Boolean network analysis code (Attract) to investigate the structure of stable vs. unstable networks. We find two distinct types of stable networks. The first type is the nearly trivial stable network with a few basins of attraction. The second type contains many basins. We conclude that second type stable networks are extremely rare.

  6. Summit of the Research Coordination Networks for Undergraduate Biology Education

    PubMed Central

    Eaton, Carrie Diaz; Allen, Deborah; Anderson, Laurel J.; Bowser, Gillian; Pauley, Mark A.; Williams, Kathy S.; Uno, Gordon E.

    2016-01-01

    The first summit of projects funded by the National Science Foundation’s Research Coordination Networks for Undergraduate Biology Education (RCN-UBE) program was held January 14–16, 2016, in Washington, DC. Sixty-five scientists and science educators from 38 of the 41 Incubator and Full RCN-UBE awards discussed the value and contributions of RCNs to the national biology education reform effort. The summit illustrated the progress of this innovative UBE track, first awarded in 2009. Participants shared experiences regarding network development and growth, identified best practices and challenges faced in network management, and discussed work accomplished. We report here on key aspects of network evaluation, characteristics of successful networks, and how to sustain and broaden participation in networks. Evidence from successful networks indicates that 5 years (the length of a Full RCN-UBE) may be insufficient time to produce a cohesive and effective network. While online communication promotes the activities of a network and disseminates effective practices, face-to-face meetings are critical for establishing ties between network participants. Creation of these National Science Foundation–funded networks may be particularly useful for consortia of faculty working to address problems or exchange novel solutions discovered while introducing active-learning methods and/or course-based research into their curricula.

  7. Untangling statistical and biological models to understand network inference: the need for a genomics network ontology.

    PubMed

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretical perspective. For this reason, we distinguish a statistical perspective from a mathematical modeling perspective and elaborate their differences and implications. Our results indicate the imperative need for a genomic network ontology in order to avoid increasing confusion about the biological interpretation of inferred networks, which can be even enhanced by approaches that integrate multiple data sets, respectively, data types.

  8. NETWORKS, BIOLOGY AND SYSTEMS ENGINEERING: A CASE STUDY IN INFLAMMATION

    PubMed Central

    Foteinou, P.T.; Yang, E.; Androulakis, I. P.

    2009-01-01

    Biological systems can be modeled as networks of interacting components across multiple scales. A central problem in computational systems biology is to identify those critical components and the rules that define their interactions and give rise to the emergent behavior of a host response. In this paper we will discuss two fundamental problems related to the construction of transcription factor networks and the identification of networks of functional modules describing disease progression. We focus on inflammation as a key physiological response of clinical and translational importance. PMID:20161495

  9. Biologically inspired neural network controller for an infrared tracking system

    NASA Astrophysics Data System (ADS)

    Frigo, Janette R.; Tilden, Mark W.

    1999-01-01

    Many biological system exhibit capable, adaptive behavior with a minimal nervous system such as those found in lower invertebrates. Scientists and engineers are studying biological system because these models may have real-world applications. the analog neural controller, herein, is loosely modeled after minimal biological nervous systems. The system consists of the controller and pair of sensor mounted on an actuator. It is implemented with an electrical oscillator network, two IR sensor and a dc motor, used as an actuator for the system. The system tracks an IR target source. The pointing accuracy of this neural network controller is estimated through experimental measurements and a numerical model of the system.

  10. Two classes of bipartite networks: nested biological and social systems.

    PubMed

    Burgos, Enrique; Ceva, Horacio; Hernández, Laura; Perazzo, R P J; Devoto, Mariano; Medan, Diego

    2008-10-01

    Bipartite graphs have received some attention in the study of social networks and of biological mutualistic systems. A generalization of a previous model is presented, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network. As a result, social and biological graphs are classified as belonging to two clearly different classes. Projected graphs, linking the agents of only one guild, are obtained from the original bipartite graph. The corresponding evolution of its statistical properties is also studied. An example of a biological mutualistic network is analyzed in detail, and it is found that the model provides a very good fitting of all the main statistical features. The model also provides a proper qualitative description of the same features observed in social webs, suggesting the possible reasons underlying the difference in the organization of these two kinds of bipartite networks.

  11. Human diseases through the lens of network biology.

    PubMed

    Furlong, Laura I

    2013-03-01

    One of the challenges raised by next generation sequencing (NGS) is the identification of clinically relevant mutations among all the genetic variation found in an individual. Network biology has emerged as an integrative and systems-level approach for the interpretation of genome data in the context of health and disease. Network biology can provide insightful models for genetic phenomena such as penetrance, epistasis, and modes of inheritance, all of which are integral aspects of Mendelian and complex diseases. Moreover, it can shed light on disease mechanisms via the identification of modules perturbed in those diseases. Current challenges include understanding disease as a result of the interplay between environmental and genetic perturbations and assessing the impact of personal sequence variations in the context of networks. Full realization of the potential of personal genomics will benefit from network biology approaches that aim to uncover the mechanisms underlying disease pathogenesis, identify new biomarkers, and guide personalized therapeutic interventions.

  12. Using directed information to build biologically relevant influence networks.

    PubMed

    Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

    2008-06-01

    The systematic inference of biologically relevant influence networks remains a challenging problem in computational biology. Even though the availability of high-throughput data has enabled the use of probabilistic models to infer the plausible structure of such networks, their true interpretation of the biology of the process is questionable. In this work, we propose a network inference methodology, based on the directed information (DTI) criterion, that incorporates the biology of transcription within the framework so as to enable experimentally verifiable inference. We use publicly available embryonic kidney and T-cell microarray datasets to demonstrate our results. We present two variants of network inference via DTI--supervised and unsupervised--and the inferred networks relevant to mammalian nephrogenesis and T-cell activation. Conformity of the obtained interactions with the literature as well as comparison with the coefficient of determination (CoD) method are demonstrated. Apart from network inference, the proposed framework enables the exploration of specific interactions, not just those revealed by data. To illustrate the latter point, a DTI-based framework to resolve interactions between transcription factor modules and target coregulated genes is proposed. Additionally, we show that DTI can be used in conjunction with mutual information to infer higher-order influence networks involving cooperative gene interactions.

  13. Interactive, multiscale navigation of large and complicated biological networks.

    PubMed

    Praneenararat, Thanet; Takagi, Toshihisa; Iwasaki, Wataru

    2011-04-15

    Many types of omics data are compiled as lists of connections between elements and visualized as networks or graphs where the nodes and edges correspond to the elements and the connections, respectively. However, these networks often appear as 'hair-balls'-with a large number of extremely tangled edges-and cannot be visually interpreted. We present an interactive, multiscale navigation method for biological networks. Our approach can automatically and rapidly abstract any portion of a large network of interest to an immediately interpretable extent. The method is based on an ultrafast graph clustering technique that abstracts networks of about 100 000 nodes in a second by iteratively grouping densely connected portions and a biological-property-based clustering technique that takes advantage of biological information often provided for biological entities (e.g. Gene Ontology terms). It was confirmed to be effective by applying it to real yeast protein network data, and would greatly help modern biologists faced with large, complicated networks in a similar manner to how Web mapping services enable interactive multiscale navigation of geographical maps (e.g. Google Maps). Java implementation of our method, named NaviCluster, is available at http://navicluster.cb.k.u-tokyo.ac.jp/. thanet@cb.k.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online.

  14. Minimum network constraint on reverse engineering to develop biological regulatory networks.

    PubMed

    Shao, Bin; Wu, Jiayi; Tian, Binghui; Ouyang, Qi

    2015-09-07

    Reconstructing the topological structure of biological regulatory networks from microarray expression data or data of protein expression profiles is one of major tasks in systems biology. In recent years, various mathematical methods have been developed to meet this task. Here, based on our previously reported reverse engineering method, we propose a new constraint, i.e., the minimum network constraint, to facilitate the reconstruction of biological networks. Three well studied regulatory networks (the budding yeast cell cycle network, the fission yeast cell cycle network, and the SOS network of Escherichia coli) were used as the test sets to verify the performance of this method. Numerical results show that the biological networks prefer to use the minimal networks to fulfill their functional tasks, making it possible to apply minimal network criteria in the network reconstruction process. Two scenarios were considered in the reconstruction process: generating data using different initial conditions; and generating data from knock out and over-expression experiments. In both cases, network structures are revealed faithfully in a few steps using our approach.

  15. bioDBnet - Biological Database Network

    Cancer.gov

    bioDBnet is a comprehensive resource of most of the biological databases available from different sites like NCBI, Uniprot, EMBL, Ensembl, Affymetrix. It provides a queryable interface to all the databases available, converts identifiers from one database into another and generates comprehensive reports.

  16. Biological Network Inference and Analysis using SEBINI and CABIN

    SciTech Connect

    Taylor, Ronald C.; Singhal, Mudita

    2008-01-01

    Attaining a detailed understanding of the various biological networks in an organism lies at the core of the emerging discipline of systems biology. A precise description of the relationships formed between genes, mRNA molecules, and proteins is a necessary step toward a complete description of the dynamic behavior of an organism at the cellular level; and towards intelligent, efficient and directed modification of an organism. The importance of understanding such regulatory, signaling, and interaction networks has fueled the development of numerous in silico inference algorithms, as well as new experimental techniques and a growing collection of public databases. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, evaluation, and improvement of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to analyze high-throughput gene expression, protein expression, or protein activation data via a suite of state-of-the-art network inference algorithms. It also allows algorithm developers to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. SEBINI can therefore be used by software developers wishing to evaluate, refine, or combine inference techniques, as well as by bioinformaticians analyzing experimental data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, which is exploratory data analysis software that enables integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. The collection of edges in public databases, along with the confidence held in each edge (if available), can be fed into CABIN as one “evidence network”, using the Cytoscape SIF file format. Using CABIN, one may

  17. Analysis and logical modeling of biological signaling transduction networks

    NASA Astrophysics Data System (ADS)

    Sun, Zhongyao

    The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together. In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.

  18. Gene duplication and the properties of biological networks.

    PubMed

    Hughes, Austin L; Friedman, Robert

    2005-12-01

    Patterns of network connection of members of multigene families were examined for two biological networks: a genetic network from the yeast Saccharomyces cerevisiae and a protein-protein interaction network from Caenorhabditis elegans. In both networks, genes belonging to gene families represented by a single member in the genome ("singletons") were disproportionately represented among the nodes having large numbers of connections. Of 68 single-member yeast families with 25 or more network connections, 28 (44.4%) were located in duplicated genomic segments believed to have originated from an ancient polyploidization event; thus, each of these 28 loci was thus presumably duplicated along with the genomic segment to which it belongs, but one of the two duplicates has subsequently been deleted. Nodes connected to major "hubs" with a large number of connections, tended to be relatively sparsely interconnected among themselves. Furthermore, duplicated genes, even those arising from recent duplication, rarely shared many network connections, suggesting that network connections are remarkably labile over evolutionary time. These factors serve to explain well-known general properties of biological networks, including their scale-free and modular nature.

  19. Applying differential dynamic logic to reconfigurable biological networks.

    PubMed

    Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena

    2017-09-01

    Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Biological impacts and context of network theory

    SciTech Connect

    Almaas, E

    2007-01-05

    Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World-Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory-, signal transduction-, protein interaction- and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.

  1. Course 10: Three Lectures on Biological Networks

    NASA Astrophysics Data System (ADS)

    Magnasco, M. O.

    1 Enzymatic networks. Proofreading knots: How DNA topoisomerases disentangle DNA 1.1 Length scales and energy scales 1.2 DNA topology 1.3 Topoisomerases 1.4 Knots and supercoils 1.5 Topological equilibrium 1.6 Can topoisomerases recognize topology? 1.7 Proposal: Kinetic proofreading 1.8 How to do it twice 1.9 The care and proofreading of knots 1.10 Suppression of supercoils 1.11 Problems and outlook 1.12 Disquisition 2 Gene expression networks. Methods for analysis of DNA chip experiments 2.1 The regulation of gene expression 2.2 Gene expression arrays 2.3 Analysis of array data 2.4 Some simplifying assumptions 2.5 Probeset analysis 2.6 Discussion 3 Neural and gene expression networks: Song-induced gene expression in the canary brain 3.1 The study of songbirds 3.2 Canary song 3.3 ZENK 3.4 The blush 3.5 Histological analysis 3.6 Natural vs. artificial 3.7 The Blush II: gAP 3.8 Meditation

  2. Classifying pairs with trees for supervised biological network inference.

    PubMed

    Schrynemackers, Marie; Wehenkel, Louis; Babu, M Madan; Geurts, Pierre

    2015-08-01

    Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as a classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the latter for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.

  3. A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics.

    PubMed

    Somvanshi, Pramod Rajaram; Venkatesh, K V

    2014-03-01

    Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

  4. Non-Hermitian localization in biological networks

    NASA Astrophysics Data System (ADS)

    Amir, Ariel; Hatano, Naomichi; Nelson, David R.

    2016-04-01

    We explore the spectra and localization properties of the N -site banded one-dimensional non-Hermitian random matrices that arise naturally in sparse neural networks. Approximately equal numbers of random excitatory and inhibitory connections lead to spatially localized eigenfunctions and an intricate eigenvalue spectrum in the complex plane that controls the spontaneous activity and induced response. A finite fraction of the eigenvalues condense onto the real or imaginary axes. For large N , the spectrum has remarkable symmetries not only with respect to reflections across the real and imaginary axes but also with respect to 90∘ rotations, with an unusual anisotropic divergence in the localization length near the origin. When chains with periodic boundary conditions become directed, with a systematic directional bias superimposed on the randomness, a hole centered on the origin opens up in the density-of-states in the complex plane. All states are extended on the rim of this hole, while the localized eigenvalues outside the hole are unchanged. The bias-dependent shape of this hole tracks the bias-independent contours of constant localization length. We treat the large-N limit by a combination of direct numerical diagonalization and using transfer matrices, an approach that allows us to exploit an electrostatic analogy connecting the "charges" embodied in the eigenvalue distribution with the contours of constant localization length. We show that similar results are obtained for more realistic neural networks that obey "Dale's law" (each site is purely excitatory or inhibitory) and conclude with perturbation theory results that describe the limit of large directional bias, when all states are extended. Related problems arise in random ecological networks and in chains of artificial cells with randomly coupled gene expression patterns.

  5. SBEToolbox: A Matlab Toolbox for Biological Network Analysis

    PubMed Central

    Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J.

    2013-01-01

    We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases. PMID:24027418

  6. Using biological networks to integrate, visualize and analyze genomics data.

    PubMed

    Charitou, Theodosia; Bryan, Kenneth; Lynn, David J

    2016-03-31

    Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene's network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide 'omics' data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.

  7. BiologicalNetworks 2.0 - an integrative view of genome biology data

    PubMed Central

    2010-01-01

    Background A significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems. Results Here we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities (genes/proteins, promoters, COGs, pathways, binding sites, and other) and their relations (interactions, co-expression, co-citations, and other). The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations. Conclusions The new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at http://www.biologicalnetworks.org. PMID:21190573

  8. A survey of visualization tools for biological network analysis

    PubMed Central

    Pavlopoulos, Georgios A; Wegener, Anna-Lynn; Schneider, Reinhard

    2008-01-01

    The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing. PMID:19040716

  9. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

    PubMed Central

    Parikshak, Neelroop N.; Gandal, Michael J.; Geschwind, Daniel H.

    2015-01-01

    Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology. PMID:26149713

  10. Ising models of strongly coupled biological networks with multivariate interactions

    NASA Astrophysics Data System (ADS)

    Merchan, Lina; Nemenman, Ilya

    2013-03-01

    Biological networks consist of a large number of variables that can be coupled by complex multivariate interactions. However, several neuroscience and cell biology experiments have reported that observed statistics of network states can be approximated surprisingly well by maximum entropy models that constrain correlations only within pairs of variables. We would like to verify if this reduction in complexity results from intricacies of biological organization, or if it is a more general attribute of these networks. We generate random networks with p-spin (p > 2) interactions, with N spins and M interaction terms. The probability distribution of the network states is then calculated and approximated with a maximum entropy model based on constraining pairwise spin correlations. Depending on the M/N ratio and the strength of the interaction terms, we observe a transition where the pairwise approximation is very good to a region where it fails. This resembles the sat-unsat transition in constraint satisfaction problems. We argue that the pairwise model works when the number of highly probable states is small. We argue that many biological systems must operate in a strongly constrained regime, and hence we expect the pairwise approximation to be accurate for a wide class of problems. This research has been partially supported by the James S McDonnell Foundation grant No.220020321.

  11. Theory of interface: category theory, directed networks and evolution of biological networks.

    PubMed

    Haruna, Taichi

    2013-11-01

    Biological networks have two modes. The first mode is static: a network is a passage on which something flows. The second mode is dynamic: a network is a pattern constructed by gluing functions of entities constituting the network. In this paper, first we discuss that these two modes can be associated with the category theoretic duality (adjunction) and derive a natural network structure (a path notion) for each mode by appealing to the category theoretic universality. The path notion corresponding to the static mode is just the usual directed path. The path notion for the dynamic mode is called lateral path which is the alternating path considered on the set of arcs. Their general functionalities in a network are transport and coherence, respectively. Second, we introduce a betweenness centrality of arcs for each mode and see how the two modes are embedded in various real biological network data. We find that there is a trade-off relationship between the two centralities: if the value of one is large then the value of the other is small. This can be seen as a kind of division of labor in a network into transport on the network and coherence of the network. Finally, we propose an optimization model of networks based on a quality function involving intensities of the two modes in order to see how networks with the above trade-off relationship can emerge through evolution. We show that the trade-off relationship can be observed in the evolved networks only when the dynamic mode is dominant in the quality function by numerical simulations. We also show that the evolved networks have features qualitatively similar to real biological networks by standard complex network analysis.

  12. Synthetic biology: exploring and exploiting genetic modularity through the design of novel biological networks.

    PubMed

    Agapakis, Christina M; Silver, Pamela A

    2009-07-01

    Synthetic biology has been used to describe many biological endeavors over the past thirty years--from designing enzymes and in vitro systems, to manipulating existing metabolisms and gene expression, to creating entirely synthetic replicating life forms. What separates the current incarnation of synthetic biology from the recombinant DNA technology or metabolic engineering of the past is an emphasis on principles from engineering such as modularity, standardization, and rigorously predictive models. As such, synthetic biology represents a new paradigm for learning about and using biological molecules and data, with applications in basic science, biotechnology, and medicine. This review covers the canonical examples as well as some recent advances in synthetic biology in terms of what we know and what we can learn about the networks underlying biology, and how this endeavor may shape our understanding of living systems.

  13. Dynamics of Boolean networks controlled by biologically meaningful functions.

    PubMed

    Raeymaekers, L

    2002-10-07

    The remarkably stable dynamics displayed by randomly constructed Boolean networks is one of the most striking examples of the spontaneous emergence of self-organization in model systems composed of many interacting elements (Kauffman, S., J. theor. Biol.22, 437-467, 1969; The Origins of Order, Oxford University Press, Oxford, 1993). The dynamics of such networks is most stable for a connectivity of two inputs per element, and decreases dramatically with increasing number of connections. Whereas the simplicity of this model system allows the tracing of the dynamical trajectories, it leaves out many features of real biological connections. For instance, the dynamics has been studied in detail only for networks constructed by allowing all theoretically possible Boolean rules, whereas only a subset of them make sense in the material world. This paper analyses the effect on the dynamics of using only Boolean functions which are meaningful in a biological sense. This analysis is particularly relevant for nets with more than two inputs per element because biological networks generally appear to be more extensively interconnected. Sets of the meaningful functions were assembled for up to four inputs per element. The use of these rules results in a smaller number of distinct attractors which have a shorter length, with relatively little sensitivity to the size of the network and to the number of inputs per element. Forcing away the activator/inhibitor ratio from the expected value of 50% further enhances the stability. This effect is more pronounced for networks consisting of a majority of activators than for networks with a corresponding majority of inhibitors, indicating that the former allow the evolution of larger genetic networks. The data further support the idea of the usefulness of logical networks as a conceptual framework for the understanding of real-world phenomena.

  14. From biological and social network metaphors to coupled bio-social wireless networks

    PubMed Central

    Barrett, Christopher L.; Eubank, Stephen; Anil Kumar, V.S.; Marathe, Madhav V.

    2010-01-01

    Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

  15. From biological and social network metaphors to coupled bio-social wireless networks.

    PubMed

    Barrett, Christopher L; Channakeshava, Karthik; Eubank, Stephen; Anil Kumar, V S; Marathe, Madhav V

    2011-01-01

    Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other.

  16. Distribution of biological databases over lowbandwidth networks

    PubMed Central

    Azam, S Sikander; Zarina, Shamshad

    2012-01-01

    Databases are integral part of bioinformatics and need to be accessed most frequently, thus downloading and updating them on a regular basis is very critical. The establishment of bioinformatics research facility is a challenge for developing countries as they suffer from inherent low-bandwidth and unreliable internet connections. Therefore, the identification of techniques supporting download and automatic synchronization of large biological database at low bandwidth is of utmost importance. In current study, two protocols (FTP and Bit Torrent) were evaluated and the utility of a BitTorren based peer-to-peer (btP2P) file distribution model for automatic synchronization and distribution of large dataset at our facility in Pakistan have been discussed. PMID:22493528

  17. Network component analysis: reconstruction of regulatory signals in biological systems.

    PubMed

    Liao, James C; Boscolo, Riccardo; Yang, Young-Lyeol; Tran, Linh My; Sabatti, Chiara; Roychowdhury, Vwani P

    2003-12-23

    High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.

  18. [Lipid networks in mast cell biology].

    PubMed

    Taketomi, Yoshitaka; Murakami, Makoto

    2011-01-01

    Tissue-resident mast cells are derived from circulating committed progenitors, which are originated from pluripotential hematopoietic stem cells in bone marrow. These progenitors migrate into extravascular tissues, where they undergo differentiation and maturation into tissue-specific mature phenotypes. When activated by IgE/antigen, stem cell factor, neuropeptides, or other stimuli, mature mast cells release three classes of biologically active products, including pre-formed mediators stored in secretory granules, newly transcribed cytokines and chemokines, and de novo synthesized lipid mediators. Therefore, these cells have been implicated as major effector cells in acute and chronic inflammatory diseases. In recent years, it has become clear that lipid mediators including arachidonic acid metabolites (prostaglandins and leukotrienes) and lysophospholipid-derived products play crucial roles in mast cell-associated pathology. In this article, we will provide an overview of the roles of various lipid mediators in allergic diseases fueled by studies of their biosynthetic enzymes or receptors. In the latter part, we will make a particular focus on phospholipase A(2) enzymes, which are placed at the bottleneck (rate-limiting) step of the lipid mediator-biosynthetic pathways.

  19. Biological network inference using low order partial correlation.

    PubMed

    Zuo, Yiming; Yu, Guoqiang; Tadesse, Mahlet G; Ressom, Habtom W

    2014-10-01

    Biological network inference is a major challenge in systems biology. Traditional correlation-based network analysis results in too many spurious edges since correlation cannot distinguish between direct and indirect associations. To address this issue, Gaussian graphical models (GGM) were proposed and have been widely used. Though they can significantly reduce the number of spurious edges, GGM are insufficient to uncover a network structure faithfully due to the fact that they only consider the full order partial correlation. Moreover, when the number of samples is smaller than the number of variables, further technique based on sparse regularization needs to be incorporated into GGM to solve the singular covariance inversion problem. In this paper, we propose an efficient and mathematically solid algorithm that infers biological networks by computing low order partial correlation (LOPC) up to the second order. The bias introduced by the low order constraint is minimal compared to the more reliable approximation of the network structure achieved. In addition, the algorithm is suitable for a dataset with small sample size but large number of variables. Simulation results show that LOPC yields far less spurious edges and works well under various conditions commonly seen in practice. The application to a real metabolomics dataset further validates the performance of LOPC and suggests its potential power in detecting novel biomarkers for complex disease.

  20. Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions.

    PubMed

    Semenov, Sergey N; Kraft, Lewis J; Ainla, Alar; Zhao, Mengxia; Baghbanzadeh, Mostafa; Campbell, Victoria E; Kang, Kyungtae; Fox, Jerome M; Whitesides, George M

    2016-09-29

    Networks of organic chemical reactions are important in life and probably played a central part in its origin. Network dynamics regulate cell division, circadian rhythms, nerve impulses and chemotaxis, and guide the development of organisms. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics such as spontaneous pattern formation, bistability and periodic oscillations, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate-thioester exchange, thiolate-disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes and DNA) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov-Zhabotinskii-type reactions), the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving

  1. Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions

    NASA Astrophysics Data System (ADS)

    Semenov, Sergey N.; Kraft, Lewis J.; Ainla, Alar; Zhao, Mengxia; Baghbanzadeh, Mostafa; Campbell, Victoria E.; Kang, Kyungtae; Fox, Jerome M.; Whitesides, George M.

    2016-09-01

    Networks of organic chemical reactions are important in life and probably played a central part in its origin. Network dynamics regulate cell division, circadian rhythms, nerve impulses and chemotaxis, and guide the development of organisms. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics such as spontaneous pattern formation, bistability and periodic oscillations, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate-thioester exchange, thiolate-disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes and DNA) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov-Zhabotinskii-type reactions), the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving

  2. Integrative Network Biology: Graph Prototyping for Co-Expression Cancer Networks

    PubMed Central

    Kugler, Karl G.; Mueller, Laurin A. J.; Graber, Armin; Dehmer, Matthias

    2011-01-01

    Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure. PMID:21829532

  3. Composite nanowire networks for biological sensor platforms

    NASA Astrophysics Data System (ADS)

    Jabal, Jamie Marie Francisco

    The main goal of this research is to design, fabricate, and test a nanomaterial-based platform adequate for the measurement of physiological changes in living cells. The two primary objectives toward this end are (1) the synthesis and selection of a suitable nanomaterial and (2) the demonstration of cellular response to a direct stimulus. Determining a useful nanomaterial morphology and behavior within a sensor configuration presented challenges based on cellular integration and access to electrochemical characterization. The prospect for feasible optimization and eventual scale-up in technology were also significant. Constraining criteria are that the nanomaterial detector must (a) be cheap and relatively easy to fabricate controllably, (b) encourage cell attachment, (c) exhibit consistent wettability over time, and (d) facilitate electrochemical processes. The ultimate goal would be to transfer a proof-of-principle and proof-of-design for a whole-cell sensor technology that is cost effective and has a potential for hand-held packaging. Initial tasks were to determine an effective and highly-functional nanomaterial for biosensors by assessing wettability, morphology and conductivity behavior of several candidate materials: gallium nitride nanowires, silicon dioxide nanosprings and nanowires, and titania nanofibers. Electrospinning poly(vinyl pyrrolidone)-coated titania nano- and microfibers (O20 nm--2 microm) into a pseudo-random network is controllable to a uniformity of 1--2° in contact angle. The final electrode can be prepared with a precise wettability ranging from partial wetting to ultrahydrophobic (170°) on a variety of substrates: glass, indium tin oxide, silicon, and aluminum. Fiber mats exhibit excellent mechanical stability against rinsing, and support the incubation of epithelial (skin) and pancreatic cells. Impedance spectroscopy on the whole-cell sensor shows resistive changes attributed to cell growth as well as complex frequency

  4. Dissecting the Molecular Mechanisms of Neurodegenerative Diseases through Network Biology.

    PubMed

    Santiago, Jose A; Bottero, Virginie; Potashkin, Judith A

    2017-01-01

    Neurodegenerative diseases are rarely caused by a mutation in a single gene but rather influenced by a combination of genetic, epigenetic and environmental factors. Emerging high-throughput technologies such as RNA sequencing have been instrumental in deciphering the molecular landscape of neurodegenerative diseases, however, the interpretation of such large amounts of data remains a challenge. Network biology has become a powerful platform to integrate multiple omics data to comprehensively explore the molecular networks in the context of health and disease. In this review article, we highlight recent advances in network biology approaches with an emphasis in brain-networks that have provided insights into the molecular mechanisms leading to the most prevalent neurodegenerative diseases including Alzheimer's (AD), Parkinson's (PD) and Huntington's diseases (HD). We discuss how integrative approaches using multi-omics data from different tissues have been valuable for identifying biomarkers and therapeutic targets. In addition, we discuss the challenges the field of network medicine faces toward the translation of network-based findings into clinically actionable tools for personalized medicine applications.

  5. Analysis of biological networks and biological pathways associated with residual feed intake in beef cattle.

    PubMed

    Karisa, Brian; Moore, Stephen; Plastow, Graham

    2014-04-01

    In this study, biological networks were reconstructed from genes and metabolites significantly associated with residual feed intake (RFI) in beef cattle. The networks were then used to identify biological pathways associated with RFI. RFI is a measure of feed efficiency, which is independent of body size and growth; therefore selection for RFI is expected to result in cattle that consume less feed without adverse effects on growth rate and mature size. Although several studies have identified genes associated with RFI, the mechanisms of the biological processes are not well understood. In this study, we utilised the results obtained from two association studies, one using 24 genes and one using plasma metabolites to reconstruct biological networks associated with RFI using IPA software (Igenuity Systems). The results pointed to biological processes such as lipid and steroid biosynthesis, protein and carbohydrate metabolism and regulation of gene expression through DNA transcription, protein stability and degradation. The major canonical pathways included signaling of growth hormone, Oncostatin M, insulin-like growth factor and AMP activated protein kinase, and cholesterol biosynthesis. This study provides information on potential biological mechanisms, and genes and metabolites involved in feed efficiency in beef cattle. © 2013 Japanese Society of Animal Science.

  6. Harnessing systems biology approaches to engineer functional microvascular networks.

    PubMed

    Sefcik, Lauren S; Wilson, Jennifer L; Papin, Jason A; Botchwey, Edward A

    2010-06-01

    Microvascular remodeling is a complex process that includes many cell types and molecular signals. Despite a continued growth in the understanding of signaling pathways involved in the formation and maturation of new blood vessels, approximately half of all compounds entering clinical trials will fail, resulting in the loss of much time, money, and resources. Most pro-angiogenic clinical trials to date have focused on increasing neovascularization via the delivery of a single growth factor or gene. Alternatively, a focus on the concerted regulation of whole networks of genes may lead to greater insight into the underlying physiology since the coordinated response is greater than the sum of its parts. Systems biology offers a comprehensive network view of the processes of angiogenesis and arteriogenesis that might enable the prediction of drug targets and whether or not activation of the targets elicits the desired outcome. Systems biology integrates complex biological data from a variety of experimental sources (-omics) and analyzes how the interactions of the system components can give rise to the function and behavior of that system. This review focuses on how systems biology approaches have been applied to microvascular growth and remodeling, and how network analysis tools can be utilized to aid novel pro-angiogenic drug discovery.

  7. Reduction of dynamical biochemical reactions networks in computational biology

    PubMed Central

    Radulescu, O.; Gorban, A. N.; Zinovyev, A.; Noel, V.

    2012-01-01

    Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler models, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state (QSS) and quasi-equilibrium approximations (QE), and provide practical recipes for model reduction of linear and non-linear networks. We also discuss the application of model reduction to the problem of parameter identification, via backward pruning machine learning techniques. PMID:22833754

  8. Passing Messages between Biological Networks to Refine Predicted Interactions

    PubMed Central

    Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

    2013-01-01

    Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net. PMID:23741402

  9. Passing messages between biological networks to refine predicted interactions.

    PubMed

    Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

    2013-01-01

    Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

  10. Integrated network analysis and effective tools in plant systems biology

    PubMed Central

    Fukushima, Atsushi; Kanaya, Shigehiko; Nishida, Kozo

    2014-01-01

    One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms. PMID:25408696

  11. Classification of biological and non-biological fluvial particles using image processing and artificial neural network

    NASA Astrophysics Data System (ADS)

    Shrestha, Bim Prasad; Shrestha, Nabin Kumar; Poudel, Laxman

    2009-04-01

    Particles flowing along with water largely affect safe drinking water, irrigation, aquatic life preservation and hydropower generation. This research describes activities that lead to development of fluvial particle characterization that includes detection of biological and non-biological particles and shape characterization using Image Processing and Artificial Neural Network (ANN). Fluvial particles are characterized based on multi spectral images processing using ANN. Images of wavelength of 630nm and 670nm are taken as most distinctive characterizing properties of biological and non-biological particles found in Bagmati River of Nepal. The samples were collected at pre-monsoon, monsoon and post-monsoon seasons. Random samples were selected and multi spectral images are processed using MATLAB 6.5. Thirty matrices were built from each sample. The obtained data of 42 rows and 60columns were taken as input training with an output matrix of 42 rows and 2 columns. Neural Network of Perceptron model was created using a transfer function. The system was first validated and later on tested at 18 different strategic locations of Bagmati River of Kathmandu Valley, Nepal. This network classified biological and non biological particles. Development of new non-destructive technique to characterize biological and non-biological particles from fluvial sample in a real time has a significance breakthrough. This applied research method and outcome is an attractive model for real time monitoring of particles and has many applications that can throw a significant outlet to many researches and for effective utilization of water resources. It opened a new horizon of opportunities for basic and applied research at Kathmandu University in Nepal.

  12. Binary threshold networks as a natural null model for biological networks

    NASA Astrophysics Data System (ADS)

    Rybarsch, Matthias; Bornholdt, Stefan

    2012-08-01

    Spin models of neural networks and genetic networks are considered elegant as they are accessible to statistical mechanics tools for spin glasses and magnetic systems. However, the conventional choice of variables in spin systems may cause problems in some models when parameter choices are unrealistic from a biological perspective. Obviously, this may limit the role of a model as a template model for biological systems. Perhaps less obviously, also ensembles of random networks are affected and may exhibit different critical properties. We consider here a prototypical network model that is biologically plausible in its local mechanisms. We study a discrete dynamical network with two characteristic properties: Nodes with binary states 0 and 1, and a modified threshold function with Θ0(0)=0. We explore the critical properties of random networks of such nodes and find a critical connectivity Kc=2.0 with activity vanishing at the critical point. Finally, we observe that the present model allows a more natural implementation of recent models of budding yeast and fission yeast cell-cycle control networks.

  13. Biological Investigations of Adaptive Networks: Neuronal Control of Conditioned Responses

    DTIC Science & Technology

    1989-07-01

    NO Boiling AFB, DC 203-4861102F 2312 Al I TI TLE (include Secunty Clamtfiation) Biological Investigations of Adaptive Networks: Neuronal Control of...based on mathematical models and computer simulation. Recordings were done from single brain stem neurons in awake, behaving animals for the purpose...single-unit recordings from awake behaving animals were developed. The relationship between single neurons ’ dynamic behavior and the CR in terms of

  14. NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities

    PubMed Central

    da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D.; Correia, Cristina; Li, Hu

    2016-01-01

    The sequential chain of interactions altering the binary state of a biomolecule represents the ‘information flow’ within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein–protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes—network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. PMID:26975659

  15. NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

    PubMed

    da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

    2016-06-02

    The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  16. Integration of biological networks and pathways with genetic association studies.

    PubMed

    Sun, Yan V

    2012-10-01

    Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.

  17. Impact of heuristics in clustering large biological networks.

    PubMed

    Shafin, Md Kishwar; Kabir, Kazi Lutful; Ridwan, Iffatur; Anannya, Tasmiah Tamzid; Karim, Rashid Saadman; Hoque, Mohammad Mozammel; Rahman, M Sohel

    2015-12-01

    Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising.

  18. Protozoan HSP90-heterocomplex: molecular interaction network and biological significance.

    PubMed

    Figueras, Maria J; Echeverria, Pablo C; Angel, Sergio O

    2014-05-01

    The HSP90 chaperone is a highly conserved protein from bacteria to higher eukaryotes. In eukaryotes, this chaperone participates in different large complexes, such as the HSP90 heterocomplex, which has important biological roles in cell homeostasis and differentiation. The HSP90-heterocomplex is also named the HSP90/HSP70 cycle because different co-chaperones (HIP, HSP40, HOP, p23, AHA1, immunophilins, PP5) participate in this complex by assembling sequentially, from the early to the mature complex. In this review, we analyze the conservation and relevance of HSP90 and the HSP90-heterocomplex in several protozoan parasites, with emphasis in Plasmodium spp., Toxoplasma spp., Leishmania spp. and Trypanosoma spp. In the last years, there has been an outburst of studies based on yeast two-hybrid methodology, co-immunoprecipitation-mass spectrometry and bioinformatics, which have generated a most comprehensive protein-protein interaction (PPI) network of HSP90 and its co-chaperones. This review analyzes the existing PPI networks of HSP90 and its co-chaperones of some protozoan parasites and discusses the usefulness of these powerful tools to analyze the biological role of the HSP90-heterocomplex in these parasites. The generation of a T. gondii HSP90 heterocomplex PPI network based on experimental data and a recent Plasmodium HSP90 heterocomplex PPI network are also included and discussed. As an example, the putative implication of nuclear transport and chromatin (histones and Sir2) as HSP90-heterocomplex interactors is here discussed.

  19. Perturbation biology: inferring signaling networks in cellular systems.

    PubMed

    Molinelli, Evan J; Korkut, Anil; Wang, Weiqing; Miller, Martin L; Gauthier, Nicholas P; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B; Pratilas, Christine A; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

  20. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    PubMed Central

    Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

  1. Systems analysis of biological networks in skeletal muscle function.

    PubMed

    Smith, Lucas R; Meyer, Gretchen; Lieber, Richard L

    2013-01-01

    Skeletal muscle function depends on the efficient coordination among subcellular systems. These systems are composed of proteins encoded by a subset of genes, all of which are tightly regulated. In the cases where regulation is altered because of disease or injury, dysfunction occurs. To enable objective analysis of muscle gene expression profiles, we have defined nine biological networks whose coordination is critical to muscle function. We begin by describing the expression of proteins necessary for optimal neuromuscular junction function that results in the muscle cell action potential. That action potential is transmitted to proteins involved in excitation-contraction coupling enabling Ca(2+) release. Ca(2+) then activates contractile proteins supporting actin and myosin cross-bridge cycling. Force generated by cross-bridges is transmitted via cytoskeletal proteins through the sarcolemma and out to critical proteins that support the muscle extracellular matrix. Muscle contraction is fueled through many proteins that regulate energy metabolism. Inflammation is a common response to injury that can result in alteration of many pathways within muscle. Muscle also has multiple pathways that regulate size through atrophy or hypertrophy. Finally, the isoforms associated with fast muscle fibers and their corresponding isoforms in slow muscle fibers are delineated. These nine networks represent important biological systems that affect skeletal muscle function. Combining high-throughput systems analysis with advanced networking software will allow researchers to use these networks to objectively study skeletal muscle systems. Copyright © 2012 Wiley Periodicals, Inc.

  2. Minimum dominating set-based methods for analyzing biological networks.

    PubMed

    Nacher, Jose C; Akutsu, Tatsuya

    2016-06-01

    The fast increase of 'multi-omics' data does not only pose a computational challenge for its analysis but also requires novel algorithmic methodologies to identify complex biological patterns and decipher the ultimate roots of human disorders. To that end, the massive integration of omics data with disease phenotypes is offering a new window into the cell functionality. The minimum dominating set (MDS) approach has rapidly emerged as a promising algorithmic method to analyze complex biological networks integrated with human disorders, which can be composed of a variety of omics data, from proteomics and transcriptomics to metabolomics. Here we review the main theoretical foundations of the methodology and the key algorithms, and examine the recent applications in which biological systems are analyzed by using the MDS approach. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    NASA Technical Reports Server (NTRS)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  4. The Default Mode Network Differentiates Biological From Non-Biological Motion

    PubMed Central

    Dayan, Eran; Sella, Irit; Mukovskiy, Albert; Douek, Yehonatan; Giese, Martin A.; Malach, Rafael; Flash, Tamar

    2016-01-01

    The default mode network (DMN) has been implicated in an array of social-cognitive functions, including self-referential processing, theory of mind, and mentalizing. Yet, the properties of the external stimuli that elicit DMN activity in relation to these domains remain unknown. Previous studies suggested that motion kinematics is utilized by the brain for social-cognitive processing. Here, we used functional MRI to examine whether the DMN is sensitive to parametric manipulations of observed motion kinematics. Preferential responses within core DMN structures differentiating non-biological from biological kinematics were observed for the motion of a realistically looking, human-like avatar, but not for an abstract object devoid of human form. Differences in connectivity patterns during the observation of biological versus non-biological kinematics were additionally observed. Finally, the results additionally suggest that the DMN is coupled more strongly with key nodes in the action observation network, namely the STS and the SMA, when the observed motion depicts human rather than abstract form. These findings are the first to implicate the DMN in the perception of biological motion. They may reflect the type of information used by the DMN in social-cognitive processing. PMID:25217472

  5. PREFACE: Complex Networks: from Biology to Information Technology

    NASA Astrophysics Data System (ADS)

    Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.

    2008-06-01

    The field of complex networks is one of the most active areas in contemporary statistical physics. Ten years after seminal work initiated the modern study of networks, interest in the field is in fact still growing, as indicated by the ever increasing number of publications in network science. The reason for such a resounding success is most likely the simplicity and broad significance of the approach that, through graph theory, allows researchers to address a variety of different complex systems within a common framework. This special issue comprises a selection of contributions presented at the workshop 'Complex Networks: from Biology to Information Technology' held in July 2007 in Pula (Cagliari), Italy as a satellite of the general conference STATPHYS23. The contributions cover a wide range of problems that are currently among the most important questions in the area of complex networks and that are likely to stimulate future research. The issue is organised into four sections. The first two sections describe 'methods' to study the structure and the dynamics of complex networks, respectively. After this methodological part, the issue proceeds with a section on applications to biological systems. The issue closes with a section concentrating on applications to the study of social and technological networks. The first section, entitled Methods: The Structure, consists of six contributions focused on the characterisation and analysis of structural properties of complex networks: The paper Motif-based communities in complex networks by Arenas et al is a study of the occurrence of characteristic small subgraphs in complex networks. These subgraphs, known as motifs, are used to define general classes of nodes and their communities by extending the mathematical expression of the Newman-Girvan modularity. The same line of research, aimed at characterising network structure through the analysis of particular subgraphs, is explored by Bianconi and Gulbahce in Algorithm

  6. Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level

    NASA Astrophysics Data System (ADS)

    Thakar, Juilee; Albert, Réka

    The following sections are included: * Introduction * Boolean Network Concepts and History * Extensions of the Classical Boolean Framework * Boolean Inference Methods and Examples in Biology * Dynamic Boolean Models: Examples in Plant Biology, Developmental Biology and Immunology * Conclusions * References

  7. Biological Instability in a Chlorinated Drinking Water Distribution Network

    PubMed Central

    Nescerecka, Alina; Rubulis, Janis; Vital, Marius; Juhna, Talis; Hammes, Frederik

    2014-01-01

    The purpose of a drinking water distribution system is to deliver drinking water to the consumer, preferably with the same quality as when it left the treatment plant. In this context, the maintenance of good microbiological quality is often referred to as biological stability, and the addition of sufficient chlorine residuals is regarded as one way to achieve this. The full-scale drinking water distribution system of Riga (Latvia) was investigated with respect to biological stability in chlorinated drinking water. Flow cytometric (FCM) intact cell concentrations, intracellular adenosine tri-phosphate (ATP), heterotrophic plate counts and residual chlorine measurements were performed to evaluate the drinking water quality and stability at 49 sampling points throughout the distribution network. Cell viability methods were compared and the importance of extracellular ATP measurements was examined as well. FCM intact cell concentrations varied from 5×103 cells mL−1 to 4.66×105 cells mL−1 in the network. While this parameter did not exceed 2.1×104 cells mL−1 in the effluent from any water treatment plant, 50% of all the network samples contained more than 1.06×105 cells mL−1. This indisputably demonstrates biological instability in this particular drinking water distribution system, which was ascribed to a loss of disinfectant residuals and concomitant bacterial growth. The study highlights the potential of using cultivation-independent methods for the assessment of chlorinated water samples. In addition, it underlines the complexity of full-scale drinking water distribution systems, and the resulting challenges to establish the causes of biological instability. PMID:24796923

  8. Biological instability in a chlorinated drinking water distribution network.

    PubMed

    Nescerecka, Alina; Rubulis, Janis; Vital, Marius; Juhna, Talis; Hammes, Frederik

    2014-01-01

    The purpose of a drinking water distribution system is to deliver drinking water to the consumer, preferably with the same quality as when it left the treatment plant. In this context, the maintenance of good microbiological quality is often referred to as biological stability, and the addition of sufficient chlorine residuals is regarded as one way to achieve this. The full-scale drinking water distribution system of Riga (Latvia) was investigated with respect to biological stability in chlorinated drinking water. Flow cytometric (FCM) intact cell concentrations, intracellular adenosine tri-phosphate (ATP), heterotrophic plate counts and residual chlorine measurements were performed to evaluate the drinking water quality and stability at 49 sampling points throughout the distribution network. Cell viability methods were compared and the importance of extracellular ATP measurements was examined as well. FCM intact cell concentrations varied from 5×10(3) cells mL(-1) to 4.66×10(5) cells mL(-1) in the network. While this parameter did not exceed 2.1×10(4) cells mL(-1) in the effluent from any water treatment plant, 50% of all the network samples contained more than 1.06×10(5) cells mL(-1). This indisputably demonstrates biological instability in this particular drinking water distribution system, which was ascribed to a loss of disinfectant residuals and concomitant bacterial growth. The study highlights the potential of using cultivation-independent methods for the assessment of chlorinated water samples. In addition, it underlines the complexity of full-scale drinking water distribution systems, and the resulting challenges to establish the causes of biological instability.

  9. Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

    PubMed Central

    2012-01-01

    Background High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Results Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and

  10. Bayesian network prior: network analysis of biological data using external knowledge

    PubMed Central

    Isci, Senol; Dogan, Haluk; Ozturk, Cengizhan; Otu, Hasan H.

    2014-01-01

    Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods. Availability: Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP. Contact: hasan.otu@bilgi.edu.tr Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24215027

  11. Phylogenetically informed logic relationships improve detection of biological network organization

    PubMed Central

    2011-01-01

    Background A "phylogenetic profile" refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses. Results Taking advantage of a phylogenetic matrix constructed from the large orthologs database Roundup, we invented a method to create balanced profiles for individual triplets of genes that guarantee equal weight on the different phylogenetic scenarios of coevolution between genes. When we applied this idea to LAPP, the method to search for logic triplets of genes, the balanced profiles resulted in significant performance improvement and the discovery of hundreds of thousands more putative triplets than unadjusted profiles. We found that logic triplets detected biological network organization and identified key proteins and their functions, ranging from neighbouring proteins in local pathways, to well separated proteins in the whole pathway, and to the interactions among different pathways at the system level. Finally, our case study suggested that the directionality in a logic relationship and the profile of a triplet could disclose the connectivity between the triplet and surrounding networks. Conclusion Balanced profiles are superior to the raw profiles employed by traditional methods of phylogenetic profiling in searching for high order gene sets. Gene triplets can provide valuable information in detection of biological network organization and identification of key genes at different levels of cellular interaction. PMID:22172058

  12. Measures of degeneracy and redundancy in biological networks

    PubMed Central

    Tononi, Giulio; Sporns, Olaf; Edelman, Gerald M.

    1999-01-01

    Degeneracy, the ability of elements that are structurally different to perform the same function, is a prominent property of many biological systems ranging from genes to neural networks to evolution itself. Because structurally different elements may produce different outputs in different contexts, degeneracy should be distinguished from redundancy, which occurs when the same function is performed by identical elements. However, because of ambiguities in the distinction between structure and function and because of the lack of a theoretical treatment, these two notions often are conflated. By using information theoretical concepts, we develop here functional measures of the degeneracy and redundancy of a system with respect to a set of outputs. These measures help to distinguish the concept of degeneracy from that of redundancy and make it operationally useful. Through computer simulations of neural systems differing in connectivity, we show that degeneracy is low both for systems in which each element affects the output independently and for redundant systems in which many elements can affect the output in a similar way but do not have independent effects. By contrast, degeneracy is high for systems in which many different elements can affect the output in a similar way and at the same time can have independent effects. We demonstrate that networks that have been selected for degeneracy have high values of complexity, a measure of the average mutual information between the subsets of a system. These measures promise to be useful in characterizing and understanding the functional robustness and adaptability of biological networks. PMID:10077671

  13. Predicting genetic interactions from Boolean models of biological networks.

    PubMed

    Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

    2015-08-01

    Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

  14. Genetic regulatory network models of biological clocks: evolutionary history matters.

    PubMed

    Knabe, Johannes F; Nehaniv, Chrystopher L; Schilstra, Maria J

    2008-01-01

    We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate periodic behaviors. GRN models may differ in the evolvability of expressive regulatory dynamics. A new class of artificial GRNs with an evolvable number of complex cis-regulatory control sites--each involving a finite number of inhibitory and activatory binding factors--is introduced, allowing realization of complex regulatory logic. Previous work on biological clocks in nature has noted the capacity of clocks to oscillate in the absence of environmental stimuli, putting forth several candidate explanations for their observed behavior, related to anticipation of environmental conditions, compartmentation of activities in time, and robustness to perturbations of various kinds or to unselected accidents of neutral selection. Several of these hypotheses are explored by evolving GRNs with and without (Gaussian) noise and blackout periods for environmental stimulation. Robustness to certain types of perturbation appears to account for some, but not all, dynamical properties of the evolved networks. Unselected abilities, also observed for biological clocks, include the capacity to adapt to change in wavelength of environmental stimulus and to clock resetting.

  15. Biological networks 101: computational modeling for molecular biologists.

    PubMed

    Scholma, Jetse; Schivo, Stefano; Urquidi Camacho, Ricardo A; van de Pol, Jaco; Karperien, Marcel; Post, Janine N

    2014-01-01

    Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that prevent their broad adoption and effective use by molecular biologists. This study clarifies the basic aspects of molecular modeling, how to convert data into useful input, as well as the number of time points and molecular parameters that should be considered for molecular regulatory models with both explanatory and predictive potential. We illustrate the necessary experimental preconditions for converting data into a computational model of network dynamics. This model requires neither a thorough background in mathematics nor precise data on intracellular concentrations, binding affinities or reaction kinetics. Finally, we show how an interactive model of crosstalk between signal transduction pathways in primary human articular chondrocytes allows insight into processes that regulate gene expression.

  16. Learning kernels from biological networks by maximizing entropy.

    PubMed

    Tsuda, Koji; Noble, William Stafford

    2004-08-04

    The diffusion kernel is a general method for computing pairwise distances among all nodes in a graph, based on the sum of weighted paths between each pair of nodes. This technique has been used successfully, in conjunction with kernel-based learning methods, to draw inferences from several types of biological networks. We show that computing the diffusion kernel is equivalent to maximizing the von Neumann entropy, subject to a global constraint on the sum of the Euclidean distances between nodes. This global constraint allows for high variance in the pairwise distances. Accordingly, we propose an alternative, locally constrained diffusion kernel, and we demonstrate that the resulting kernel allows for more accurate support vector machine prediction of protein functional classifications from metabolic and protein-protein interaction networks. Supplementary results and data are available at noble.gs.washington.edu/proj/maxent

  17. Competition for Catalytic Resources Alters Biological Network Dynamics

    NASA Astrophysics Data System (ADS)

    Rondelez, Yannick

    2012-01-01

    Genetic regulation networks orchestrate many complex cellular behaviors. Dynamic operations that take place within cells are thus dependent on the gene expression machinery, enabled by powerful enzymes such as polymerases, ribosomes, or nucleases. These generalist enzymes typically process many different substrates, potentially leading to competitive situations: by saturating the common enzyme, one substrate may down-regulate its competitors. However, most theoretical or experimental models simply omit these effects, focusing on the pattern of genetic regulatory interactions as the main determinant of network function. We show here that competition effects have important outcomes, which can be spotted within the global dynamics of experimental systems. Further we demonstrate that enzyme saturation creates a layer of cross couplings that may foster, but also hamper, the expected behavior of synthetic biology constructs.

  18. Networks in phylogenetic analysis: new tools for population biology.

    PubMed

    Morrison, David A

    2005-04-30

    Phylogenetic analysis has changed greatly in the past decade, including the more widespread appreciation of the idea that evolutionary histories are not always tree-like, and may, thus, be best represented as reticulated networks rather than as strictly dichotomous trees. Reconstructing such histories in the absence of a bifurcating speciation process is even more difficult than the usual procedure, and a range of alternative strategies have been developed. There seem to be two basic uses for a network model of evolution: the display of real but unobservable evolutionary events (i.e. a hypothesis of the true phylogenetic history), and the display of character conflict within the data itself (i.e. a summary of the data). These two general approaches are briefly reviewed here, and the strengths and weaknesses of the different implementations are compared and contrasted. Each network methodology seems to have limitations in terms of how it responds to increasing complexity (e.g. conflict) in the data, and therefore each is likely to be more appropriate for one of the two uses than for the other. Several examples using parasitological data sets illustrate the uses of networks within the context of population biology.

  19. Molecular codes in biological and chemical reaction networks.

    PubMed

    Görlich, Dennis; Dittrich, Peter

    2013-01-01

    Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.

  20. Reconstruction of Biological Networks by Incorporating Prior Knowledge into Bayesian Network Models

    PubMed Central

    Shin, Dong-Guk

    2012-01-01

    Abstract Bayesian network model is widely used for reverse engineering of biological network structures. An advantage of this model is its capability to integrate prior knowledge into the model learning process, which can lead to improving the quality of the network reconstruction outcome. Some previous works have explored this area with focus on using prior knowledge of the direct molecular links, except for a few recent ones proposing to examine the effects of molecular orderings. In this study, we propose a Bayesian network model that can integrate both direct links and orderings into the model. Random weights are assigned to these two types of prior knowledge to alleviate bias toward certain types of information. We evaluate our model performance using both synthetic data and biological data for the RAF signaling network, and illustrate the significant improvement on network structure reconstruction of the proposing models over the existing methods. We also examine the correlation between the improvement and the abundance of ordering prior knowledge. To address the issue of generating prior knowledge, we propose an approach to automatically extract potential molecular orderings from knowledge resources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) database and Gene Ontology (GO) annotation. PMID:23210479

  1. Algorithmic Perspectives of Network Transitive Reduction Problems and their Applications to Synthesis and Analysis of Biological Networks

    PubMed Central

    Aditya, Satabdi; DasGupta, Bhaskar; Karpinski, Marek

    2013-01-01

    In this survey paper, we will present a number of core algorithmic questions concerning several transitive reduction problems on network that have applications in network synthesis and analysis involving cellular processes. Our starting point will be the so-called minimum equivalent digraph problem, a classic computational problem in combinatorial algorithms. We will subsequently consider a few non-trivial extensions or generalizations of this problem motivated by applications in systems biology. We will then discuss the applications of these algorithmic methodologies in the context of three major biological research questions: synthesizing and simplifying signal transduction networks, analyzing disease networks, and measuring redundancy of biological networks. PMID:24833332

  2. Biologically relevant neural network architectures for support vector machines.

    PubMed

    Jändel, Magnus

    2014-01-01

    Neural network architectures that implement support vector machines (SVM) are investigated for the purpose of modeling perceptual one-shot learning in biological organisms. A family of SVM algorithms including variants of maximum margin, 1-norm, 2-norm and ν-SVM is considered. SVM training rules adapted for neural computation are derived. It is found that competitive queuing memory (CQM) is ideal for storing and retrieving support vectors. Several different CQM-based neural architectures are examined for each SVM algorithm. Although most of the sixty-four scanned architectures are unconvincing for biological modeling four feasible candidates are found. The seemingly complex learning rule of a full ν-SVM implementation finds a particularly simple and natural implementation in bisymmetric architectures. Since CQM-like neural structures are thought to encode skilled action sequences and bisymmetry is ubiquitous in motor systems it is speculated that trainable pattern recognition in low-level perception has evolved as an internalized motor programme.

  3. Omics and Exercise: Global Approaches for Mapping Exercise Biological Networks.

    PubMed

    Hoffman, Nolan J

    2017-03-27

    The application of global "-omics" technologies to exercise has introduced new opportunities to map the complexity and interconnectedness of biological networks underlying the tissue-specific responses and systemic health benefits of exercise. This review will introduce major research tracks and recent advancements in this emerging field, as well as critical gaps in understanding the orchestration of molecular exercise dynamics that will benefit from unbiased omics investigations. Furthermore, significant research hurdles that need to be overcome to effectively fill these gaps related to data collection, computation, interpretation, and integration across omics applications will be discussed. Collectively, a cross-disciplinary physiological and omics-based systems approach will lead to discovery of a wealth of novel exercise-regulated targets for future mechanistic validation. This frontier in exercise biology will aid the development of personalized therapeutic strategies to improve athletic performance and human health through precision exercise medicine.

  4. Methods of information theory and algorithmic complexity for network biology.

    PubMed

    Zenil, Hector; Kiani, Narsis A; Tegnér, Jesper

    2016-03-01

    We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Computational studies of gene regulatory networks: in numero molecular biology.

    PubMed

    Hasty, J; McMillen, D; Isaacs, F; Collins, J J

    2001-04-01

    Remarkable progress in genomic research is leading to a complete map of the building blocks of biology. Knowledge of this map is, in turn, setting the stage for a fundamental description of cellular function at the DNA level. Such a description will entail an understanding of gene regulation, in which proteins often regulate their own production or that of other proteins in a complex web of interactions. The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will probably involve the union of new experiments and computational modelling techniques.

  6. Incremental and unifying modelling formalism for biological interaction networks

    PubMed Central

    Yartseva, Anastasia; Klaudel, Hanna; Devillers, Raymond; Képès, François

    2007-01-01

    Background An appropriate choice of the modeling formalism from the broad range of existing ones may be crucial for efficiently describing and analyzing biological systems. Results We propose a new unifying and incremental formalism for the representation and modeling of biological interaction networks. This formalism allows automated translations into other formalisms, thus enabling a thorough study of the dynamic properties of a biological system. As a first illustration, we propose a translation into the R. Thomas' multivalued logical formalism which provides a possible semantics; a methodology for constructing such models is presented on a classical benchmark: the λ phage genetic switch. We also show how to extract from our model a classical ODE description of the dynamics of a system. Conclusion This approach provides an additional level of description between the biological and mathematical ones. It yields, on the one hand, a knowledge expression in a form which is intuitive for biologists and, on the other hand, its representation in a formal and structured way. PMID:17996051

  7. Dynamic optimization of biological networks under parametric uncertainty.

    PubMed

    Nimmegeers, Philippe; Telen, Dries; Logist, Filip; Impe, Jan Van

    2016-08-31

    Micro-organisms play an important role in various industrial sectors (including biochemical, food and pharmaceutical industries). A profound insight in the biochemical reactions inside micro-organisms enables an improved biochemical process control. Biological networks are an important tool in systems biology for incorporating microscopic level knowledge. Biochemical processes are typically dynamic and the cells have often more than one objective which are typically conflicting, e.g., minimizing the energy consumption while maximizing the production of a specific metabolite. Therefore multi-objective optimization is needed to compute trade-offs between those conflicting objectives. In model-based optimization, one of the inherent problems is the presence of uncertainty. In biological processes, this uncertainty can be present due to, e.g., inherent biological variability. Not taking this uncertainty into account, possibly leads to the violation of constraints and erroneous estimates of the actual objective function(s). To account for the variance in model predictions and compute a prediction interval, this uncertainty should be taken into account during process optimization. This leads to a challenging optimization problem under uncertainty, which requires a robustified solution. Three techniques for uncertainty propagation: linearization, sigma points and polynomial chaos expansion, are compared for the dynamic optimization of biological networks under parametric uncertainty. These approaches are compared in two case studies: (i) a three-step linear pathway model in which the accumulation of intermediate metabolites has to be minimized and (ii) a glycolysis inspired network model in which a multi-objective optimization problem is considered, being the minimization of the enzymatic cost and the minimization of the end time before reaching a minimum extracellular metabolite concentration. A Monte Carlo simulation procedure has been applied for the assessment of the

  8. Making the right connections: biological networks in the light of evolution

    PubMed Central

    Knight, Christopher G; Pinney, John W

    2009-01-01

    Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein–protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre-stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves. PMID:19722181

  9. Integrative Biology Identifies Shared Transcriptional Networks in CKD

    PubMed Central

    Martini, Sebastian; Nair, Viji; Keller, Benjamin J.; Eichinger, Felix; Hawkins, Jennifer J.; Randolph, Ann; Böger, Carsten A.; Gadegbeku, Crystal A.; Fox, Caroline S.; Cohen, Clemens D.

    2014-01-01

    A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR-associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases. PMID:24925724

  10. Integrative biology identifies shared transcriptional networks in CKD.

    PubMed

    Martini, Sebastian; Nair, Viji; Keller, Benjamin J; Eichinger, Felix; Hawkins, Jennifer J; Randolph, Ann; Böger, Carsten A; Gadegbeku, Crystal A; Fox, Caroline S; Cohen, Clemens D; Kretzler, Matthias

    2014-11-01

    A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR-associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases.

  11. Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications.

    PubMed

    Namasivayam, Aishwarya Alex; Morales, Alejandro Ferreiro; Lacave, Ángela María Fajardo; Tallam, Aravind; Simovic, Borislav; Alfaro, David Garrido; Bobbili, Dheeraj Reddy; Martin, Florian; Androsova, Ganna; Shvydchenko, Irina; Park, Jennifer; Calvo, Jorge Val; Hoeng, Julia; Peitsch, Manuel C; Racero, Manuel González Vélez; Biryukov, Maria; Talikka, Marja; Pérez, Modesto Berraquero; Rohatgi, Neha; Díaz-Díaz, Noberto; Mandarapu, Rajesh; Ruiz, Rubén Amián; Davidyan, Sergey; Narayanasamy, Shaman; Boué, Stéphanie; Guryanova, Svetlana; Arbas, Susana Martínez; Menon, Swapna; Xiang, Yang

    2016-01-01

    Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.

  12. Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

    PubMed Central

    Namasivayam, Aishwarya Alex; Morales, Alejandro Ferreiro; Lacave, Ángela María Fajardo; Tallam, Aravind; Simovic, Borislav; Alfaro, David Garrido; Bobbili, Dheeraj Reddy; Martin, Florian; Androsova, Ganna; Shvydchenko, Irina; Park, Jennifer; Calvo, Jorge Val; Hoeng, Julia; Peitsch, Manuel C.; Racero, Manuel González Vélez; Biryukov, Maria; Talikka, Marja; Pérez, Modesto Berraquero; Rohatgi, Neha; Díaz-Díaz, Noberto; Mandarapu, Rajesh; Ruiz, Rubén Amián; Davidyan, Sergey; Narayanasamy, Shaman; Boué, Stéphanie; Guryanova, Svetlana; Arbas, Susana Martínez; Menon, Swapna; Xiang, Yang

    2016-01-01

    Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications. PMID:27429547

  13. A network biology model of micronutrient related health.

    PubMed

    van Ommen, Ben; Fairweather-Tait, Susan; Freidig, Andreas; Kardinaal, Alwine; Scalbert, Augustin; Wopereis, Suzan

    2008-06-01

    Micronutrients are involved in specific biochemical pathways and have dedicated functions in the body, but they are also interconnected in complex metabolic networks, such as oxidative-reductive and inflammatory pathways and hormonal regulation, in which the overarching function is to optimise health. Post-genomic technologies, in particular metabolomics and proteomics, both of which are appropriate for plasma samples, provide a new opportunity to study the metabolic effects of micronutrients in relation to optimal health. The study of micronutrient-related health status requires a combination of data on markers of dietary exposure, markers of target function and biological response, health status metabolites, and disease parameters. When these nutrient-centred and physiology/health-centred parameters are combined and studied using a systems biology approach with bioinformatics and multivariate statistical tools, it should be possible to generate a micronutrient phenotype database. From this we can explore external factors that define the phenotype, such as lifestage and lifestyle, and the impact of genotype, and the results can also be used to define micronutrient requirements and provide dietary advice. New mechanistic insights have already been developed using biological network models, for example genes and protein-protein interactions in the aetiology of type 2 diabetes mellitus. It is hoped that the challenge of applying this approach to micronutrients will, in time, result in a change from micronutrient oriented to a health oriented views and provide a more holistic understanding of the role played by multiple micronutrients in the maintenance of homeostasis and prevention of chronic disease, for example through their involvement in oxidation and inflammation.

  14. The impact of network biology in pharmacology and toxicology.

    PubMed

    Panagiotou, G; Taboureau, O

    2012-01-01

    With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical researchers are urged to implement new types of analyses that can lead to new therapeutic interventions with increased safety and efficacy compared with existing medications.

  15. A network biology approach to denitrification in Pseudomonas aeruginosa

    DOE PAGES

    Arat, Seda; Bullerjahn, George S.; Laubenbacher, Reinhard

    2015-02-23

    Pseudomonas aeruginosa is a metabolically flexible member of the Gammaproteobacteria. Under anaerobic conditions and the presence of nitrate, P. aeruginosa can perform (complete) denitrification, a respiratory process of dissimilatory nitrate reduction to nitrogen gas via nitrite (NO₂), nitric oxide (NO) and nitrous oxide (N₂O). This study focuses on understanding the influence of environmental conditions on bacterial denitrification performance, using a mathematical model of a metabolic network in P. aeruginosa. To our knowledge, this is the first mathematical model of denitrification for this bacterium. Analysis of the long-term behavior of the network under changing concentration levels of oxygen (O₂), nitrate (NO₃),more » and phosphate (PO₄) suggests that PO₄ concentration strongly affects denitrification performance. The model provides three predictions on denitrification activity of P. aeruginosa under various environmental conditions, and these predictions are either experimentally validated or supported by pertinent biological literature. One motivation for this study is to capture the effect of PO₄ on a denitrification metabolic network of P. aeruginosa in order to shed light on mechanisms for greenhouse gas N₂O accumulation during seasonal oxygen depletion in aquatic environments such as Lake Erie (Laurentian Great Lakes, USA). Simulating the microbial production of greenhouse gases in anaerobic aquatic systems such as Lake Erie allows a deeper understanding of the contributing environmental effects that will inform studies on, and remediation strategies for, other hypoxic sites worldwide.« less

  16. CytoKavosh: A Cytoscape Plug-In for Finding Network Motifs in Large Biological Networks

    PubMed Central

    Razaghi Moghadam Kashani, Zahra; Salehzadeh-Yazdi, Ali; Khakabimamaghani, Sahand

    2012-01-01

    Network motifs are small connected sub-graphs that have recently gathered much attention to discover structural behaviors of large and complex networks. Finding motifs with any size is one of the most important problems in complex and large networks. It needs fast and reliable algorithms and tools for achieving this purpose. CytoKavosh is one of the best choices for finding motifs with any given size in any complex network. It relies on a fast algorithm, Kavosh, which makes it faster than other existing tools. Kavosh algorithm applies some well known algorithmic features and includes tricky aspects, which make it an efficient algorithm in this field. CytoKavosh is a Cytoscape plug-in which supports us in finding motifs of given size in a network that is formerly loaded into the Cytoscape work-space (directed or undirected). High performance of CytoKavosh is achieved by dynamically linking highly optimized functions of Kavosh's C++ to the Cytoscape Java program, which makes this plug-in suitable for analyzing large biological networks. Some significant attributes of CytoKavosh is efficiency in time usage and memory and having no limitation related to the implementation in motif size. CytoKavosh is implemented in a visual environment Cytoscape that is convenient for the users to interact and create visual options to analyze the structural behavior of a network. This plug-in can work on any given network and is very simple to use and generates graphical results of discovered motifs with any required details. There is no specific Cytoscape plug-in, specific for finding the network motifs, based on original concept. So, we have introduced for the first time, CytoKavosh as the first plug-in, and we hope that this plug-in can be improved to cover other options to make it the best motif-analyzing tool. PMID:22952659

  17. Computationally efficient measure of topological redundancy of biological and social networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; Dasgupta, Bhaskar; Hegde, Rashmi; Sivanathan, Gowri Sangeetha; Gitter, Anthony; Gürsoy, Gamze; Paul, Pradyut; Sontag, Eduardo

    2011-09-01

    It is well known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient, and applicable to a variety of directed networks such as cellular signaling, and metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) Social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively) correlated with the monotonicity of their dynamics.

  18. Using Sub-Network Combinations to Scale Up an Enumeration Method for Determining the Network Structures of Biological Functions

    PubMed Central

    Ouyang, Q.

    2016-01-01

    Deduction of biological regulatory networks from their functions is one of the focus areas of systems biology. Among the different techniques used in this reverse-engineering task, one powerful method is to enumerate all candidate network structures to find suitable ones. However, this method is severely limited by calculation capability: due to the brute-force approach, it is infeasible for networks with large number of nodes to be studied using traditional enumeration method because of the combinatorial explosion. In this study, we propose a new reverse-engineering technique based on the enumerating method: sub-network combinations. First, a complex biological function is divided into several sub-functions. Next, the three-node-network enumerating method is applied to search for sub-networks that are able to realize each of the sub-functions. Finally, complex whole networks are constructed by enumerating all possible combinations of sub-networks. The optimal ones are selected and analyzed. To demonstrate the effectiveness of this new method, we used it to deduct the network structures of a Pavlovian-like function. The whole Pavlovian-like network was successfully constructed by combining robust sub-networks, and the results were analyzed. With sub-network combination, the complexity has been largely reduced. Our method also provides a functional modular view of biological systems. PMID:27992476

  19. Systems biology beyond degree, hubs and scale-free networks: the case for multiple metrics in complex networks.

    PubMed

    Roy, Soumen

    2012-06-01

    Modeling and topological analysis of networks in biological and other complex systems, must venture beyond the limited consideration of very few network metrics like degree, betweenness or assortativity. A proper identification of informative and redundant entities from many different metrics, using recently demonstrated techniques, is essential. A holistic comparison of networks and growth models is best achieved only with the use of such methods.

  20. MicroRNA-regulated networks: the perfect storm for classical molecular biology, the ideal scenario for systems biology.

    PubMed

    Vera, Julio; Lai, Xin; Schmitz, Ulf; Wolkenhauer, Olaf

    2013-01-01

    MicroRNAs (miRNAs) are involved in many regulatory pathways some of which are complex networks enriched in regulatory motifs like positive or negative feedback loops or coherent and incoherent feedforward loops. Their complexity makes the understanding of their regulation difficult and the interpretation of experimental data cumbersome. In this book chapter we claim that systems biology is the appropriate approach to investigate the regulation of these miRNA-regulated networks. Systems biology is an interdisciplinary approach by which biomedical questions on biochemical networks are addressed by integrating experiments with mathematical modelling and simulation. We here introduce the foundations of the systems biology approach, the basic theoretical and computational tools used to perform model-based analyses of miRNA-regulated networks and review the scientific literature in systems biology of miRNA regulation, with a focus on cancer.

  1. CellNet: network biology applied to stem cell engineering.

    PubMed

    Cahan, Patrick; Li, Hu; Morris, Samantha A; Lummertz da Rocha, Edroaldo; Daley, George Q; Collins, James J

    2014-08-14

    Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. Biologically plausible learning in neural networks with modulatory feedback.

    PubMed

    Grant, W Shane; Tanner, James; Itti, Laurent

    2017-04-01

    Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model. To the authors' knowledge, this is the first time a border ownership network has been learned. Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. Our results predict that the mechanisms we use are integral for learning modulatory connections in the brain and furthermore that modulatory connections have a strong dependence on inhibition.

  3. Systems biology of death receptor networks: live and let die

    PubMed Central

    Lavrik, I N

    2014-01-01

    The extrinsic apoptotic pathway is initiated by death receptor activation. Death receptor activation leads to the formation of death receptor signaling platforms, resulting in the demolition of the cell. Despite the fact that death receptor-mediated apoptosis has been studied to a high level of detail, its quantitative regulation until recently has been poorly understood. This situation has dramatically changed in the last years. Creation of mathematical models of death receptor signaling led to an enormous progress in the quantitative understanding of the network regulation and provided fascinating insights into the mechanisms of apoptosis control. In the following sections, the models of the death receptor signaling and their biological implications will be addressed. Central attention will be given to the models of CD95/Fas/APO-1, an exemplified member of the death receptor signaling pathways. The CD95 death-inducing signaling complex (DISC) and regulation of CD95 DISC activity by its key inhibitor c-FLIP, have been vigorously investigated by modeling approaches, and therefore will be the major topic here. Furthermore, the non-linear dynamics of the DISC, positive feedback loops and bistability as well as stoichiometric switches in extrinsic apoptosis will be discussed. Collectively, this review gives a comprehensive view how the mathematical modeling supported by quantitative experimental approaches has provided a new understanding of the death receptor signaling network. PMID:24874731

  4. Managing biological networks by using text mining and computer-aided curation

    NASA Astrophysics Data System (ADS)

    Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

    2015-11-01

    In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

  5. Structural network analysis of biological networks for assessment of potential disease model organisms.

    PubMed

    Nabhan, Ahmed Ragab; Sarkar, Indra Neil

    2014-02-01

    Model organisms provide opportunities to design research experiments focused on disease-related processes (e.g., using genetically engineered populations that produce phenotypes of interest). For some diseases, there may be non-obvious model organisms that can help in the study of underlying disease factors. In this study, an approach is presented that leverages knowledge about human diseases and associated biological interactions networks to identify potential model organisms for a given disease category. The approach starts with the identification of functional and interaction patterns of diseases within genetic pathways. Next, these characteristic patterns are matched to interaction networks of candidate model organisms to identify similar subsystems that have characteristic patterns for diseases of interest. The quality of a candidate model organism is then determined by the degree to which the identified subsystems match genetic pathways from validated knowledge. The results of this study suggest that non-obvious model organisms may be identified through the proposed approach.

  6. Enhancement of COPD biological networks using a web-based collaboration interface.

    PubMed

    Boue, Stephanie; Fields, Brett; Hoeng, Julia; Park, Jennifer; Peitsch, Manuel C; Schlage, Walter K; Talikka, Marja; Binenbaum, Ilona; Bondarenko, Vladimir; Bulgakov, Oleg V; Cherkasova, Vera; Diaz-Diaz, Norberto; Fedorova, Larisa; Guryanova, Svetlana; Guzova, Julia; Igorevna Koroleva, Galina; Kozhemyakina, Elena; Kumar, Rahul; Lavid, Noa; Lu, Qingxian; Menon, Swapna; Ouliel, Yael; Peterson, Samantha C; Prokhorov, Alexander; Sanders, Edward; Schrier, Sarah; Schwaitzer Neta, Golan; Shvydchenko, Irina; Tallam, Aravind; Villa-Fombuena, Gema; Wu, John; Yudkevich, Ilya; Zelikman, Mariya

    2015-01-01

    The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be

  7. Enhancement of COPD biological networks using a web-based collaboration interface

    PubMed Central

    Boue, Stephanie; Fields, Brett; Hoeng, Julia; Park, Jennifer; Peitsch, Manuel C.; Schlage, Walter K.; Talikka, Marja; Binenbaum, Ilona; Bondarenko, Vladimir; Bulgakov, Oleg V.; Cherkasova, Vera; Diaz-Diaz, Norberto; Fedorova, Larisa; Guryanova, Svetlana; Guzova, Julia; Igorevna Koroleva, Galina; Kozhemyakina, Elena; Kumar, Rahul; Lavid, Noa; Lu, Qingxian; Menon, Swapna; Ouliel, Yael; Peterson, Samantha C.; Prokhorov, Alexander; Sanders, Edward; Schrier, Sarah; Schwaitzer Neta, Golan; Shvydchenko, Irina; Tallam, Aravind; Villa-Fombuena, Gema; Wu, John; Yudkevich, Ilya; Zelikman, Mariya

    2015-01-01

    The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be

  8. Complex network problems in physics, computer science and biology

    NASA Astrophysics Data System (ADS)

    Cojocaru, Radu Ionut

    There is a close relation between physics and mathematics and the exchange of ideas between these two sciences are well established. However until few years ago there was no such a close relation between physics and computer science. Even more, only recently biologists started to use methods and tools from statistical physics in order to study the behavior of complex system. In this thesis we concentrate on applying and analyzing several methods borrowed from computer science to biology and also we use methods from statistical physics in solving hard problems from computer science. In recent years physicists have been interested in studying the behavior of complex networks. Physics is an experimental science in which theoretical predictions are compared to experiments. In this definition, the term prediction plays a very important role: although the system is complex, it is still possible to get predictions for its behavior, but these predictions are of a probabilistic nature. Spin glasses, lattice gases or the Potts model are a few examples of complex systems in physics. Spin glasses and many frustrated antiferromagnets map exactly to computer science problems in the NP-hard class defined in Chapter 1. In Chapter 1 we discuss a common result from artificial intelligence (AI) which shows that there are some problems which are NP-complete, with the implication that these problems are difficult to solve. We introduce a few well known hard problems from computer science (Satisfiability, Coloring, Vertex Cover together with Maximum Independent Set and Number Partitioning) and then discuss their mapping to problems from physics. In Chapter 2 we provide a short review of combinatorial optimization algorithms and their applications to ground state problems in disordered systems. We discuss the cavity method initially developed for studying the Sherrington-Kirkpatrick model of spin glasses. We extend this model to the study of a specific case of spin glass on the Bethe

  9. The redox biology network in cancer pathophysiology and therapeutics

    PubMed Central

    Manda, Gina; Isvoranu, Gheorghita; Comanescu, Maria Victoria; Manea, Adrian; Debelec Butuner, Bilge; Korkmaz, Kemal Sami

    2015-01-01

    The review pinpoints operational concepts related to the redox biology network applied to the pathophysiology and therapeutics of solid tumors. A sophisticated network of intrinsic and extrinsic cues, integrated in the tumor niche, drives tumorigenesis and tumor progression. Critical mutations and distorted redox signaling pathways orchestrate pathologic events inside cancer cells, resulting in resistance to stress and death signals, aberrant proliferation and efficient repair mechanisms. Additionally, the complex inter-cellular crosstalk within the tumor niche, mediated by cytokines, redox-sensitive danger signals (HMGB1) and exosomes, under the pressure of multiple stresses (oxidative, inflammatory, metabolic), greatly contributes to the malignant phenotype. The tumor-associated inflammatory stress and its suppressive action on the anti-tumor immune response are highlighted. We further emphasize that ROS may act either as supporter or enemy of cancer cells, depending on the context. Oxidative stress-based therapies, such as radiotherapy and photodynamic therapy, take advantage of the cytotoxic face of ROS for killing tumor cells by a non-physiologically sudden, localized and intense oxidative burst. The type of tumor cell death elicited by these therapies is discussed. Therapy outcome depends on the differential sensitivity to oxidative stress of particular tumor cells, such as cancer stem cells, and therefore co-therapies that transiently down-regulate their intrinsic antioxidant system hold great promise. We draw attention on the consequences of the damage signals delivered by oxidative stress-injured cells to neighboring and distant cells, and emphasize the benefits of therapeutically triggered immunologic cell death in metastatic cancer. An integrative approach should be applied when designing therapeutic strategies in cancer, taking into consideration the mutational, metabolic, inflammatory and oxidative status of tumor cells, cellular heterogeneity and the

  10. The redox biology network in cancer pathophysiology and therapeutics.

    PubMed

    Manda, Gina; Isvoranu, Gheorghita; Comanescu, Maria Victoria; Manea, Adrian; Debelec Butuner, Bilge; Korkmaz, Kemal Sami

    2015-08-01

    The review pinpoints operational concepts related to the redox biology network applied to the pathophysiology and therapeutics of solid tumors. A sophisticated network of intrinsic and extrinsic cues, integrated in the tumor niche, drives tumorigenesis and tumor progression. Critical mutations and distorted redox signaling pathways orchestrate pathologic events inside cancer cells, resulting in resistance to stress and death signals, aberrant proliferation and efficient repair mechanisms. Additionally, the complex inter-cellular crosstalk within the tumor niche, mediated by cytokines, redox-sensitive danger signals (HMGB1) and exosomes, under the pressure of multiple stresses (oxidative, inflammatory, metabolic), greatly contributes to the malignant phenotype. The tumor-associated inflammatory stress and its suppressive action on the anti-tumor immune response are highlighted. We further emphasize that ROS may act either as supporter or enemy of cancer cells, depending on the context. Oxidative stress-based therapies, such as radiotherapy and photodynamic therapy, take advantage of the cytotoxic face of ROS for killing tumor cells by a non-physiologically sudden, localized and intense oxidative burst. The type of tumor cell death elicited by these therapies is discussed. Therapy outcome depends on the differential sensitivity to oxidative stress of particular tumor cells, such as cancer stem cells, and therefore co-therapies that transiently down-regulate their intrinsic antioxidant system hold great promise. We draw attention on the consequences of the damage signals delivered by oxidative stress-injured cells to neighboring and distant cells, and emphasize the benefits of therapeutically triggered immunologic cell death in metastatic cancer. An integrative approach should be applied when designing therapeutic strategies in cancer, taking into consideration the mutational, metabolic, inflammatory and oxidative status of tumor cells, cellular heterogeneity and the

  11. Design principles for the analysis and construction of robustly homeostatic biological networks.

    PubMed

    Tang, Zhe F; McMillen, David R

    2016-11-07

    Homeostatic biological systems resist external disturbances, allowing cells and organisms to maintain a constant internal state despite perturbations from their surroundings. Many biological regulatory networks are known to act homeostatically, with examples including thermal adaptation, osmoregulation, and chemotaxis. Understanding the network topologies (sets of regulatory interactions) and biological parameter regimes that can yield homeostasis in a biological system is of interest both for the study of natural biological system, and in the context of designing new biological control schemes for use in synthetic biology. Here, we examine the mathematical properties of a function that maps a biological system's inputs to its outputs, we have formulated a novel criterion (the "cofactor condition") that compactly describes the conditions for homeostasis. We further analyze the problem of robust homeostasis, wherein the system is required to maintain homeostatic behavior when its parameter values are slightly altered. We use the cofactor condition to examine previously reported examples of robust homeostasis, showing that it is a useful way to unify a number of seemingly different analyses into a single framework. Based on the observation that all previous robustly homeostatic examples fall into one of three classes, we propose a "strong cofactor condition" and use it to provide an algorithm for designing new robustly homeostatic biological networks, giving both their topologies and constraints on their parameter values. Applying the design algorithm to a three-node biological network, we construct several robustly homeostatic genetic networks, uncovering network topologies not previously identified as candidates for exhibiting robust homeostasis.

  12. Deciphering the connectivity structure of biological networks using MixNet

    PubMed Central

    Picard, Franck; Miele, Vincent; Daudin, Jean-Jacques; Cottret, Ludovic; Robin, Stéphane

    2009-01-01

    Background As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles. Results We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the E. coli transcriptional regulatory network, the macaque cortex network, a foodweb network and the Buchnera aphidicola metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering. Conclusion We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks. PMID:19534742

  13. Identifying common components across biological network graphs using a bipartite data model.

    PubMed

    Baker, Ej; Culpepper, C; Philips, C; Bubier, J; Langston, M; Chesler, Ej

    2014-01-01

    The GeneWeaver bipartite data model provides an efficient means to evaluate shared molecular components from sets derived across diverse species, disease states and biological processes. In order to adapt this model for examining related molecular components and biological networks, such as pathway or gene network data, we have developed a means to leverage the bipartite data structure to extract and analyze shared edges. Using the Pathway Commons database we demonstrate the ability to rapidly identify shared connected components among a diverse set of pathways. In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships. Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components. This approach enables self-organization of biological processes through shared biological networks.

  14. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

    PubMed

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

  15. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

    PubMed Central

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

  16. Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

    PubMed

    Ren, Li-Hong; Ding, Yong-Sheng; Shen, Yi-Zhen; Zhang, Xiang-Feng

    2008-10-01

    Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

  17. NFP: An R Package for Characterizing and Comparing of Annotated Biological Networks

    PubMed Central

    Xu, Wenjian

    2017-01-01

    Large amounts of various biological networks exist for representing different types of interaction data, such as genetic, metabolic, gene regulatory, and protein-protein relationships. Recent approaches on biological network study are based on different mathematical concepts. It is necessary to construct a uniform framework to judge the functionality of biological networks. We recently introduced a knowledge-based computational framework that reliably characterized biological networks in system level. The method worked by making systematic comparisons to a set of well-studied “basic networks,” measuring both the functional and topological similarities. A biological network could be characterized as a spectrum-like vector consisting of similarities to basic networks. Here, to facilitate the application, development, and adoption of this framework, we present an R package called NFP. This package extends our previous pipeline, offering a powerful set of functions for Network Fingerprint analysis. The software shows great potential in biological network study. The open source NFP R package is freely available under the GNU General Public License v2.0 at CRAN along with the vignette. PMID:28280740

  18. Systems infection biology: a compartmentalized immune network of pig spleen challenged with Haemophilus parasuis

    PubMed Central

    2013-01-01

    Background Network biology (systems biology) approaches are useful tools for elucidating the host infection processes that often accompany complex immune networks. Although many studies have recently focused on Haemophilus parasuis, a model of Gram-negative bacterium, little attention has been paid to the host's immune response to infection. In this article, we use network biology to investigate infection with Haemophilus parasuis in an in vivo pig model. Results By targeting the spleen immunogenome, we established an expression signature indicative of H. parasuis infection using a PCA/GSEA combined method. We reconstructed the immune network and estimated the network topology parameters that characterize the immunogene expressions in response to H. parasuis infection. The results showed that the immune network of H. parasuis infection is compartmentalized (not globally linked). Statistical analysis revealed that the reconstructed network is scale-free but not small-world. Based on the quantitative topological prioritization, we inferred that the C1R-centered clique might play a vital role in responding to H. parasuis infection. Conclusions Here, we provide the first report of reconstruction of the immune network in H. parasuis-infected porcine spleen. The distinguishing feature of our work is the focus on utilizing the immunogenome for a network biology-oriented analysis. Our findings complement and extend the frontiers of knowledge of host infection biology for H. parasuis and also provide a new clue for systems infection biology of Gram-negative bacilli in mammals. PMID:23339624

  19. Systems infection biology: a compartmentalized immune network of pig spleen challenged with Haemophilus parasuis.

    PubMed

    Zhao, Ming; Liu, Xiang-dong; Li, Xin-yun; Chen, Hong-bo; Jin, Hui; Zhou, Rui; Zhu, Meng-jin; Zhao, Shu-hong

    2013-01-22

    Network biology (systems biology) approaches are useful tools for elucidating the host infection processes that often accompany complex immune networks. Although many studies have recently focused on Haemophilus parasuis, a model of Gram-negative bacterium, little attention has been paid to the host's immune response to infection. In this article, we use network biology to investigate infection with Haemophilus parasuis in an in vivo pig model. By targeting the spleen immunogenome, we established an expression signature indicative of H. parasuis infection using a PCA/GSEA combined method. We reconstructed the immune network and estimated the network topology parameters that characterize the immunogene expressions in response to H. parasuis infection. The results showed that the immune network of H. parasuis infection is compartmentalized (not globally linked). Statistical analysis revealed that the reconstructed network is scale-free but not small-world. Based on the quantitative topological prioritization, we inferred that the C1R-centered clique might play a vital role in responding to H. parasuis infection. Here, we provide the first report of reconstruction of the immune network in H. parasuis-infected porcine spleen. The distinguishing feature of our work is the focus on utilizing the immunogenome for a network biology-oriented analysis. Our findings complement and extend the frontiers of knowledge of host infection biology for H. parasuis and also provide a new clue for systems infection biology of Gram-negative bacilli in mammals.

  20. NDEx: A Community Resource for Sharing and Publishing of Biological Networks.

    PubMed

    Pillich, Rudolf T; Chen, Jing; Rynkov, Vladimir; Welker, David; Pratt, Dexter

    2017-01-01

    Networks are a powerful and flexible paradigm that facilitate communication and computation about interactions of any type, whether social, economic, or biological. NDEx, the Network Data Exchange, is an online commons to enable new modes of collaboration and publication using biological networks. NDEx creates an access point and interface to a broad range of networks, whether they express molecular interactions, curated relationships from literature, or the outputs of systematic analysis of big data. Research organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx can also facilitate the integration of networks as data in electronic publications, thus making a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow seamlessly between scientists.

  1. Gene regulatory networks and the underlying biology of developmental toxicity

    EPA Science Inventory

    Embryonic cells are specified by large-scale networks of functionally linked regulatory genes. Knowledge of the relevant gene regulatory networks is essential for understanding phenotypic heterogeneity that emerges from disruption of molecular functions, cellular processes or sig...

  2. Gene regulatory networks and the underlying biology of developmental toxicity

    EPA Science Inventory

    Embryonic cells are specified by large-scale networks of functionally linked regulatory genes. Knowledge of the relevant gene regulatory networks is essential for understanding phenotypic heterogeneity that emerges from disruption of molecular functions, cellular processes or sig...

  3. A Glimpse to Background and Characteristics of Major Molecular Biological Networks

    PubMed Central

    Altaf-Ul-Amin, Md.; Katsuragi, Tetsuo; Sato, Tetsuo; Kanaya, Shigehiko

    2015-01-01

    Recently, biology has become a data intensive science because of huge data sets produced by high throughput molecular biological experiments in diverse areas including the fields of genomics, transcriptomics, proteomics, and metabolomics. These huge datasets have paved the way for system-level analysis of the processes and subprocesses of the cell. For system-level understanding, initially the elements of a system are connected based on their mutual relations and a network is formed. Among omics researchers, construction and analysis of biological networks have become highly popular. In this review, we briefly discuss both the biological background and topological properties of major types of omics networks to facilitate a comprehensive understanding and to conceptualize the foundation of network biology. PMID:26491677

  4. Community Structure Reveals Biologically Functional Modules in MEF2C Transcriptional Regulatory Network

    PubMed Central

    Alcalá-Corona, Sergio A.; Velázquez-Caldelas, Tadeo E.; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2016-01-01

    Gene regulatory networks are useful to understand the activity behind the complex mechanisms in transcriptional regulation. A main goal in contemporary biology is using such networks to understand the systemic regulation of gene expression. In this work, we carried out a systematic study of a transcriptional regulatory network derived from a comprehensive selection of all potential transcription factor interactions downstream from MEF2C, a human transcription factor master regulator. By analyzing the connectivity structure of such network, we were able to find different biologically functional processes and specific biochemical pathways statistically enriched in communities of genes into the network, such processes are related to cell signaling, cell cycle and metabolism. In this way we further support the hypothesis that structural properties of biological networks encode an important part of their functional behavior in eukaryotic cells. PMID:27252657

  5. Integration of biological networks and gene expression data using Cytoscape

    PubMed Central

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

    2013-01-01

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

  6. 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.

  7. Theoretical approaches for the dynamics of complex biological systems from information of networks

    PubMed Central

    MOCHIZUKI, Atsushi

    2016-01-01

    Modern biology has provided many examples of large networks describing the interactions between multiple species of bio-molecules. It is believed that the dynamics of molecular activities based on such networks are the origin of biological functions. On the other hand, we have a limited understanding for dynamics of molecular activity based on networks. To overcome this problem, we have developed two structural theories, by which the important aspects of the dynamical properties of the system are determined only from information on the network structure, without assuming other quantitative details. The first theory, named Linkage Logic, determines a subset of molecules in regulatory networks, by which any long-term dynamical behavior of the whole system can be identified/controlled. The second theory, named Structural Sensitivity Analysis, determines the sensitivity responses of the steady state of chemical reaction networks to perturbations of the reaction rate. The first and second theories investigate the dynamical properties of regulatory and reaction networks, respectively. The first theory targets the attractors of the regulatory network systems, whereas the second theory applies only to the steady states of the reaction network systems, but predicts their detailed behavior. To demonstrate the utility of our methods several biological network systems, and show they are practically useful to analyze behaviors of biological systems. PMID:27725468

  8. A method for developing regulatory gene set networks to characterize complex biological systems.

    PubMed

    Suphavilai, Chayaporn; Zhu, Liugen; Chen, Jake Y

    2015-01-01

    Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease. R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.

  9. NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks.

    PubMed

    Theodosiou, Theodosios; Efstathiou, Georgios; Papanikolaou, Nikolas; Kyrpides, Nikos C; Bagos, Pantelis G; Iliopoulos, Ioannis; Pavlopoulos, Georgios A

    2017-07-14

    Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

  10. Implications of Developmental Gene Regulatory Networks Inside and Outside Developmental Biology.

    PubMed

    Peter, Isabelle S; Davidson, Eric H

    2016-01-01

    The insight that the genomic control of developmental process is encoded in the form of gene regulatory networks has profound impacts on many areas of modern bioscience. Most importantly, it affects developmental biology itself, as it means that a causal understanding of development requires knowledge of the architecture of regulatory network interactions. Furthermore, it follows that functional changes in developmental gene regulatory networks have to be considered as a primary mechanism for evolutionary process. We here discuss some of the recent advances in gene regulatory network biology and how they have affected our current understanding of development, evolution, and regulatory genomics.

  11. Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research.

    PubMed

    Jinawath, Natini; Bunbanjerdsuk, Sacarin; Chayanupatkul, Maneerat; Ngamphaiboon, Nuttapong; Asavapanumas, Nithi; Svasti, Jisnuson; Charoensawan, Varodom

    2016-11-22

    With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation is frequently used to capture the presence of these molecules as well as their relationship. Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time. We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors. These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify previously overlooked problems, leading to rapid advances in medicine. From the clinicians' point of view, it is necessary to bridge the gap between theoretical network biology and practical biomedical research, in order to improve the diagnosis, prevention, and treatment of the world's major diseases.

  12. Revisiting the variation of clustering coefficient of biological networks suggests new modular structure.

    PubMed

    Hao, Dapeng; Ren, Cong; Li, Chuanxing

    2012-05-01

    A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

  13. Revisiting the variation of clustering coefficient of biological networks suggests new modular structure

    PubMed Central

    2012-01-01

    Background A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. Results We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn’t show dependence of degree. Conclusions Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to “deterministic model” of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy. PMID:22548803

  14. A powerful weighted statistic for detecting group differences of directed biological networks.

    PubMed

    Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Xu, Jing; Ma, Daoxin; Xue, Fuzhong

    2016-09-30

    Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website.

  15. A powerful weighted statistic for detecting group differences of directed biological networks

    PubMed Central

    Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Xu, Jing; Ma, Daoxin; Xue, Fuzhong

    2016-01-01

    Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website. PMID:27686331

  16. 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.

  17. Robustness of the p53 network and biological hackers.

    PubMed

    Dartnell, Lewis; Simeonidis, Evangelos; Hubank, Michael; Tsoka, Sophia; Bogle, I David L; Papageorgiou, Lazaros G

    2005-06-06

    The p53 protein interaction network is crucial in regulating the metazoan cell cycle and apoptosis. Here, the robustness of the p53 network is studied by analyzing its degeneration under two modes of attack. Linear Programming is used to calculate average path lengths among proteins and the network diameter as measures of functionality. The p53 network is found to be robust to random loss of nodes, but vulnerable to a targeted attack against its hubs, as a result of its architecture. The significance of the results is considered with respect to mutational knockouts of proteins and the directed attacks mounted by tumour inducing viruses.

  18. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network.

    PubMed

    Hughes, Tyler B; Dang, Na Le; Miller, Grover P; Swamidass, S Joshua

    2016-08-24

    Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network-the XenoSite reactivity model-using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the

  19. Pandora, a pathway and network discovery approach based on common biological evidence.

    PubMed

    Zhang, Kelvin Xi; Ouellette, B F Francis

    2010-02-15

    Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information. Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein-protein interaction, genetic interaction, domain-domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view.

  20. Pandora, a PAthway and Network DiscOveRy Approach based on common biological evidence

    PubMed Central

    Zhang, Kelvin Xi; Ouellette, B. F. Francis

    2010-01-01

    Motivation: Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information. Results: Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein–protein interaction, genetic interaction, domain–domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view. Availability: The method has been implemented in Perl and is available for downloading from http://www.oicr.on.ca/research/ouellette/pandora. It is distributed under the terms of GPL (http://opensource.org/licenses/gpl-2.0.php) Contact: francis@oicr.on.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20031970

  1. Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network.

    PubMed

    Podder, Avijit; Jatana, Nidhi; Latha, N

    2014-09-21

    Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network.

  2. Why network approach can promote a new way of thinking in biology

    PubMed Central

    Giuliani, Alessandro; Filippi, Simonetta; Bertolaso, Marta

    2014-01-01

    This work deals with the particular nature of network-based approach in biology. We will comment about the shift from the consideration of the molecular layer as the definitive place where causative process start to the elucidation of the among elements (at any level of biological organization they are located) interaction network as the main goal of scientific explanation. This shift comes from the intrinsic nature of networks where the properties of a specific node are determined by its position in the entire network (top-down explanation) while the global network characteristics emerge from the nodes wiring pattern (bottom-up explanation). This promotes a “middle-out” paradigm formally identical to the time honored chemical thought holding big promises in the study of biological regulation. PMID:24782892

  3. Why network approach can promote a new way of thinking in biology.

    PubMed

    Giuliani, Alessandro; Filippi, Simonetta; Bertolaso, Marta

    2014-01-01

    This work deals with the particular nature of network-based approach in biology. We will comment about the shift from the consideration of the molecular layer as the definitive place where causative process start to the elucidation of the among elements (at any level of biological organization they are located) interaction network as the main goal of scientific explanation. This shift comes from the intrinsic nature of networks where the properties of a specific node are determined by its position in the entire network (top-down explanation) while the global network characteristics emerge from the nodes wiring pattern (bottom-up explanation). This promotes a "middle-out" paradigm formally identical to the time honored chemical thought holding big promises in the study of biological regulation.

  4. Mapping Transcriptional Networks in Plants: Data-Driven Discovery of Novel Biological Mechanisms.

    PubMed

    Gaudinier, Allison; Brady, Siobhan M

    2016-04-29

    In plants, systems biology approaches have led to the generation of a variety of large data sets. Many of these data are created to elucidate gene expression profiles and their corresponding transcriptional regulatory mechanisms across a range of tissue types, organs, and environmental conditions. In an effort to map the complexity of this transcriptional regulatory control, several types of experimental assays have been used to map transcriptional regulatory networks. In this review, we discuss how these methods can be best used to identify novel biological mechanisms by focusing on the appropriate biological context. Translating network biology back to gene function in the plant, however, remains a challenge. We emphasize the need for validation and insight into the underlying biological processes to successfully exploit systems approaches in an effort to determine the emergent properties revealed by network analyses.

  5. CompNet: a GUI based tool for comparison of multiple biological interaction networks.

    PubMed

    Kuntal, Bhusan K; Dutta, Anirban; Mande, Sharmila S

    2016-04-26

    Network visualization and analysis tools aid in better understanding of complex biological systems. Furthermore, to understand the differences in behaviour of system(s) under various environmental conditions (e.g. stress, infection), comparing multiple networks becomes necessary. Such comparisons between multiple networks may help in asserting causation and in identifying key components of the studied biological system(s). Although many available network comparison methods exist, which employ techniques like network alignment and querying to compute pair-wise similarity between selected networks, most of them have limited features with respect to interactive visual comparison of multiple networks. In this paper, we present CompNet - a graphical user interface based network comparison tool, which allows visual comparison of multiple networks based on various network metrics. CompNet allows interactive visualization of the union, intersection and/or complement regions of a selected set of networks. Different visualization features (e.g. pie-nodes, edge-pie matrix, etc.) aid in easy identification of the key nodes/interactions and their significance across the compared networks. The tool also allows one to perform network comparisons on the basis of neighbourhood architecture of constituent nodes and community compositions, a feature particularly useful while analyzing biological networks. To demonstrate the utility of CompNet, we have compared a (time-series) human gene-expression dataset, post-infection by two strains of Mycobacterium tuberculosis, overlaid on the human protein-protein interaction network. Using various functionalities of CompNet not only allowed us to comprehend changes in interaction patterns over the course of infection, but also helped in inferring the probable fates of the host cells upon infection by the two strains. CompNet is expected to be a valuable visual data mining tool and is freely available for academic use from http

  6. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology.

    PubMed

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view.

  7. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology

    PubMed Central

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view. PMID:23515240

  8. Commentary: Biochemistry and Molecular Biology Educators Launch National Network

    ERIC Educational Resources Information Center

    Bailey, Cheryl; Bell, Ellis; Johnson, Margaret; Mattos, Carla; Sears, Duane; White, Harold B.

    2010-01-01

    The American Society of Biochemistry and Molecular Biology (ASBMB) has launched an National Science Foundation (NSF)-funded 5 year project to support biochemistry and molecular biology educators learning what and how students learn. As a part of this initiative, hundreds of life scientists will plan and develop a rich central resource for…

  9. Commentary: Biochemistry and Molecular Biology Educators Launch National Network

    ERIC Educational Resources Information Center

    Bailey, Cheryl; Bell, Ellis; Johnson, Margaret; Mattos, Carla; Sears, Duane; White, Harold B.

    2010-01-01

    The American Society of Biochemistry and Molecular Biology (ASBMB) has launched an National Science Foundation (NSF)-funded 5 year project to support biochemistry and molecular biology educators learning what and how students learn. As a part of this initiative, hundreds of life scientists will plan and develop a rich central resource for…

  10. Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

    NASA Astrophysics Data System (ADS)

    Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

    2017-03-01

    Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological

  11. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

    PubMed

    Miconi, Thomas

    2017-02-23

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

  12. Graphical methods for analysing feedback in biological networks - A survey

    NASA Astrophysics Data System (ADS)

    Radde, Nicole; Bar, Nadav S.; Banaji, Murad

    2010-01-01

    Observed phenotypes usually arise from complex networks of interacting cell components. Qualitative information about the structure of these networks is often available, while quantitative information may be partial or absent. It is natural then to ask what, if anything, we can learn about the behaviour of the system solely from its qualitative structure. In this article we review some techniques which can be applied to answer this question, focussing in particular on approaches involving graphical representations of model structure. By applying these techniques to various cellular network examples, we discuss their strengths and limitations, and point to future research directions.

  13. Reduction of Qualitative Models of Biological Networks for Transient Dynamics Analysis.

    PubMed

    Pauleve, Loic

    2017-09-06

    Qualitative models of dynamics of signalling pathways and gene regulatory networks allow to capture temporal properties of biological networks while requiring few parameters. However, these discrete models typically suffer from the so-called state space explosion problem which makes the formal assessment of their potential behaviours very challenging.

  14. How Transcription Networks Evolve and Produce Biological Novelty.

    PubMed

    Nocedal, Isabel; Johnson, Alexander D

    2015-01-01

    The rewiring of gene regulatory networks over evolutionary timescales produces changes in the patterns of gene expression and is a major source of diversity among species. Yet the molecular mechanisms underlying evolutionary rewiring are only beginning to be understood. Here, we discuss recent analyses in ascomycete yeasts that have revealed several general principles of network rewiring. Specifically, we discuss how transcription networks can maintain a functional output despite changes in mechanism, how specific types of constraints alter available evolutionary trajectories, and how regulatory rewiring can ultimately lead to phenotypic novelty. We also argue that the structure and "logic" of extant gene regulatory networks can largely be accounted for by constraints that shape their evolutionary trajectories. Copyright © 2015 Cold Spring Harbor Laboratory Press; all rights reserved.

  15. Parenclitic networks: uncovering new functions in biological data

    PubMed Central

    Zanin, Massimiliano; Alcazar, Joaquín Medina; Carbajosa, Jesus Vicente; Paez, Marcela Gomez; Papo, David; Sousa, Pedro; Menasalvas, Ernestina; Boccaletti, Stefano

    2014-01-01

    We introduce a novel method to represent time independent, scalar data sets as complex networks. We apply our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana. In the proposed network representation, the most important genes for the plant response turn out to be the nodes with highest centrality in appropriately reconstructed networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure. PMID:24870931

  16. Parenclitic networks: uncovering new functions in biological data.

    PubMed

    Zanin, Massimiliano; Alcazar, Joaquín Medina; Carbajosa, Jesus Vicente; Paez, Marcela Gomez; Papo, David; Sousa, Pedro; Menasalvas, Ernestina; Boccaletti, Stefano

    2014-05-29

    We introduce a novel method to represent time independent, scalar data sets as complex networks. We apply our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana. In the proposed network representation, the most important genes for the plant response turn out to be the nodes with highest centrality in appropriately reconstructed networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure.

  17. Network motifs – recurring circuitry components in biological systems

    EPA Science Inventory

    Environmental perturbations, elicited by chemicals, dietary supplements, and drugs, can alter the dynamics of the molecular circuits and networks operating in cells, leading to multiple disease endpoints. Multi-component signal transduction pathways and gene regulatory circuits u...

  18. Using Network Biology to Bridge Pharmacokinetics and Pharmacodynamics in Oncology

    PubMed Central

    Kirouac, D C; Onsum, M D

    2013-01-01

    If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico. PMID:24005988

  19. Pattern Learning, Damage and Repair within Biological Neural Networks

    NASA Astrophysics Data System (ADS)

    Siu, Theodore; Fitzgerald O'Neill, Kate; Shinbrot, Troy

    2015-03-01

    Traumatic brain injury (TBI) causes damage to neural networks, potentially leading to disability or even death. Nearly one in ten of these patients die, and most of the remainder suffer from symptoms ranging from headaches and nausea to convulsions and paralysis. In vitro studies to develop treatments for TBI have limited in vivo applicability, and in vitro therapies have even proven to worsen the outcome of TBI patients. We propose that this disconnect between in vitro and in vivo outcomes may be associated with the fact that in vitro tests assess indirect measures of neuronal health, but do not investigate the actual function of neuronal networks. Therefore in this talk, we examine both in vitro and in silico neuronal networks that actually perform a function: pattern identification. We allow the networks to execute genetic, Hebbian, learning, and additionally, we examine the effects of damage and subsequent repair within our networks. We show that the length of repaired connections affects the overall pattern learning performance of the network and we propose therapies that may improve function following TBI in clinical settings.

  20. Characterization of Adaptation by Morphology in a Planar Biological Network of Plasmodial Slime Mold

    NASA Astrophysics Data System (ADS)

    Ito, Masateru; Okamoto, Riki; Takamatsu, Atsuko

    2011-07-01

    Growth processes of a planar biological network of plasmodium of a true slime mold, Physarum polycephalum, were analyzed quantitatively. The plasmodium forms a transportation network through which protoplasm conveys nutrients, oxygen, and cellular organelles similarly to blood in a mammalian vascular network. To analyze the network structure, vertices were defined at tube bifurcation points. Then edges were defined for the tubes connecting both end vertices. Morphological analysis was attempted along with conventional topological analysis, revealing that the growth process of the plasmodial network structure depends on environmental conditions. In an attractive condition, the network is a polygonal lattice with more than six edges per vertex at the early stage and the hexagonal lattice at a later stage. Through all growing stages, the tube structure was not highly developed but an unstructured protoplasmic thin sheet was dominantly formed. The network size is small. In contrast, in the repulsive condition, the network is a mixture of polygonal lattice and tree-graph. More specifically, the polygonal lattice has more than six edges per vertex in the early stage, then a tree-graph structure is added to the lattice network at a later stage. The thick tube structure was highly developed. The network size, in the meaning of Euclidean distance but not topological one, grows considerably. Finally, the biological meaning of the environment-dependent network structure in the plasmodium is discussed.

  1. System Review about Function Role of ESCC Driver Gene KDM6A by Network Biology Approach.

    PubMed

    Ran, Jihua; Li, Hui; Li, Huiwu

    2016-01-01

    Background. KDM6A (Lysine (K)-Specific Demethylase 6A) is the driver gene related to esophageal squamous cell carcinoma (ESCC). In order to provide more biological insights into KDM6A, in this paper, we treat PPI (protein-protein interaction) network derived from KDM6A as a conceptual framework and follow it to review its biological function. Method. We constructed a PPI network with Cytoscape software and performed clustering of network with Clust&See. Then, we evaluate the pathways, which are statistically involved in the network derived from KDM6A. Lastly, gene ontology analysis of clusters of genes in the network was conducted. Result. The network includes three clusters that consist of 74 nodes connected via 453 edges. Fifty-five pathways are statistically involved in the network and most of them are functionally related to the processes of cell cycle, gene expression, and carcinogenesis. The biology themes of clusters 1, 2, and 3 are chromatin modification, regulation of gene expression by transcription factor complex, and control of cell cycle, respectively. Conclusion. The PPI network presents a panoramic view which can facilitate for us to understand the function role of KDM6A. It is a helpful way by network approach to perform system review on a certain gene.

  2. Universality in Nonlinear Elasticity of Biological and Polymeric Networks and Gels

    NASA Astrophysics Data System (ADS)

    Carrillo, Jan-Michael; Dobrynin, Andrey

    2011-03-01

    Networks and gels are part of our everyday experience starting from automotive tires and rubber bands to biological tissues and cells. Biological and polymeric networks show remarkably high deformability at relatively small stresses and can sustain reversible deformations up to ten times of their initial size. A distinctive feature of these materials is highly nonlinear stress-strain curves leading to material hardening with increasing deformation. This differentiates networks and gels from conventional materials, such as metals and glasses, showing linear stress-strain relationship in the reversible deformation regime. Using theoretical analysis and molecular dynamics simulations we propose and test a model that describes nonlinear mechanical properties of a broad variety of biological and polymeric networks and gels by relating their macroscopic strain hardening behavior with molecular parameters of the network strands. This model provides a universal relationship between the strain-dependent network modulus and the network deformation and explains strain-hardening of natural rubber, synthetic polymeric networks, and biopolymer networks of actin, collagen, fibrin, vimentin and neurofilaments. NSF: DMR-1004576.

  3. Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

    PubMed

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

    The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks. Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

  4. The Stochastic Evolutionary Game for a Population of Biological Networks Under Natural Selection

    PubMed Central

    Chen, Bor-Sen; Ho, Shih-Ju

    2014-01-01

    In this study, a population of evolutionary biological networks is described by a stochastic dynamic system with intrinsic random parameter fluctuations due to genetic variations and external disturbances caused by environmental changes in the evolutionary process. Since information on environmental changes is unavailable and their occurrence is unpredictable, they can be considered as a game player with the potential to destroy phenotypic stability. The biological network needs to develop an evolutionary strategy to improve phenotypic stability as much as possible, so it can be considered as another game player in the evolutionary process, ie, a stochastic Nash game of minimizing the maximum network evolution level caused by the worst environmental disturbances. Based on the nonlinear stochastic evolutionary game strategy, we find that some genetic variations can be used in natural selection to construct negative feedback loops, efficiently improving network robustness. This provides larger genetic robustness as a buffer against neutral genetic variations, as well as larger environmental robustness to resist environmental disturbances and maintain a network phenotypic traits in the evolutionary process. In this situation, the robust phenotypic traits of stochastic biological networks can be more frequently selected by natural selection in evolution. However, if the harbored neutral genetic variations are accumulated to a sufficiently large degree, and environmental disturbances are strong enough that the network robustness can no longer confer enough genetic robustness and environmental robustness, then the phenotype robustness might break down. In this case, a network phenotypic trait may be pushed from one equilibrium point to another, changing the phenotypic trait and starting a new phase of network evolution through the hidden neutral genetic variations harbored in network robustness by adaptive evolution. Further, the proposed evolutionary game is extended to

  5. Logical Reduction of Biological Networks to Their Most Determinative Components.

    PubMed

    Matache, Mihaela T; Matache, Valentin

    2016-07-01

    Boolean networks have been widely used as models for gene regulatory networks, signal transduction networks, or neural networks, among many others. One of the main difficulties in analyzing the dynamics of a Boolean network and its sensitivity to perturbations or mutations is the fact that it grows exponentially with the number of nodes. Therefore, various approaches for simplifying the computations and reducing the network to a subset of relevant nodes have been proposed in the past few years. We consider a recently introduced method for reducing a Boolean network to its most determinative nodes that yield the highest information gain. The determinative power of a node is obtained by a summation of all mutual information quantities over all nodes having the chosen node as a common input, thus representing a measure of information gain obtained by the knowledge of the node under consideration. The determinative power of nodes has been considered in the literature under the assumption that the inputs are independent in which case one can use the Bahadur orthonormal basis. In this article, we relax that assumption and use a standard orthonormal basis instead. We use techniques of Hilbert space operators and harmonic analysis to generate formulas for the sensitivity to perturbations of nodes, quantified by the notions of influence, average sensitivity, and strength. Since we work on finite-dimensional spaces, our formulas and estimates can be and are formulated in plain matrix algebra terminology. We analyze the determinative power of nodes for a Boolean model of a signal transduction network of a generic fibroblast cell. We also show the similarities and differences induced by the alternative complete orthonormal basis used. Among the similarities, we mention the fact that the knowledge of the states of the most determinative nodes reduces the entropy or uncertainty of the overall network significantly. In a special case, we obtain a stronger result than in previous

  6. Network-based drug discovery by integrating systems biology and computational technologies

    PubMed Central

    Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

    2013-01-01

    Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

  7. Network-based drug discovery by integrating systems biology and computational technologies.

    PubMed

    Leung, Elaine L; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua; Liu, Liang

    2013-07-01

    Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.

  8. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    PubMed

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Mining bridge and brick motifs from complex biological networks for functionally and statistically significant discovery.

    PubMed

    Cheng, Chia-Ying; Huang, Chung-Yuan; Sun, Chuen-Tsai

    2008-02-01

    A major task for postgenomic systems biology researchers is to systematically catalogue molecules and their interactions within living cells. Advancements in complex-network theory are being made toward uncovering organizing principles that govern cell formation and evolution, but we lack understanding of how molecules and their interactions determine how complex systems function. Molecular bridge motifs include isolated motifs that neither interact nor overlap with others, whereas brick motifs act as network foundations that play a central role in defining global topological organization. To emphasize their structural organizing and evolutionary characteristics, we define bridge motifs as consisting of weak links only and brick motifs as consisting of strong links only, then propose a method for performing two tasks simultaneously, which are as follows: 1) detecting global statistical features and local connection structures in biological networks and 2) locating functionally and statistically significant network motifs. To further understand the role of biological networks in system contexts, we examine functional and topological differences between bridge and brick motifs for predicting biological network behaviors and functions. After observing brick motif similarities between E. coli and S. cerevisiae, we note that bridge motifs differentiate C. elegans from Drosophila and sea urchin in three types of networks. Similarities (differences) in bridge and brick motifs imply similar (different) key circuit elements in the three organisms. We suggest that motif-content analyses can provide researchers with global and local data for real biological networks and assist in the search for either isolated or functionally and topologically overlapping motifs when investigating and comparing biological system functions and behaviors.

  10. 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

  11. Using network biology to bridge pharmacokinetics and pharmacodynamics in oncology.

    PubMed

    Kirouac, D C; Onsum, M D

    2013-09-04

    If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e71; doi:10.1038/psp.2013.38; published online 4 September 2013.

  12. On protocols and measures for the validation of supervised methods for the inference of biological networks.

    PubMed

    Schrynemackers, Marie; Küffner, Robert; Geurts, Pierre

    2013-12-03

    Networks provide a natural representation of molecular biology knowledge, in particular to model relationships between biological entities such as genes, proteins, drugs, or diseases. Because of the effort, the cost, or the lack of the experiments necessary for the elucidation of these networks, computational approaches for network inference have been frequently investigated in the literature. In this paper, we examine the assessment of supervised network inference. Supervised inference is based on machine learning techniques that infer the network from a training sample of known interacting and possibly non-interacting entities and additional measurement data. While these methods are very effective, their reliable validation in silico poses a challenge, since both prediction and validation need to be performed on the basis of the same partially known network. Cross-validation techniques need to be specifically adapted to classification problems on pairs of objects. We perform a critical review and assessment of protocols and measures proposed in the literature and derive specific guidelines how to best exploit and evaluate machine learning techniques for network inference. Through theoretical considerations and in silico experiments, we analyze in depth how important factors influence the outcome of performance estimation. These factors include the amount of information available for the interacting entities, the sparsity and topology of biological networks, and the lack of experimentally verified non-interacting pairs.

  13. Revealing gene regulation and association through biological networks

    USDA-ARS?s Scientific Manuscript database

    This review had first summarized traditional methods used by plant breeders for genetic improvement, such as QTL analysis and transcriptomic analysis. With accumulating data, we can draw a network that comprises all possible links between members of a community, including protein–protein interaction...

  14. Biology Inspired Approach for Communal Behavior in Sensor Networks

    NASA Technical Reports Server (NTRS)

    Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

    2006-01-01

    Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.

  15. Constructing module maps for integrated analysis of heterogeneous biological networks

    PubMed Central

    Amar, David; Shamir, Ron

    2014-01-01

    Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here, we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that considerably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein–protein interactions and negative genetic interactions in yeast, we discover epistatic relations among protein complexes. Second, we analyze protein–protein interactions and DNA damage-specific positive genetic interactions in yeast and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non–small-cell lung cancer patients, we analyze networks of global co-expression and disease-dependent differential co-expression and identify a sharp drop in correlation between two modules of immune activation processes, with possible microRNA control. Our study demonstrates that module maps are a powerful tool for deeper analysis of heterogeneous high-throughput omic data. PMID:24497192

  16. [Action mechanism of drugs for preventing and treating coronary heart disease based on biological networks].

    PubMed

    Zhang, Yan-Ling; Huang, Ming-Feng; Qiao, Yan-Jiang

    2013-08-01

    Coronary heart disease (CHD) related genes and targets, as well as drug targets for preventing and treating CHD were taken as the study objects to build a CHD disease network and a drug action network preventing and treating CHD. Such topological characteristic parameters of the networks as degree distribution, characteristic path length, connectivity and heterogeneity were analyzed to verify the reliability of the networks. On that basis, the intersection calculation was conducted for both networks to analyze the drug action mechanism of their sub-networks. The disease network are composed of 15,221 nodes and 31,177 sides, while the drug action network preventing and treating CHD has 15,073 nodes and 32,376 sides. Both of their topological characteristic parameters showed scale-free small world structural characteristics. Two reaction pathways in the sub-networks-calcitonin gene-related peptide and IL-6 activated JAK/STAT were taken as examples to discuss the indirect action mechanism for preventing and treating CHD. The results showed that the biological network analysis method combining the disease network and the drug action network is helpful to further studies on the action mechanism of the drugs, and significant to the prevention and treatment of diseases.

  17. Biological Networks Underlying Soybean Seed Oil Composition and Content

    USDA-ARS?s Scientific Manuscript database

    Soybean is the most important oil crop in the United States. Production of soybean seed oil requires coordinated expression of many biological components and pathways, which is further regulated by seed development and phyto-hormones. A new research project is initiated in my laboratory to delineat...

  18. Detection of Complexes in Biological Networks Through Diversified Dense Subgraph Mining.

    PubMed

    Ma, Xiuli; Zhou, Guangyu; Shang, Jingbo; Wang, Jingjing; Peng, Jian; Han, Jiawei

    2017-09-01

    Protein-protein interaction (PPI) networks, providing a comprehensive landscape of protein interaction patterns, enable us to explore biological processes and cellular components at multiple resolutions. For a biological process, a number of proteins need to work together to perform a job. Proteins densely interact with each other, forming large molecular machines or cellular building blocks. Identification of such densely interconnected clusters or protein complexes from PPI networks enables us to obtain a better understanding of the hierarchy and organization of biological processes and cellular components. However, most existing graph clustering algorithms on PPI networks often cannot effectively detect densely connected subgraphs and overlapped subgraphs. In this article, we formulate the problem of complex detection as diversified dense subgraph mining and introduce a novel approximation algorithm to efficiently enumerate putative protein complexes from biological networks. The key insight of our algorithm is that instead of enumerating all dense subgraphs, we only need to find a small diverse subset of subgraphs that cover as many proteins as possible. The problem is modeled as finding a diverse set of maximal dense subgraphs where we develop highly effective pruning techniques to guarantee efficiency. To scale up to large networks, we devise a divide-and-conquer approach to speed up the algorithm in a distributed manner. By comparing with existing clustering and dense subgraph-based algorithms on several yeast and human PPI networks, we demonstrate that our method can detect more putative protein complexes and achieves better prediction accuracy.

  19. Gene network biological validity based on gene-gene interaction relevance.

    PubMed

    Gómez-Vela, Francisco; Díaz-Díaz, Norberto

    2014-01-01

    In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

  20. MetNetGE: interactive views of biological networks and ontologies

    PubMed Central

    2010-01-01

    Background Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large metabolic networks often result in a dense web of connections that is difficult to interpret biologically. The MetNetGE application organizes and visualizes biological networks in a meaningful way to improve performance and biological interpretability. Results MetNetGE is an interactive visualization tool based on the Google Earth platform. MetNetGE features novel visualization techniques for pathway and ontology information display. Instead of simply showing hundreds of pathways in a complex graph, MetNetGE gives an overview of the network using the hierarchical pathway ontology using a novel layout, called the Enhanced Radial Space-Filling (ERSF) approach that allows the network to be summarized compactly. The non-tree edges in the pathway or gene ontology, which represent pathways or genes that belong to multiple categories, are linked using orbital connections in a third dimension. Biologists can easily identify highly activated pathways or gene ontology categories by mapping of summary experiment statistics such as coefficient of variation and overrepresentation values onto the visualization. After identifying such pathways, biologists can focus on the corresponding region to explore detailed pathway structure and experimental data in an aligned 3D tiered layout. In this paper, the use of MetNetGE is illustrated with pathway diagrams and data from E. coli and Arabidopsis. Conclusions MetNetGE is a visualization tool that organizes biological networks according to a hierarchical ontology structure. The ERSF technique assigns attributes in 3D space, such as color, height, and transparency, to any ontological structure. For hierarchical data, the novel ERSF layout enables the user to identify pathways or categories that are differentially regulated in particular experiments. Met

  1. AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES

    PubMed Central

    DARABOS, CHRISTIAN; QIU, JINGYA; MOORE, JASON H.

    2015-01-01

    Complex diseases are the result of intricate interactions between genetic, epigenetic and environmental factors. In previous studies, we used epidemiological and genetic data linking environmental exposure or genetic variants to phenotypic disease to construct Human Phenotype Networks and separately analyze the effects of both environment and genetic factors on disease interactions. To better capture the intricacies of the interactions between environmental exposure and the biological pathways in complex disorders, we integrate both aspects into a single “tripartite” network. Despite extensive research, the mechanisms by which chemical agents disrupt biological pathways are still poorly understood. In this study, we use our integrated network model to identify specific biological pathway candidates possibly disrupted by environmental agents. We conjecture that a higher number of co-occurrences between an environmental substance and biological pathway pair can be associated with a higher likelihood that the substance is involved in disrupting that pathway. We validate our model by demonstrating its ability to detect known arsenic and signal transduction pathway interactions and speculate on candidate cell-cell junction organization pathways disrupted by cadmium. The validation was supported by distinct publications of cell biology and genetic studies that associated environmental exposure to pathway disruption. The integrated network approach is a novel method for detecting the biological effects of environmental exposures. A better understanding of the molecular processes associated with specific environmental exposures will help in developing targeted molecular therapies for patients who have been exposed to the toxicity of environmental chemicals. PMID:26776169

  2. Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

    PubMed

    Koehler, Jacob; Rawlings, Chris; Verrier, Paul; Mitchell, Rowan; Skusa, Andre; Ruegg, Alexander; Philippi, Stephan

    2005-01-01

    The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

  3. A Biologically-Inspired Neural Network Architecture for Image Processing

    DTIC Science & Technology

    1990-12-01

    network culture, and software development. Thanks to Dr. Steven K. Rogers, Dr. Matthew Kabrisky, Captain Dennis Ruck and Captain Greg Tarr, I had...Current Knowledge ................... 1 1.1.1 Neuronal Stimulus Specificity ................ 2 1.1.2 Phase Synchrony ........................ 3 1.1.3 Gabor ...83 List of Figures Figure Page 1. 2-D Windowed Patterns of Varying Periodicities and Orientations . . 2 2. Examples of 2-D Gabor Functions (1:3

  4. Detecting modules in biological networks by edge weight clustering and entropy significance

    PubMed Central

    Lecca, Paola; Re, Angela

    2015-01-01

    Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data, finally resulting in more biologically plausible models. In this study, we present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features, compared to current approaches, lie in: (1) the simultaneous exploitation of network node and edge weights to improve the biological interpretability of the connected components detected, (2) the assessment of their statistical significance, and (3) the identification of emerging topological properties in the detected connected components. WG-Cluster utilizes three major steps: (i) an unsupervised version of k-means edge-based algorithm detects sub-graphs with similar edge weights, (ii) a fast-greedy algorithm detects connected components which are then scored and selected according to the statistical significance of their scores, and (iii) an analysis of the convolution between sub-graph mean edge weight and connected component score provides a summarizing view of the connected components. WG-Cluster can be applied to directed and undirected networks of different types of interacting entities and scales up to large omics data sets. Here, we show that WG-Cluster can be

  5. An S-System Parameter Estimation Method (SPEM) for Biological Networks

    PubMed Central

    Yang, Xinyi; Dent, Jennifer E.

    2012-01-01

    Abstract Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes. By basing the parameterizing approach on a known power-law relationship model between connected genes (S-system), we are able to account for non-linearity in the network, without compromising the ability to analyze network characteristics. In this article, we introduce the S-System Parameter Estimation Method (SPEM). SPEM, a freely available R software package (http://www.picb.ac.cn/ClinicalGenomicNTW/temp3.html), takes gene expression data in time series and returns the network of interactions as a set of differential equations. The methods, which are presented and tested here, are shown to provide accurate results not only on synthetic data, but more importantly on real and therefore noisy by nature, biological data. In summary, SPEM shows high sensitivity and positive predicted values, as well as free availability and expansibility (because based on open source software). We expect these characteristics to make it a useful and broadly applicable software in the challenging reconstruction of dynamic gene networks. PMID:22300319

  6. Biological Implications of Dynamical Phases in Non-equilibrium Networks

    NASA Astrophysics Data System (ADS)

    Murugan, Arvind; Vaikuntanathan, Suriyanarayanan

    2016-03-01

    Biology achieves novel functions like error correction, ultra-sensitivity and accurate concentration measurement at the expense of free energy through Maxwell Demon-like mechanisms. The design principles and free energy trade-offs have been studied for a variety of such mechanisms. In this review, we emphasize a perspective based on dynamical phases that can explain commonalities shared by these mechanisms. Dynamical phases are defined by typical trajectories executed by non-equilibrium systems in the space of internal states. We find that coexistence of dynamical phases can have dramatic consequences for function vs free energy cost trade-offs. Dynamical phases can also provide an intuitive picture of the design principles behind such biological Maxwell Demons.

  7. Recent advances in mammalian synthetic biology-design of synthetic transgene control networks.

    PubMed

    Tigges, Marcel; Fussenegger, Martin

    2009-08-01

    Capitalizing on an era of functional genomic research, systems biology offers a systematic quantitative analysis of existing biological systems thereby providing the molecular inventory of biological parts that are currently being used for rational synthesis and engineering of complex biological systems with novel and potentially useful functions-an emerging discipline known as synthetic biology. During the past decade synthetic biology has rapidly developed from simple control devices fine-tuning the activity of single genes and proteins to multi-gene/protein-based transcription and signaling networks providing new insight into global control and molecular reaction dynamics, thereby enabling the design of novel drug-synthesis pathways as well as genetic devices with unmatched biological functions. While pioneering synthetic devices have first been designed as test, toy, and teaser systems for use in prokaryotes and lower eukaryotes, first examples of a systematic assembly of synthetic gene networks in mammalian cells has sketched the full potential of synthetic biology: foster novel therapeutic opportunities in gene and cell-based therapies. Here we provide a concise overview on the latest advances in mammalian synthetic biology.

  8. Reverse engineering biological networks :applications in immune responses to bio-toxins.

    SciTech Connect

    Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.; Haaland, David Michael; Timlin, Jerilyn Ann; Thomas, Edward Victor; Slepoy, Alexander; Zhang, Zhaoduo; May, Elebeoba Eni; Martin, Shawn Bryan; Faulon, Jean-Loup Michel

    2005-12-01

    Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.

  9. Quantitative assessment of biological impact using transcriptomic data and mechanistic network models

    SciTech Connect

    Thomson, Ty M.; Sewer, Alain; Martin, Florian; Belcastro, Vincenzo; Frushour, Brian P.; Gebel, Stephan; Park, Jennifer; Schlage, Walter K.; Talikka, Marja; Vasilyev, Dmitry M.; Westra, Jurjen W.; Hoeng, Julia; Peitsch, Manuel C.

    2013-11-01

    Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology. - Highlights: • The impact of biologically active substances is quantified at multiple levels. • The systems-level impact integrates the perturbations of individual networks. • The networks capture the relationships between

  10. Investigating the Combinatory Effects of Biological Networks on Gene Co-expression

    PubMed Central

    Zhang, Cheng; Lee, Sunjae; Mardinoglu, Adil; Hua, Qiang

    2016-01-01

    Co-expressed genes often share similar functions, and gene co-expression networks have been widely used in studying the functionality of gene modules. Previous analysis indicated that genes are more likely to be co-expressed if they are either regulated by the same transcription factors, forming protein complexes or sharing similar topological properties in protein-protein interaction networks. Here, we reconstructed transcriptional regulatory and protein-protein networks for Saccharomyces cerevisiae using well-established databases, and we evaluated their co-expression activities using publically available gene expression data. Based on our network-dependent analysis, we found that genes that were co-regulated in the transcription regulatory networks and shared similar neighbors in the protein-protein networks were more likely to be co-expressed. Moreover, their biological functions were closely related. PMID:27445830

  11. MCF: a tool to find multi-scale community profiles in biological networks.

    PubMed

    Gao, Shang; Chen, Alan; Rahmani, Ali; Jarada, Tamer; Alhajj, Reda; Demetrick, Doug; Zeng, Jia

    2013-12-01

    Recent developments of complex graph clustering methods have implicated the practical applications with biological networks in different settings. Multi-scale Community Finder (MCF) is a tool to profile network communities (i.e., clusters of nodes) with the control of community sizes. The controlling parameter is referred to as the scale of the network community profile. MCF is able to find communities in all major types of networks including directed, signed, bipartite, and multi-slice networks. The fast computation promotes the practicability of the tool for large-scaled analysis (e.g., protein-protein interaction and gene co-expression networks). MCF is distributed as an open-source C++ package for academic use with both command line and user interface options, and can be downloaded at http://bsdxd.cpsc.ucalgary.ca/MCF. Detailed user manual and sample data sets are also available at the project website. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  12. Slow poisoning and destruction of networks: Edge proximity and its implications for biological and infrastructure networks

    NASA Astrophysics Data System (ADS)

    Banerjee, Soumya Jyoti; Sinha, Saptarshi; Roy, Soumen

    2015-02-01

    We propose a network metric, edge proximity, Pe, which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high Pe might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by Pe leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. Pe can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by Pe causes notable efficiency loss in U.S. and European power grid networks. Pe identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by Pe form the backbone of long food web chains. Finally, we scrutinize the potential of Pe in edge controllability dynamics of directed networks.

  13. Slow poisoning and destruction of networks: edge proximity and its implications for biological and infrastructure networks.

    PubMed

    Banerjee, Soumya Jyoti; Sinha, Saptarshi; Roy, Soumen

    2015-02-01

    We propose a network metric, edge proximity, P(e), which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high P(e) might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by P(e) leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. P(e) can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by P(e) causes notable efficiency loss in U.S. and European power grid networks. P(e) identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by P(e) form the backbone of long food web chains. Finally, we scrutinize the potential of P(e) in edge controllability dynamics of directed networks.

  14. 3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

    NASA Technical Reports Server (NTRS)

    Ross, Muriel D.; Meyer, Glenn; Lam, Tony; Cutler, Lynn; Vaziri, Parshaw

    1990-01-01

    Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

  15. 3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

    NASA Technical Reports Server (NTRS)

    Ross, Muriel D.; Meyer, Glenn; Lam, Tony; Cutler, Lynn; Vaziri, Parshaw

    1990-01-01

    Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

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

    PubMed Central

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

    2017-01-01

    Abstract Summary: 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. Availability and Implementation: GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius. Contact: genius.psbl@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27993775

  17. WikiPathways App for Cytoscape: Making biological pathways amenable to network analysis and visualization.

    PubMed

    Kutmon, Martina; Lotia, Samad; Evelo, Chris T; Pico, Alexander R

    2014-01-01

    In this paper we present the open-source WikiPathways app for Cytoscape ( http://apps.cytoscape.org/apps/wikipathways) that can be used to import biological pathways for data visualization and network analysis. WikiPathways is an open, collaborative biological pathway database that provides fully annotated pathway diagrams for manual download or through web services. The WikiPathways app allows users to load pathways in two different views: as an annotated pathway ideal for data visualization and as a simple network to perform computational analysis. An example pathway and dataset are used to demonstrate the functionality of the WikiPathways app and how they can be combined and used together with other apps. More than 3000 downloads in the first 12 months following its release in August 2013 highlight the importance and adoption of the app in the network biology field.

  18. Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

    PubMed

    Kriegeskorte, Nikolaus

    2015-11-24

    Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

  19. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    PubMed

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. The BIOSCI electronic newsgroup network for the biological sciences. Final report, October 1, 1992--June 30, 1996

    SciTech Connect

    Kristofferson, D.; Mack, D.

    1996-10-01

    This is the final report for a DOE funded project on BIOSCI Electronic Newsgroup Network for the biological sciences. A usable network for scientific discussion, major announcements, problem solving, etc. has been created.

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

    PubMed Central

    2014-01-01

    Background Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. Results The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. Conclusions We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases’ common biology, and in the elaboration of diagnosis and treatments. PMID:24460644

  2. Networking Biology: The Origins of Sequence-Sharing Practices in Genomics.

    PubMed

    Stevens, Hallam

    2015-10-01

    The wide sharing of biological data, especially nucleotide sequences, is now considered to be a key feature of genomics. Historians and sociologists have attempted to account for the rise of this sharing by pointing to precedents in model organism communities and in natural history. This article supplements these approaches by examining the role that electronic networking technologies played in generating the specific forms of sharing that emerged in genomics. The links between early computer users at the Stanford Artificial Intelligence Laboratory in the 1960s, biologists using local computer networks in the 1970s, and GenBank in the 1980s, show how networking technologies carried particular practices of communication, circulation, and data distribution from computing into biology. In particular, networking practices helped to transform sequences themselves into objects that had value as a community resource.

  3. Construction of biological networks from unstructured information based on a semi-automated curation workflow.

    PubMed

    Szostak, Justyna; Ansari, Sam; Madan, Sumit; Fluck, Juliane; Talikka, Marja; Iskandar, Anita; De Leon, Hector; Hofmann-Apitius, Martin; Peitsch, Manuel C; Hoeng, Julia

    2015-06-17

    Capture and representation of scientific knowledge in a structured format are essential to improve the understanding of biological mechanisms involved in complex diseases. Biological knowledge and knowledge about standardized terminologies are difficult to capture from literature in a usable form. A semi-automated knowledge extraction workflow is presented that was developed to allow users to extract causal and correlative relationships from scientific literature and to transcribe them into the computable and human readable Biological Expression Language (BEL). The workflow combines state-of-the-art linguistic tools for recognition of various entities and extraction of knowledge from literature sources. Unlike most other approaches, the workflow outputs the results to a curation interface for manual curation and converts them into BEL documents that can be compiled to form biological networks. We developed a new semi-automated knowledge extraction workflow that was designed to capture and organize scientific knowledge and reduce the required curation skills and effort for this task. The workflow was used to build a network that represents the cellular and molecular mechanisms implicated in atherosclerotic plaque destabilization in an apolipoprotein-E-deficient (ApoE(-/-)) mouse model. The network was generated using knowledge extracted from the primary literature. The resultant atherosclerotic plaque destabilization network contains 304 nodes and 743 edges supported by 33 PubMed referenced articles. A comparison between the semi-automated and conventional curation processes showed similar results, but significantly reduced curation effort for the semi-automated process. Creating structured knowledge from unstructured text is an important step for the mechanistic interpretation and reusability of knowledge. Our new semi-automated knowledge extraction workflow reduced the curation skills and effort required to capture and organize scientific knowledge. The

  4. Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks.

    PubMed

    Naudé, Jérémie; Cessac, Bruno; Berry, Hugues; Delord, Bruno

    2013-09-18

    Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal activity, cardinal for the proper functioning of nervous systems. In invertebrates, HIP is critical for orchestrating stereotyped activity patterns. The functional impact of HIP remains more obscure in vertebrate networks, where higher order cognitive processes rely on complex neural dynamics. The hypothesis has emerged that HIP might control the complexity of activity dynamics in recurrent networks, with important computational consequences. However, conflicting results about the causal relationships between cellular HIP, network dynamics, and computational performance have arisen from machine-learning studies. Here, we assess how cellular HIP effects translate into collective dynamics and computational properties in biological recurrent networks. We develop a realistic multiscale model including a generic HIP rule regulating the neuronal threshold with actual molecular signaling pathways kinetics, Dale's principle, sparse connectivity, synaptic balance, and Hebbian synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a working point at which inputs are transduced by large derivative ranges of the transfer function. This cellular mechanism ensures increased network dynamics complexity, robust balance with SP at the edge of chaos, and improved input separability. Although critically dependent upon balanced excitatory and inhibitory drives, these effects display striking robustness to changes in network architecture, learning rates, and input features. Thus, the mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in neural networks. Understanding this robustness is an important challenge to unraveling principles underlying self-organization around criticality in biological recurrent neural networks.

  5. Causal inference in biology networks with integrated belief propagation.

    PubMed

    Chang, Rui; Karr, Jonathan R; Schadt, Eric E

    2015-01-01

    Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

  6. Multichannel Convolutional Neural Network for Biological Relation Extraction

    PubMed Central

    Quan, Chanqin; Sun, Xiao; Bai, Wenjun

    2016-01-01

    The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of “vocabulary gap” and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores. PMID:28053977

  7. Stochastic noncooperative and cooperative evolutionary game strategies of a population of biological networks under natural selection.

    PubMed

    Chen, Bor-Sen; Yeh, Chin-Hsun

    2017-09-05

    We review current static and dynamic evolutionary game strategies of biological networks and discuss the lack of random genetic variations and stochastic environmental disturbances in these models. To include these factors, a population of evolving biological networks is modeled as a nonlinear stochastic biological system with Poisson-driven genetic variations and random environmental fluctuations (stimuli). To gain insight into the evolutionary game theory of stochastic biological networks under natural selection, the phenotypic robustness and network evolvability of noncooperative and cooperative evolutionary game strategies are discussed from a stochastic Nash game perspective. The noncooperative strategy can be transformed into an equivalent multi-objective optimization problem and is shown to display significantly improved network robustness to tolerate genetic variations and buffer environmental disturbances, maintaining phenotypic traits for longer than the cooperative strategy. However, the noncooperative case requires greater effort and more compromises between partly conflicting players. Global linearization is used to simplify the problem of solving nonlinear stochastic evolutionary games. Finally, a simple stochastic evolutionary model of a metabolic pathway is simulated to illustrate the procedure of solving for two evolutionary game strategies and to confirm and compare their respective characteristics in the evolutionary process. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Integration of network biology and imaging to study cancer phenotypes and responses

    PubMed Central

    Tian, Ye; Wang, Sean S.; Zhang, Zhen; Rodriguez, Olga C.; Petricoin, Emanuel; Shih, Ie-Ming; Chan, Daniel; Avantaggiati, Maria; Yu, Guoqiang; Ye, Shaozhen; Clarke, Robert; Wang, Chao; Zhang, Bai; Wang, Yue; Albanese, Chris

    2014-01-01

    Ever growing “omics” data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology. PMID:25750594

  9. CORE-Net: exploiting prior knowledge and preferential attachment to infer biological interaction networks.

    PubMed

    Montefusco, F; Cosentino, C; Amato, F

    2010-09-01

    The problem of reverse engineering in the topology of functional interaction networks from time-course experimental data has received considerable attention in literature, due to the potential applications in the most diverse fields, comprising engineering, biology, economics and social sciences. The present work introduces a novel technique, CORE-Net, which addresses this problem focusing on the case of biological interaction networks. The method is based on the representation of the network in the form of a dynamical system and on an iterative convex optimisation procedure. A first advantage of the proposed approach is that it allows to exploit qualitative prior knowledge about the network interactions, of the same kind as typically available from biological literature and databases. A second novel contribution consists of exploiting the growth and preferential attachment mechanisms to improve the inference performances when dealing with networks which exhibit a scale-free topology. The technique is first assessed through numerical tests on in silico random networks, subsequently it is applied to reverse engineering a cell cycle regulatory subnetwork in Saccharomyces cerevisiae from experimental microarray data. These tests show that the combined exploitation of prior knowledge and preferential attachment significantly improves the predictions with respect to other approaches.

  10. A comprehensive analysis of the dynamic biological networks in HCV induced hepatocarcinogenesis.

    PubMed

    He, Bing; Zhang, Hao; Shi, Tieliu

    2011-04-19

    Hepatocellular carcinoma (HCC) is a primary malignancy of the liver, which is closely related to hepatitis C and cirrhosis. The molecular mechanisms underlying the hepatocarcinogenesis induced by HCV infection remain clarified from a standpoint of systems biology. By integrating data from protein-protein interactions, transcriptional regulation, and disease related microarray analysis, we carried out a dynamic biological network analysis on the progression of HCV induced hepatocarcinogenesis, and systematically explored the potentially disease-related mechanisms through a network view. The dysfunctional interactions among proteins and deregulatory relationships between transcription factors and their target genes could be causes for the occurrence and progression of this disease. The six pathologically defined disease stages in the development and progression of HCC after HCV infection were included in this study. We constructed disease-related biological networks for each disease stage, and identified progression-related sub-networks that potentially play roles in the developmental stage of the corresponding disease and participate in the later stage of cancer progression. In addition, we identified novel risk factors related to HCC based on the analysis of the progression-related sub-networks. The dynamic characteristics of the network reflect important features of the disease development and progression, which provide important information for us to further explore underlying mechanisms of the disease.

  11. FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.

    PubMed

    Wang, Ting; Ren, Zhao; Ding, Ying; Fang, Zhou; Sun, Zhe; MacDonald, Matthew L; Sweet, Robert A; Wang, Jieru; Chen, Wei

    2016-02-01

    Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer's disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named "FastGGM".

  12. Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

    PubMed Central

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine. PMID:25364745

  13. Systems Biology Approaches to the Study of Biological Networks Underlying Alzheimer's Disease: Role of miRNAs.

    PubMed

    Roth, Wera; Hecker, David; Fava, Eugenio

    2016-01-01

    MicroRNAs (miRNAs) are emerging as significant regulators of mRNA complexity in the human central nervous system (CNS) thereby controlling distinct gene expression profiles in a spatio-temporal manner during development, neuronal plasticity, aging and (age-related) neurodegeneration, including Alzheimer's disease (AD). Increasing effort is expended towards dissecting and deciphering the molecular and genetic mechanisms of neurobiological and pathological functions of these brain-enriched miRNAs. Along these lines, recent data pinpoint distinct miRNAs and miRNA networks being linked to APP splicing, processing and Aβ pathology (Lukiw et al., Front Genet 3:327, 2013), and furthermore, to the regulation of tau and its cellular subnetworks (Lau et al., EMBO Mol Med 5:1613, 2013), altogether underlying the onset and propagation of Alzheimer's disease. MicroRNA profiling studies in Alzheimer's disease suffer from poor consensus which is an acknowledged concern in the field, and constitutes one of the current technical challenges. Hence, a strong demand for experimental and computational systems biology approaches arises, to incorporate and integrate distinct levels of information and scientific knowledge into a complex system of miRNA networks in the context of the transcriptome, proteome and metabolome in a given cellular environment. Here, we will discuss the state-of-the-art technologies and computational approaches on hand that may lead to a deeper understanding of the complex biological networks underlying the pathogenesis of Alzheimer's disease.

  14. Design principles and specificity in biological networks with cross activation

    NASA Astrophysics Data System (ADS)

    Hu, Bo; Levine, Herbert; Rappel, Wouter-Jan

    2011-04-01

    Cells sense and respond to diverse environmental stimuli using a set of intracellular signaling components. Often, the signal transduction pathways contain shared components which lead to cross activation at different levels of the pathway. To discover the design principles that ensure signaling specificity is a challenging task, especially for pathways that contain numerous components. Here, we present an analysis of cross-activating pathways and show that a general inhibitory scheme, asymmetric hierarchical inhibition, is sufficient to ensure signaling specificity. Based on this inhibitory scheme, we are able to enumerate all possible network topologies containing two inhibitory links that guarantee specificity. Furthermore, we apply our methodology to the mating and filamentous growth pathways of the yeast model system Saccharomyces cerevisiae. We enumerate the possible ways to wire this model system and determine which topology is consistent with experimental data.

  15. 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

  16. Laser polarization fluorescence of the networks of optically anisotropic biological crystals

    NASA Astrophysics Data System (ADS)

    Ushenko, Y. A.; Dubolazov, A. V.; Angelsky, A. P.; Sidor, M. I.; Bodnar, G. B.; Koval, G.; Zabolotna, N. I.; Smolarz, A.; Junisbekov, M. Sh.

    2013-01-01

    The present work is devoted to investigation of mechanisms of optical anisotropy of biological tissues polycrystalline networks and laser polarization fluorescence. The model of complex optical anisotropy, which takes into account both linear and circular birefringence, as well as linear and circular dichroism of fibrillar networks of histological sections of women reproductive sphere is proposed. The data of statistical, correlation and fractal processing of coordinate distributions of laser polarization fluorescence is provided. The technique of azimuthally invariant Mueller-matrix mapping of laser polarization fluorescence of protein networks in the tasks of differentiation of benign and malignant tumors of uterus wall is elaborated.

  17. The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

    PubMed Central

    Ramoni, Marco F.

    2010-01-01

    The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms. PMID:19906839

  18. Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

    PubMed Central

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

    Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems. PMID:21850228

  19. NetMets: software for quantifying and visualizing errors in biological network segmentation

    PubMed Central

    2012-01-01

    One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization. PMID:22607549

  20. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

    PubMed Central

    Herrgård, Markus J.; Swainston, Neil; Dobson, Paul; Dunn, Warwick B.; Arga, K. Yalçin; Arvas, Mikko; Blüthgen, Nils; Borger, Simon; Costenoble, Roeland; Heinemann, Matthias; Hucka, Michael; Le Novère, Nicolas; Li, Peter; Liebermeister, Wolfram; Mo, Monica L.; Oliveira, Ana Paula; Petranovic, Dina; Pettifer, Stephen; Simeonidis, Evangelos; Smallbone, Kieran; Spasić, Irena; Weichart, Dieter; Brent, Roger; Broomhead, David S.; Westerhoff, Hans V.; Kırdar, Betül; Penttilä, Merja; Klipp, Edda; Palsson, Bernhard Ø.; Sauer, Uwe; Oliver, Stephen G.; Mendes, Pedro; Nielsen, Jens; Kell, Douglas B.

    2014-01-01

    Genomic data now allow the large-scale manual or semi-automated reconstruction of metabolic networks. A network reconstruction represents a highly curated organism-specific knowledge base. A few genome-scale network reconstructions have appeared for metabolism in the baker’s yeast Saccharomyces cerevisiae. These alternative network reconstructions differ in scope and content, and further have used different terminologies to describe the same chemical entities, thus making comparisons between them difficult. The formulation of a ‘community consensus’ network that collects and formalizes the ‘community knowledge’ of yeast metabolism is thus highly desirable. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. Special emphasis is laid on referencing molecules to persistent databases or using database-independent forms such as SMILES or InChI strings, since this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language, and we describe the manner in which it can be maintained as a community resource. It should serve as a common denominator for system biology studies of yeast. Similar strategies will be of benefit to communities studying genome-scale metabolic networks of other organisms. PMID:18846089

  1. Prediction of novel drug indications using network driven biological data prioritization and integration

    PubMed Central

    2014-01-01

    Background With the rapid development of high-throughput genomic technologies and the accumulation of genome-wide datasets for gene expression profiling and biological networks, the impact of diseases and drugs on gene expression can be comprehensively characterized. Drug repositioning offers the possibility of reduced risks in the drug discovery process, thus it is an essential step in drug development. Results Computational prediction of drug-disease interactions using gene expression profiling datasets and biological networks is a new direction in drug repositioning that has gained increasing interest. We developed a computational framework to build disease-drug networks using drug- and disease-specific subnetworks. The framework incorporates protein networks to refine drug and disease associated genes and prioritize genes in disease and drug specific networks. For each drug and disease we built multiple networks using gene expression profiling and text mining. Finally a logistic regression model was used to build functional associations between drugs and diseases. Conclusions We found that representing drugs and diseases by genes with high centrality degree in gene networks is the most promising representation of drug or disease subnetworks. PMID:24397863

  2. Structural identifiability of cyclic graphical models of biological networks with latent variables.

    PubMed

    Wang, Yulin; Lu, Na; Miao, Hongyu

    2016-06-13

    Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

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

    PubMed

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

    2017-07-14

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

  4. Discriminating different classes of biological networks by analyzing the graphs spectra distribution.

    PubMed

    Takahashi, Daniel Yasumasa; Sato, João Ricardo; Ferreira, Carlos Eduardo; Fujita, André

    2012-01-01

    The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.

  5. Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules.

    PubMed

    Tripathi, Shailesh; Moutari, Salissou; Dehmer, Matthias; Emmert-Streib, Frank

    2016-03-18

    It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells. In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks. Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system.

  6. What do interaction network metrics tell us about specialization and biological traits?

    PubMed

    Blüthgen, Nico; Fründ, Jochen; Vázquez, Diego P; Menzel, Florian

    2008-12-01

    The structure of ecological interaction networks is often interpreted as a product of meaningful ecological and evolutionary mechanisms that shape the degree of specialization in community associations. However, here we show that both unweighted network metrics (connectance, nestedness, and degree distribution) and weighted network metrics (interaction evenness, interaction strength asymmetry) are strongly constrained and biased by the number of observations. Rarely observed species are inevitably regarded as "specialists," irrespective of their actual associations, leading to biased estimates of specialization. Consequently, a skewed distribution of species observation records (such as the lognormal), combined with a relatively low sampling density typical for ecological data, already generates a "nested" and poorly "connected" network with "asymmetric interaction strengths" when interactions are neutral. This is confirmed by null model simulations of bipartite networks, assuming that partners associate randomly in the absence of any specialization and any variation in the correspondence of biological traits between associated species (trait matching). Variation in the skewness of the frequency distribution fundamentally changes the outcome of network metrics. Therefore, interpretation of network metrics in terms of fundamental specialization and trait matching requires an appropriate control for such severe constraints imposed by information deficits. When using an alternative approach that controls for these effects, most natural networks of mutualistic or antagonistic systems show a significantly higher degree of reciprocal specialization (exclusiveness) than expected under neutral conditions. A higher exclusiveness is coherent with a tighter coevolution and suggests a lower ecological redundancy than implied by nested networks.

  7. RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS1

    PubMed Central

    Silva, Ricardo; Heller, Katherine; Ghahramani, Zoubin; Airoldi, Edoardo M.

    2013-01-01

    Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects S = {A(1) : B(1), A(2) : B(2), …, A(N) : B(N)}, measures how well other pairs A : B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided. PMID:24587838

  8. Modelling Biological Clocks with Bio-PEPA: Stochasticity and Robustness for the Neurospora crassa Circadian Network

    NASA Astrophysics Data System (ADS)

    Akman, Ozgur E.; Ciocchetta, Federica; Degasperi, Andrea; Guerriero, Maria Luisa

    Circadian clocks are biochemical networks, present in nearly all living organisms, whose function is to regulate the expression of specific mRNAs and proteins to synchronise rhythms of metabolism, physiology and behaviour to the 24 hour day/night cycle. Because of their experimental tractability and biological significance, circadian clocks have been the subject of a number of computational modelling studies.

  9. Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms

    PubMed Central

    Li, Le; Yip, Kevin Y.

    2016-01-01

    Currently most terms and term-term relationships in Gene Ontology (GO) are defined manually, which creates cost, consistency and completeness issues. Recent studies have demonstrated the feasibility of inferring GO automatically from biological networks, which represents an important complementary approach to GO construction. These methods (NeXO and CliXO) are unsupervised, which means 1) they cannot use the information contained in existing GO, 2) the way they integrate biological networks may not optimize the accuracy, and 3) they are not customized to infer the three different sub-ontologies of GO. Here we present a semi-supervised method called Unicorn that extends these previous methods to tackle the three problems. Unicorn uses a sub-tree of an existing GO sub-ontology as training part to learn parameters in integrating multiple networks. Cross-validation results show that Unicorn reliably inferred the left-out parts of each specific GO sub-ontology. In addition, by training Unicorn with an old version of GO together with biological networks, it successfully re-discovered some terms and term-term relationships present only in a new version of GO. Unicorn also successfully inferred some novel terms that were not contained in GO but have biological meanings well-supported by the literature.Availability: Source code of Unicorn is available at http://yiplab.cse.cuhk.edu.hk/unicorn/. PMID:27976738

  10. On Biological Network Visualization: Understanding Challenges, Measuring the Status Quo, and Estimating Saliency of Visual Attributes

    ERIC Educational Resources Information Center

    Gopal, Nikhil

    2017-01-01

    Biomedical research increasingly relies on the analysis and visualization of a wide range of collected data. However, for certain research questions, such as those investigating the interconnectedness of biological elements, the sheer quantity and variety of data results in rather uninterpretable--this is especially true for network visualization,…

  11. Continuous Cotemporal Probabilistic Modeling of Systems Biology Networks from Sparse Data

    PubMed Central

    John, David J.; Fetrow, Jacquelyn S.; Norris, James L.

    2014-01-01

    Modeling of biological networks is a difficult endeavour, but exploration of this problem is essential for understanding the systems behaviour of biological processes. In this contribution, developed for sparse data, we present a new continuous Bayesian graphical learning algorithm to cotemporally model proteins in signaling networks and genes in transcriptional regulatory networks. In this continuous Bayesian algorithm the correlation matrix is singular because the number of time points is less than the number of biological entities (genes or proteins). A suitable restriction on the degree of the graph’s vertices is applied and a Metropolis-Hastings algorithm is guided by a BIC-based posterior probability score. Ten independent and diverse runs of the algorithm are conducted, so that the probability space is properly well-explored. Diagnostics to test the applicability of the algorithm to the specific data sets are developed; this is a major benefit of the methodology. This novel algorithm is applied to two time course experimental data sets: 1) protein modification data identifying a potential signaling network in chondrocytes; and 2) gene expression data identifying the transcriptional regulatory network underlying dendritic cell maturation. This method gives high estimated posterior probabilities to many of the proteins’ directed edges that are predicted by the literature; for the gene study, the method gives high posterior probabilities to many of the literature-predicted sibling edges. In simulations, the method gives substantially higher estimated posterior probabilities for true edges and true subnetworks than for their false counterparts. PMID:20855920

  12. 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.

  13. A robust and biologically plausible spike pattern recognition network.

    PubMed

    Larson, Eric; Perrone, Ben P; Sen, Kamal; Billimoria, Cyrus P

    2010-11-17

    The neural mechanisms that enable recognition of spiking patterns in the brain are currently unknown. This is especially relevant in sensory systems, in which the brain has to detect such patterns and recognize relevant stimuli by processing peripheral inputs; in particular, it is unclear how sensory systems can recognize time-varying stimuli by processing spiking activity. Because auditory stimuli are represented by time-varying fluctuations in frequency content, it is useful to consider how such stimuli can be recognized by neural processing. Previous models for sound recognition have used preprocessed or low-level auditory signals as input, but complex natural sounds such as speech are thought to be processed in auditory cortex, and brain regions involved in object recognition in general must deal with the natural variability present in spike trains. Thus, we used neural recordings to investigate how a spike pattern recognition system could deal with the intrinsic variability and diverse response properties of cortical spike trains. We propose a biologically plausible computational spike pattern recognition model that uses an excitatory chain of neurons to spatially preserve the temporal representation of the spike pattern. Using a single neural recording as input, the model can be trained using a spike-timing-dependent plasticity-based learning rule to recognize neural responses to 20 different bird songs with >98% accuracy and can be stimulated to evoke reverse spike pattern playback. Although we test spike train recognition performance in an auditory task, this model can be applied to recognize sufficiently reliable spike patterns from any neuronal system.

  14. The Google matrix controls the stability of structured ecological and biological networks

    PubMed Central

    Stone, Lewi

    2016-01-01

    May's celebrated theoretical work of the 70's contradicted the established paradigm by demonstrating that complexity leads to instability in biological systems. Here May's random-matrix modelling approach is generalized to realistic large-scale webs of species interactions, be they structured by networks of competition, mutualism or both. Simple relationships are found to govern these otherwise intractable models, and control the parameter ranges for which biological systems are stable and feasible. Our analysis of model and real empirical networks is only achievable on introducing a simplifying Google-matrix reduction scheme, which in the process, yields a practical ecological eigenvalue stability index. These results provide an insight into how network topology, especially connectance, influences species stable coexistence. Constraints controlling feasibility (positive equilibrium populations) in these systems are found more restrictive than those controlling stability, helping explain the enigma of why many classes of feasible ecological models are nearly always stable. PMID:27687986

  15. The Google matrix controls the stability of structured ecological and biological networks

    NASA Astrophysics Data System (ADS)

    Stone, Lewi

    2016-09-01

    May's celebrated theoretical work of the 70's contradicted the established paradigm by demonstrating that complexity leads to instability in biological systems. Here May's random-matrix modelling approach is generalized to realistic large-scale webs of species interactions, be they structured by networks of competition, mutualism or both. Simple relationships are found to govern these otherwise intractable models, and control the parameter ranges for which biological systems are stable and feasible. Our analysis of model and real empirical networks is only achievable on introducing a simplifying Google-matrix reduction scheme, which in the process, yields a practical ecological eigenvalue stability index. These results provide an insight into how network topology, especially connectance, influences species stable coexistence. Constraints controlling feasibility (positive equilibrium populations) in these systems are found more restrictive than those controlling stability, helping explain the enigma of why many classes of feasible ecological models are nearly always stable.

  16. The Google matrix controls the stability of structured ecological and biological networks.

    PubMed

    Stone, Lewi

    2016-09-30

    May's celebrated theoretical work of the 70's contradicted the established paradigm by demonstrating that complexity leads to instability in biological systems. Here May's random-matrix modelling approach is generalized to realistic large-scale webs of species interactions, be they structured by networks of competition, mutualism or both. Simple relationships are found to govern these otherwise intractable models, and control the parameter ranges for which biological systems are stable and feasible. Our analysis of model and real empirical networks is only achievable on introducing a simplifying Google-matrix reduction scheme, which in the process, yields a practical ecological eigenvalue stability index. These results provide an insight into how network topology, especially connectance, influences species stable coexistence. Constraints controlling feasibility (positive equilibrium populations) in these systems are found more restrictive than those controlling stability, helping explain the enigma of why many classes of feasible ecological models are nearly always stable.

  17. 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

  18. VANESA - a software application for the visualization and analysis of networks in system biology applications.

    PubMed

    Brinkrolf, Christoph; Janowski, Sebastian Jan; Kormeier, Benjamin; Lewinski, Martin; Hippe, Klaus; Borck, Daniela; Hofestädt, Ralf

    2014-06-23

    VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf.net.

  19. The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs

    PubMed Central

    Villa-Vialaneix, Nathalie; Liaubet, Laurence; Laurent, Thibault; Cherel, Pierre; Gamot, Adrien; SanCristobal, Magali

    2013-01-01

    What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology. PMID:23577081

  20. The structure of a gene co-expression network reveals biological functions underlying eQTLs.

    PubMed

    Villa-Vialaneix, Nathalie; Liaubet, Laurence; Laurent, Thibault; Cherel, Pierre; Gamot, Adrien; SanCristobal, Magali

    2013-01-01

    What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.

  1. Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

    SciTech Connect

    Eschrich, Steven; Zhang Hongling; Zhao Haiyan; Boulware, David; Lee, Ji-Hyun; Bloom, Gregory; Torres-Roca, Javier F.

    2009-10-01

    Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. Methods and Materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

  2. Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery

    PubMed Central

    Kumari, Sapna; Nie, Jeff; Chen, Huann-Sheng; Ma, Hao; Stewart, Ron; Li, Xiang; Lu, Meng-Zhu; Taylor, William M.; Wei, Hairong

    2012-01-01

    Background Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. Methods and Results In this study, we compared eight gene association methods – Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson – and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. Conclusions We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction. PMID:23226279

  3. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

    PubMed Central

    2016-01-01

    Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network—the XenoSite reactivity model—using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule

  4. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

    PubMed Central

    He, Fei; Murabito, Ettore; Westerhoff, Hans V.

    2016-01-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. PMID:27075000

  5. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

    PubMed

    He, Fei; Murabito, Ettore; Westerhoff, Hans V

    2016-04-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.

  6. Biology-Inspired Distributed Consensus in Massively-Deployed Sensor Networks

    NASA Technical Reports Server (NTRS)

    Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

    2005-01-01

    Promises of ubiquitous control of the physical environment by large-scale wireless sensor networks open avenues for new applications that are expected to redefine the way we live and work. Most of recent research has concentrated on developing techniques for performing relatively simple tasks in small-scale sensor networks assuming some form of centralized control. The main contribution of this work is to propose a new way of looking at large-scale sensor networks, motivated by lessons learned from the way biological ecosystems are organized. Indeed, we believe that techniques used in small-scale sensor networks are not likely to scale to large networks; that such large-scale networks must be viewed as an ecosystem in which the sensors/effectors are organisms whose autonomous actions, based on local information, combine in a communal way to produce global results. As an example of a useful function, we demonstrate that fully distributed consensus can be attained in a scalable fashion in massively deployed sensor networks where individual motes operate based on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects.

  7. Biology-Inspired Distributed Consensus in Massively-Deployed Sensor Networks

    NASA Technical Reports Server (NTRS)

    Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

    2005-01-01

    Promises of ubiquitous control of the physical environment by large-scale wireless sensor networks open avenues for new applications that are expected to redefine the way we live and work. Most of recent research has concentrated on developing techniques for performing relatively simple tasks in small-scale sensor networks assuming some form of centralized control. The main contribution of this work is to propose a new way of looking at large-scale sensor networks, motivated by lessons learned from the way biological ecosystems are organized. Indeed, we believe that techniques used in small-scale sensor networks are not likely to scale to large networks; that such large-scale networks must be viewed as an ecosystem in which the sensors/effectors are organisms whose autonomous actions, based on local information, combine in a communal way to produce global results. As an example of a useful function, we demonstrate that fully distributed consensus can be attained in a scalable fashion in massively deployed sensor networks where individual motes operate based on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects.

  8. An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks.

    PubMed

    He, Jieyue; Wang, Chunyan; Qiu, Kunpu; Zhong, Wei

    2014-01-01

    Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. The algorithm of probability graph isomorphism evaluation based on circuit simulation

  9. An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks

    PubMed Central

    2014-01-01

    Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism

  10. MODA: an efficient algorithm for network motif discovery in biological networks.

    PubMed

    Omidi, Saeed; Schreiber, Falk; Masoudi-Nejad, Ali

    2009-10-01

    In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/

  11. Networks In Real Space: Characteristics and Analysis for Biology and Mechanics

    NASA Astrophysics Data System (ADS)

    Modes, Carl; Magnasco, Marcelo; Katifori, Eleni

    Functional networks embedded in physical space play a crucial role in countless biological and physical systems, from the efficient dissemination of oxygen, blood sugars, and hormonal signals in vascular systems to the complex relaying of informational signals in the brain to the distribution of stress and strain in architecture or static sand piles. Unlike their more-studied abstract cousins, such as the hyperlinked internet, social networks, or economic and financial connections, these networks are both constrained by and intimately connected to the physicality of their real, embedding space. We report on the results of new computational and analytic approaches tailored to these physical networks with particular implications and insights for mammalian organ vasculature.

  12. A review of active learning approaches to experimental design for uncovering biological networks

    PubMed Central

    2017-01-01

    Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

  13. Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

    PubMed Central

    Blatti, Charles; Sinha, Saurabh

    2016-01-01

    Motivation: Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or ‘properties’ such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene–gene or gene–property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. Results: We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. Availability and Implementation: DRaWR was implemented as

  14. Metabolome 2.0: quantitative genetics and network biology of metabolic phenotypes.

    PubMed

    Dumas, Marc-Emmanuel

    2012-10-01

    The characterization of the metabolome has rapidly evolved over two decades, from early developments in analytical chemistry to systems biology. Metabolites and small molecules are not independent; they are organized in biochemical pathways and in a wider metabolic network, which is itself dependent on various genetic and signaling networks for its regulation. Recent advances in genomics, transcriptomics, proteomics and metabolomics have been matched by the development of publicly available repositories, which have helped shaping a new generation of integrative studies using metabolite measurements in molecular epidemiology and genetic studies. Although the environment influences metabolism, the identification of the genetic determinants of metabolic phenotypes (metabotypes) was made possible by the development of metabotype quantitative trait locus (mQTL) mapping and metabolomic genome-wide association studies (mGWAS) in a rigorous statistical genetics framework, deriving associations between metabolite concentrations and genetic polymorphisms. However, given the complexity of the biomolecular events involved in the regulation of metabolic patterns, alternative network biology approaches have also been recently introduced, such as integrated metabolome and interactome mapping (iMIM). This unprecedented convergence of metabolic biochemistry, quantitative genetics and network biology already has had a strong impact on the role of the metabolome in biomedical sciences, and this review gives a foretaste of its anticipated successes in eventually delivering personalized medicine.

  15. Statistical method for revealing form-function relations in biological networks

    PubMed Central

    Mugler, Andrew; Grinshpun, Boris; Franks, Riley

    2011-01-01

    Over the past decade, a number of researchers in systems biology have sought to relate the function of biological systems to their network-level descriptions—lists of the most important players and the pairwise interactions between them. Both for large networks (in which statistical analysis is often framed in terms of the abundance of repeated small subgraphs) and for small networks which can be analyzed in greater detail (or even synthesized in vivo and subjected to experiment), revealing the relationship between the topology of small subgraphs and their biological function has been a central goal. We here seek to pose this revelation as a statistical task, illustrated using a particular setup which has been constructed experimentally and for which parameterized models of transcriptional regulation have been studied extensively. The question “how does function follow form” is here mathematized by identifying which topological attributes correlate with the diverse possible information-processing tasks which a transcriptional regulatory network can realize. The resulting method reveals one form-function relationship which had earlier been predicted based on analytic results, and reveals a second for which we can provide an analytic interpretation. Resulting source code is distributed via http://formfunction.sourceforge.net. PMID:21183719

  16. Maria Goeppert-Mayer Award Talk: Probing the structure and dynamics of biological networks

    NASA Astrophysics Data System (ADS)

    Albert, Reka

    2011-03-01

    The relationship between the structure and dynamics of networks is one of the central topics in network science. In the context of biological regulatory networks at the molecular to cellular level, the dynamics in question is often thought of as information propagation through the network. Quantitative dynamic models help to achieve an understanding of this process, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting biological signal transduction, and at the same time probing the structure-function relation of these networks. This analysis, however, necessitates the extension of current graph theoretical frameworks to incorporate features such as the positive or negative nature of interactions and synergistic behaviors among multiple components. This talk will present a method for structural analysis in an augmented graph framework that can probe the dynamics of information transfer. The first step is to expand the network to a richer representation that incorporates negative and synergistic regulation by the addition of pseudo-nodes and new edges. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. As a first application of this method we ranked the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on various regulatory networks shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and agrees with dynamic simulation results and experimental observations. Future applications include determining the ESMs that (do

  17. Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

    PubMed Central

    Waltemath, Dagmar; Bergmann, Frank T.; Chaouiya, Claudine; Czauderna, Tobias; Gleeson, Padraig; Goble, Carole; Golebiewski, Martin; Hucka, Michael; Juty, Nick; Krebs, Olga; Le Novère, Nicolas; Mi, Huaiyu; Moraru, Ion I.; Myers, Chris J.; Nickerson, David; Olivier, Brett G.; Rodriguez, Nicolas; Schreiber, Falk; Smith, Lucian; Zhang, Fengkai; Bonnet, Eric

    2014-01-01

    The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology.

  18. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.

    PubMed

    Yan, Jingwen; Risacher, Shannon L; Shen, Li; Saykin, Andrew J

    2017-06-30

    In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  19. Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

    NASA Astrophysics Data System (ADS)

    Piñero, Janet; Berenstein, Ariel; Gonzalez-Perez, Abel; Chernomoretz, Ariel; Furlong, Laura I.

    2016-04-01

    Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.

  20. Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

    PubMed Central

    Piñero, Janet; Berenstein, Ariel; Gonzalez-Perez, Abel; Chernomoretz, Ariel; Furlong, Laura I.

    2016-01-01

    Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules. PMID:27080396

  1. FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks

    PubMed Central

    Ding, Ying; Fang, Zhou; Sun, Zhe; MacDonald, Matthew L.; Sweet, Robert A.; Wang, Jieru; Chen, Wei

    2016-01-01

    Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer’s disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named “FastGGM”. PMID:26872036

  2. Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks

    PubMed Central

    Han, Hyun Wook; Ohn, Jung Hun; Moon, Jisook; Kim, Ju Han

    2013-01-01

    Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein–protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P < 8 × 10−12, P < 2 × 10−4) and a high codon adaptation index (P < 2 × 10−16, P < 2 × 10−8), indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease. PMID:23935122

  3. Task-Oriented Modular Decomposition of Biological Networks: Trigger Mechanism in Blood Coagulation

    PubMed Central

    Panteleev, Mikhail A.; Balandina, Anna N.; Lipets, Elena N.; Ovanesov, Mikhail V.; Ataullakhanov, Fazoil I.

    2010-01-01

    Abstract Analysis of complex time-dependent biological networks is an important challenge in the current postgenomic era. We propose a middle-out approach for decomposition and analysis of complex time-dependent biological networks based on: 1), creation of a detailed mechanism-driven mathematical model of the network; 2), network response decomposition into several physiologically relevant subtasks; and 3), subsequent decomposition of the model, with the help of task-oriented necessity and sensitivity analysis into several modules that each control a single specific subtask, which is followed by further simplification employing temporal hierarchy reduction. The technique is tested and illustrated by studying blood coagulation. Five subtasks (threshold, triggering, control by blood flow velocity, spatial propagation, and localization), together with responsible modules, can be identified for the coagulation network. We show that the task of coagulation triggering is completely regulated by a two-step pathway containing a single positive feedback of factor V activation by thrombin. These theoretical predictions are experimentally confirmed by studies of fibrin generation in normal, factor V-, and factor VIII-deficient plasmas. The function of the factor V-dependent feedback is to minimize temporal and parametrical intervals of fibrin clot instability. We speculate that this pathway serves to lessen possibility of fibrin clot disruption by flow and subsequent thromboembolism. PMID:20441738

  4. Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.

    PubMed

    Han, Hyun Wook; Ohn, Jung Hun; Moon, Jisook; Kim, Ju Han

    2013-11-01

    Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P < 8 × 10(-12), P < 2 × 10(-4)) and a high codon adaptation index (P < 2 × 10(-16), P < 2 × 10(-8)), indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.

  5. Toward integration of systems biology formalism: the gene regulatory networks case.

    PubMed

    Gentilini, Raffaella

    2005-01-01

    We consider the problem of integrating different systems biology formalisms, namely, the process calculi based formalism, the modeling approach based on systems of differential equations, and the one relying on automata-like descriptions (and model checking). Specifically, we define automatic procedures for translating stochastic pi-calculus descriptions of gene regulatory networks to S-systems differential equations. Tools for extracting and reasoning on (approximate) solutions of S-systems have been recently developed in the literature, and can be exploited to establish a link with automata-based systems biology and model checking techniques.

  6. Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks

    PubMed Central

    Audouze, Karine; Juncker, Agnieszka Sierakowska; Roque, Francisco J. S. S. A.; Krysiak-Baltyn, Konrad; Weinhold, Nils; Taboureau, Olivier; Jensen, Thomas Skøt; Brunak, Søren

    2010-01-01

    Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential associations between chemicals and proteins. Examples are shown for chemicals associated with breast cancer, lung cancer and necrosis, and potential protein targets for di-ethylhexyl-phthalate, 2,3,7,8-tetrachlorodibenzo-p-dioxin, pirinixic acid and permethrine. The chemical-protein associations are supported through recent published studies, which illustrate the power of our approach that integrates toxicogenomics data with other data types. PMID:20502671

  7. Visual Analysis of Transcriptome Data in the Context of Anatomical Structures and Biological Networks

    PubMed Central

    Junker, Astrid; Rohn, Hendrik; Schreiber, Falk

    2012-01-01

    The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks. PMID:23162564

  8. PiNGO: a Cytoscape plugin to find candidate genes in biological networks.

    PubMed

    Smoot, Michael; Ono, Keiichiro; Ideker, Trey; Maere, Steven

    2011-04-01

    PiNGO is a tool to screen biological networks for candidate genes, i.e. genes predicted to be involved in a biological process of interest. The user can narrow the search to genes with particular known functions or exclude genes belonging to particular functional classes. PiNGO provides support for a wide range of organisms and Gene Ontology classification schemes, and it can easily be customized for other organisms and functional classifications. PiNGO is implemented as a plugin for Cytoscape, a popular network visualization platform. PiNGO is distributed as an open-source Java package under the GNU General Public License (http://www.gnu.org/), and can be downloaded via the Cytoscape plugin manager. A detailed user guide and tutorial are available on the PiNGO website (http://www.psb.ugent.be/esb/PiNGO.

  9. 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.

  10. Adaptive diffusion kernel learning from biological networks for protein function prediction

    PubMed Central

    Sun, Liang; Ji, Shuiwang; Ye, Jieping

    2008-01-01

    Background Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods are suitable for learning from graph-based data such as biological networks, as they only require the abstraction of the similarities between objects into the kernel matrix. One key issue in kernel methods is the selection of a good kernel function. Diffusion kernels, the discretization of the familiar Gaussian kernel of Euclidean space, are commonly used for graph-based data. Results In this paper, we address the issue of learning an optimal diffusion kernel, in the form of a convex combination of a set of pre-specified kernels constructed from biological networks, for protein function prediction. Most prior work on this kernel learning task focus on variants of the loss function based on Support Vector Machines (SVM). Their extensions to other loss functions such as the one based on Kullback-Leibler (KL) divergence, which is more suitable for mining biological networks, lead to expensive optimization problems. By exploiting the special structure of the diffusion kernel, we show that this KL divergence based kernel learning problem can be formulated as a simple optimization problem, which can then be solved efficiently. It is further extended to the multi-task case where we predict multiple functions of a protein simultaneously. We evaluate the efficiency and effectiveness of the proposed algorithms using two benchmark data sets. Conclusion Results show that the performance of linearly combined diffusion kernel is better than every single candidate diffusion kernel. When the number of tasks is large, the algorithms based on multiple tasks are favored due to their competitive recognition performance and small computational costs. PMID:18366736

  11. From systems biology to photosynthesis and whole-plant modeling: a conceptual model for integrating multi-scale networks

    SciTech Connect

    Weston, David; Hanson, Paul J; Norby, Richard J; Tuskan, Gerald A; Wullschleger, Stan D

    2012-01-01

    Network analysis is now a common statistical tool for molecular biologists. Network algorithms are readily used to model gene, protein and metabolic correlations providing insight into pathways driving biological phenomenon. One output from such an analysis is a candidate gene list that can be responsible, in part, for the biological process of interest. The question remains, however, as to whether molecular network analysis can be used to inform process models at higher levels of biological organization. In our previous work, transcriptional networks derived from three plant species were constructed, interrogated for orthology and then correlated to photosynthetic inhibition at elevated temperature. One unique aspect of that study was the link from co-expression networks to net photosynthesis. In this addendum, we propose a conceptual model where traditional network analysis can be linked to whole-plant models thereby informing predictions on key processes such as photosynthesis, nutrient uptake and assimilation, and C partitioning.

  12. From systems biology to photosynthesis and whole-plant physiology: a conceptual model for integrating multi-scale networks.

    PubMed

    Weston, David J; Hanson, Paul J; Norby, Richard J; Tuskan, Gerald A; Wullschleger, Stan D

    2012-02-01

    Network analysis is now a common statistical tool for molecular biologists. Network algorithms are readily used to model gene, protein and metabolic correlations providing insight into pathways driving biological phenomenon. One output from such an analysis is a candidate gene list that can be responsible, in part, for the biological process of interest. The question remains, however, as to whether molecular network analysis can be used to inform process models at higher levels of biological organization. In our previous work, transcriptional networks derived from three plant species were constructed, interrogated for orthology and then correlated with photosynthetic inhibition at elevated temperature. One unique aspect of that study was the link from co-expression networks to net photosynthesis. In this addendum, we propose a conceptual model where traditional network analysis can be linked to whole-plant models thereby informing predictions on key processes such as photosynthesis, nutrient uptake and assimilation, and C partitioning.

  13. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks

    PubMed Central

    Wiencko, Heather L.; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J.

    2016-01-01

    Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat). PMID:27853512

  14. Elastic-Net Copula Granger Causality for Inference of Biological Networks

    PubMed Central

    Siyal, Mohammad Yakoob

    2016-01-01

    Aim In bioinformatics, the inference of biological networks is one of the most active research areas. It involves decoding various complex biological networks that are responsible for performing diverse functions in human body. Among these networks analysis, most of the research focus is towards understanding effective brain connectivity and gene networks in order to cure and prevent related diseases like Alzheimer and cancer respectively. However, with recent advances in data procurement technology, such as DNA microarray analysis and fMRI that can simultaneously process a large amount of data, it yields high-dimensional data sets. These high dimensional dataset analyses possess challenges for the analyst. Background Traditional methods of Granger causality inference use ordinary least-squares methods for structure estimation, which confront dimensionality issues when applied to high-dimensional data. Apart from dimensionality issues, most existing methods were designed to capture only the linear inferences from time series data. Method and Conclusion In this paper, we address the issues involved in assessing Granger causality for both linear and nonlinear high-dimensional data by proposing an elegant form of the existing LASSO-based method that we call “Elastic-Net Copula Granger causality”. This method provides a more stable way to infer biological networks which has been verified using rigorous experimentation. We have compared the proposed method with the existing method and demonstrated that this new strategy outperforms the existing method on all measures: precision, false detection rate, recall, and F1 score. We have also applied both methods to real HeLa cell data and StarPlus fMRI datasets and presented a comparison of the effectiveness of both methods. PMID:27792750

  15. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.

    PubMed

    Muetze, Tanja; Goenawan, Ivan H; Wiencko, Heather L; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J

    2016-01-01

    Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest.

  16. A multilevel gamma-clustering layout algorithm for visualization of biological networks.

    PubMed

    Hruz, Tomas; Wyss, Markus; Lucas, Christoph; Laule, Oliver; von Rohr, Peter; Zimmermann, Philip; Bleuler, Stefan

    2013-01-01

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

  17. Biologically Derived Soft Conducting Hydrogels Using Heparin-Doped Polymer Networks

    PubMed Central

    2015-01-01

    The emergence of flexible and stretchable electronic components expands the range of applications of electronic devices. Flexible devices are ideally suited for electronic biointerfaces because of mechanically permissive structures that conform to curvilinear structures found in native tissue. Most electronic materials used in these applications exhibit elastic moduli on the order of 0.1–1 MPa. However, many electronically excitable tissues exhibit elasticities in the range of 1–10 kPa, several orders of magnitude smaller than existing components used in flexible devices. This work describes the use of biologically derived heparins as scaffold materials for fabricating networks with hybrid electronic/ionic conductivity and ultracompliant mechanical properties. Photo-cross-linkable heparin–methacrylate hydrogels serve as templates to control the microstructure and doping of in situ polymerized polyaniline structures. Macroscopic heparin-doped polyaniline hydrogel dual networks exhibit impedances as low as Z = 4.17 Ω at 1 kHz and storage moduli of G′ = 900 ± 100 Pa. The conductivity of heparin/polyaniline networks depends on the oxidation state and microstructure of secondary polyaniline networks. Furthermore, heparin/polyaniline networks support the attachment, proliferation, and differentiation of murine myoblasts without any surface treatments. Taken together, these results suggest that heparin/polyaniline hydrogel networks exhibit suitable physical properties as an electronically active biointerface material that can match the mechanical properties of soft tissues composed of excitable cells. PMID:24738911

  18. Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

    PubMed

    Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

    2017-01-01

    Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

  19. ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network.

    PubMed

    Wang, Jianxin; Zhong, Jiancheng; Chen, Gang; Li, Min; Wu, Fang-xiang; Pan, Yi

    2015-01-01

    Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.

  20. EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks.

    PubMed

    Sambaturu, Narmada; Mishra, Madhulika; Chandra, Nagasuma

    2016-08-18

    In biological systems, diseases are caused by small perturbations in a complex network of interactions between proteins. Perturbations typically affect only a small number of proteins, which go on to disturb a larger part of the network. To counteract this, a stress-response is launched, resulting in a complex pattern of variations in the cell. Identifying the key players involved in either spreading the perturbation or responding to it can give us important insights. We develop an algorithm, EpiTracer, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out. We propose a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network by identifying highest activity paths specific to the condition of interest, obtained by mapping gene expression profiles to the PPI network. We demonstrate the algorithm using an overexpression study and a knockdown study. In the overexpression study, the gene that was overexpressed (PARK2) was highlighted as the most important epicenter specific to the perturbation. The other top-ranked epicenters were involved in either supporting the activity of PARK2, or counteracting it. Also, 5 of the identified epicenters showed no significant differential expression, showing that our method can find information which simple differential expression analysis cannot. In the second dataset (SP1 knockdown), alternative regulators of SP1 targets were highlighted as epicenters. Also, the gene that was knocked down (SP1) was picked up as an epicenter specific to the control condition. Sensitivity analysis showed that the genes identified as epicenters remain largely unaffected by small changes. We develop an algorithm, EpiTracer, to find epicenters in condition-specific biological networks, given the PPI network and gene expression levels. EpiTracer includes programs which can extract the

  1. Singular structure of Mueller matrices images of biological crystal networks for diagnostic human tissues pathological changes

    NASA Astrophysics Data System (ADS)

    Sakhnovskiy, M. Y.; Ushenko, V. A.

    2013-09-01

    The process of converting of laser radiation by optically anisotropic crystals of biological networks are singular in the sense of total (simultaneous) of mechanisms of orientation and phase (birefringence) anisotropy the formation of polarization-inhomogeneous field of scattered radiation. This work is aimed at developing a method of polarization selection mechanisms of blood plasma polycrystalline networks anisotropy. The relationship between statistics, correlation and fractal parameters of polarization-inhomogeneous images of blood plasma and by linear dichroism and linear birefringence of polycrystalline networks albumin and globulin was found. The criteria of differentiation and diagnostic images of polarization-inhomogeneous plasma samples of the control group (donor) and a group of patients with malignant changes of breast tissue was identified.

  2. Net2Align: An Algorithm For Pairwise Global Alignment of Biological Networks

    PubMed Central

    Wadhwab, Gulshan; Upadhyayaa, K. C.

    2016-01-01

    The amount of data on molecular interactions is growing at an enormous pace, whereas the progress of methods for analysing this data is still lacking behind. Particularly, in the area of comparative analysis of biological networks, where one wishes to explore the similarity between two biological networks, this holds a potential problem. In consideration that the functionality primarily runs at the network level, it advocates the need for robust comparison methods. In this paper, we describe Net2Align, an algorithm for pairwise global alignment that can perform node-to-node correspondences as well as edge-to-edge correspondences into consideration. The uniqueness of our algorithm is in the fact that it is also able to detect the type of interaction, which is essential in case of directed graphs. The existing algorithm is only able to identify the common nodes but not the common edges. Another striking feature of the algorithm is that it is able to remove duplicate entries in case of variable datasets being aligned. This is achieved through creation of a local database which helps exclude duplicate links. In a pervasive computational study on gene regulatory network, we establish that our algorithm surpasses its counterparts in its results. Net2Align has been implemented in Java 7 and the source code is available as supplementary files. PMID:28356678

  3. Competing endogenous RNA networks: tying the essential knots for cancer biology and therapeutics.

    PubMed

    Sanchez-Mejias, Avencia; Tay, Yvonne

    2015-03-28

    A recently discovered dimension of post-transcriptional gene regulation involves co-regulatory crosstalk between RNA transcripts, which compete for common pools of microRNA (miRNA) molecules. These competing endogenous RNAs (ceRNAs), or natural miRNA sponges, have an active role in regulating miRNA availability within the cell and form intertwined regulatory networks. Recent reports have implicated diverse RNA species including protein-coding messenger RNAs and non-coding RNAs as ceRNAs in human development and diseases including human cancer. In this review, we discuss the most recent discoveries that implicate natural miRNA decoys in human cancer biology, as well as exciting advances in the study of ceRNA networks and dynamics. The structure and topology of intricate genome-scale ceRNA networks can be predicted computationally, and their dynamic response to fluctuations in ceRNA and miRNA levels can be studied via mathematical modeling. Additionally, the development of new methods to quantitatively determine absolute expression levels of miRNA and ceRNA molecules have expanded the capacity to accurately study the efficiency of ceRNA crosstalk in diverse biological models. These major milestones are of critical importance to identify key components of ceRNA regulatory networks that could aid the development of new approaches to cancer diagnostics and oligonucleotide-based therapeutics.

  4. A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation

    PubMed Central

    Ogris, Christoph; Guala, Dimitri; Helleday, Thomas; Sonnhammer, Erik L. L.

    2017-01-01

    Analyzing gene expression patterns is a mainstay to gain functional insights of biological systems. A plethora of tools exist to identify significant enrichment of pathways for a set of differentially expressed genes. Most tools analyze gene overlap between gene sets and are therefore severely hampered by the current state of pathway annotation, yet at the same time they run a high risk of false assignments. A way to improve both true positive and false positive rates (FPRs) is to use a functional association network and instead look for enrichment of network connections between gene sets. We present a new network crosstalk analysis method BinoX that determines the statistical significance of network link enrichment or depletion between gene sets, using the binomial distribution. This is a much more appropriate statistical model than previous methods have employed, and as a result BinoX yields substantially better true positive and FPRs than was possible before. A number of benchmarks were performed to assess the accuracy of BinoX and competing methods. We demonstrate examples of how BinoX finds many biologically meaningful pathway annotations for gene sets from cancer and other diseases, which are not found by other methods. BinoX is available at http://sonnhammer.org/BinoX. PMID:27664219

  5. Process-Driven Inference of Biological Network Structure: Feasibility, Minimality, and Multiplicity

    PubMed Central

    Wang, Guanyu; Rong, Yongwu; Chen, Hao; Pearson, Carl; Du, Chenghang; Simha, Rahul; Zeng, Chen

    2012-01-01

    A common problem in molecular biology is to use experimental data, such as microarray data, to infer knowledge about the structure of interactions between important molecules in subsystems of the cell. By approximating the state of each molecule as “on” or “off”, it becomes possible to simplify the problem, and exploit the tools of Boolean analysis for such inference. Amongst Boolean techniques, the process-driven approach has shown promise in being able to identify putative network structures, as well as stability and modularity properties. This paper examines the process-driven approach more formally, and makes four contributions about the computational complexity of the inference problem, under the “dominant inhibition” assumption of molecular interactions. The first is a proof that the feasibility problem (does there exist a network that explains the data?) can be solved in polynomial-time. Second, the minimality problem (what is the smallest network that explains the data?) is shown to be NP-hard, and therefore unlikely to result in a polynomial-time algorithm. Third, a simple polynomial-time heuristic is shown to produce near-minimal solutions, as demonstrated by simulation. Fourth, the theoretical framework explains how multiplicity (the number of network solutions to realize a given biological process), which can take exponential-time to compute, can instead be accurately estimated by a fast, polynomial-time heuristic. PMID:22815739

  6. The feasibility of genome-scale biological network inference using Graphics Processing Units.

    PubMed

    Thiagarajan, Raghuram; Alavi, Amir; Podichetty, Jagdeep T; Bazil, Jason N; Beard, Daniel A

    2017-01-01

    Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called 'big data' applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster.

  7. An end-to-end workflow for engineering of biological networks from high-level specifications.

    PubMed

    Beal, Jacob; Weiss, Ron; Densmore, Douglas; Adler, Aaron; Appleton, Evan; Babb, Jonathan; Bhatia, Swapnil; Davidsohn, Noah; Haddock, Traci; Loyall, Joseph; Schantz, Richard; Vasilev, Viktor; Yaman, Fusun

    2012-08-17

    We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflow's modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.

  8. Double network bacterial cellulose hydrogel to build a biology-device interface.

    PubMed

    Shi, Zhijun; Li, Ying; Chen, Xiuli; Han, Hongwei; Yang, Guang

    2014-01-21

    Establishing a biology-device interface might enable the interaction between microelectronics and biotechnology. In this study, electroactive hydrogels have been produced using bacterial cellulose (BC) and conducting polymer (CP) deposited on the BC hydrogel surface to cover the BC fibers. The structures of these composites thus have double networks, one of which is a layer of electroactive hydrogels combined with BC and CP. The electroconductivity provides the composites with capabilities for voltage and current response, and the BC hydrogel layer provides good biocompatibility, biodegradability, bioadhesion and mass transport properties. Such a system might allow selective biological functions such as molecular recognition and specific catalysis and also for probing the detailed genetic and molecular mechanisms of life. A BC-CP composite hydrogel could then lead to a biology-device interface. Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) are used here to study the composite hydrogels' electroactive property. BC-PAni and BC-PPy respond to voltage changes. This provides a mechanism to amplify electrochemical signals for analysis or detection. BC hydrogels were found to be able to support the growth, spreading and migration of human normal skin fibroblasts without causing any cytotoxic effect on the cells in the cell culture. These double network BC-CP hydrogels are biphasic Janus hydrogels which integrate electroactivity with biocompatibility, and might provide a biology-device interface to produce implantable devices for personalized and regenerative medicine.

  9. Double network bacterial cellulose hydrogel to build a biology-device interface

    NASA Astrophysics Data System (ADS)

    Shi, Zhijun; Li, Ying; Chen, Xiuli; Han, Hongwei; Yang, Guang

    2013-12-01

    Establishing a biology-device interface might enable the interaction between microelectronics and biotechnology. In this study, electroactive hydrogels have been produced using bacterial cellulose (BC) and conducting polymer (CP) deposited on the BC hydrogel surface to cover the BC fibers. The structures of these composites thus have double networks, one of which is a layer of electroactive hydrogels combined with BC and CP. The electroconductivity provides the composites with capabilities for voltage and current response, and the BC hydrogel layer provides good biocompatibility, biodegradability, bioadhesion and mass transport properties. Such a system might allow selective biological functions such as molecular recognition and specific catalysis and also for probing the detailed genetic and molecular mechanisms of life. A BC-CP composite hydrogel could then lead to a biology-device interface. Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) are used here to study the composite hydrogels' electroactive property. BC-PAni and BC-PPy respond to voltage changes. This provides a mechanism to amplify electrochemical signals for analysis or detection. BC hydrogels were found to be able to support the growth, spreading and migration of human normal skin fibroblasts without causing any cytotoxic effect on the cells in the cell culture. These double network BC-CP hydrogels are biphasic Janus hydrogels which integrate electroactivity with biocompatibility, and might provide a biology-device interface to produce implantable devices for personalized and regenerative medicine.

  10. Vision-based force measurement using neural networks for biological cell microinjection.

    PubMed

    Karimirad, Fatemeh; Chauhan, Sunita; Shirinzadeh, Bijan

    2014-03-21

    This paper presents a vision-based force measurement method using an artificial neural network model. The proposed model is used for measuring the applied load to a spherical biological cell during micromanipulation process. The devised vision-based method is most useful when force measurement capability is required, but it is very challenging or even infeasible to use a force sensor. Artificial neural networks in conjunction with image processing techniques have been used to estimate the applied load to a cell. A bio-micromanipulation system capable of force measurement has also been established in order to collect the training data required for the proposed neural network model. The geometric characterization of zebrafish embryos membranes has been performed during the penetration of the micropipette prior to piercing. The geometric features are extracted from images using image processing techniques. These features have been used to describe the shape and quantify the deformation of the cell at different indentation depths. The neural network is trained by taking the visual data as the input and the measured corresponding force as the output. Once the neural network is trained with sufficient number of data, it can be used as a precise sensor in bio-micromanipulation setups. However, the proposed neural network model is applicable for indentation of any other spherical elastic object. The results demonstrate the capability of the proposed method. The outcomes of this study could be useful for measuring force in biological cell micromanipulation processes such as injection of the mouse oocyte/embryo. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. The need to connect: on the cell biology of synapses, behaviors, and networks in science

    PubMed Central

    Colón-Ramos, Daniel A.

    2016-01-01

    My laboratory is interested in the cell biology of the synapse. Synapses, which are points of cellular communication between neurons, were first described by Santiago Ramón y Cajal as “protoplasmic kisses that appear to constitute the final ecstasy of an epic love story.” Who would not want to work on that?! My lab examines the biological mechanisms neurons use to find and connect to each other. How are synapses formed during development, maintained during growth, and modified during learning? In this essay, I reflect about my scientific journey to the synapse, the cell biological one, but also a metaphorical synapse—my role as a point of contact between the production of knowledge and its dissemination. In particular, I discuss how the architecture of scientific networks propels knowledge production but can also exclude certain groups in science. PMID:27799494

  12. Landauer in the Age of Synthetic Biology: Energy Consumption and Information Processing in Biochemical Networks

    NASA Astrophysics Data System (ADS)

    Mehta, Pankaj; Lang, Alex H.; Schwab, David J.

    2016-03-01

    A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using "post-translational" synthetic biology that exploits reversible protein modification.

  13. Network news: prime time for systems biology of the plant circadian clock.

    PubMed

    McClung, C Robertson; Gutiérrez, Rodrigo A

    2010-12-01

    Whole-transcriptome analyses have established that the plant circadian clock regulates virtually every plant biological process and most prominently hormonal and stress response pathways. Systems biology efforts have successfully modeled the plant central clock machinery and an iterative process of model refinement and experimental validation has contributed significantly to the current view of the central clock machinery. The challenge now is to connect this central clock to the output pathways for understanding how the plant circadian clock contributes to plant growth and fitness in a changing environment. Undoubtedly, systems approaches will be needed to integrate and model the vastly increased volume of experimental data in order to extract meaningful biological information. Thus, we have entered an era of systems modeling, experimental testing, and refinement. This approach, coupled with advances from the genetic and biochemical analyses of clock function, is accelerating our progress towards a comprehensive understanding of the plant circadian clock network. Copyright © 2010 Elsevier Ltd. All rights reserved.

  14. Coordinated motor activity in simulated spinal networks emerges from simple biologically plausible rules of connectivity.

    PubMed

    Dale, Nicholas

    2003-01-01

    The spinal motor circuits of the Xenopus embryo have been simulated in a 400-neuron network. To explore the consequences of differing patterns of synaptic connectivity within the network for the generation of the motor rhythm, a system of biologically plausible rules was devised to control synapse formation by three parameters. Each neuron had an intrinsic probability of synapse formation (P(soma), specified by a space constant lambda) that was a monotonically decreasing function of its soma location in the rostro-caudal axis of the simulated network. The neurons had rostral and caudal going axons of specified length (L(axon)) associated with a probability of synapse formation (P(axon)). The final probability of synapse formation was the product of P(soma) and P(axon). Realistic coordinated activity only occurred when L(axon) and the probabilities of interconnection were sufficiently high. Increasing the values of the three network parameters reduced the burst duration, cycle period, and rostro-caudal delay and increased the reliability with which the network functioned as measured by the coefficient of variance of these parameters. Whereas both L(axon) and P(axon) had powerful and consistent effects on network output, the effects of lambda on burst duration and rostro-caudal delay were more variable and depended on the values of the other two parameters. This network model can reproduce the rostro-caudal coordination of swimming without using coupled oscillator theory. The changes in network connectivity and resulting changes in activity explored by the model mimic the development of the motor pattern for swimming in the real embryo.

  15. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    PubMed

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  16. Content-rich biological network constructed by mining PubMed abstracts

    PubMed Central

    Chen, Hao; Sharp, Burt M

    2004-01-01

    Background The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public. Results We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. Conclusions Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot can be accessed free of charge to academic users. PMID:15473905

  17. Temporal Expression-based Analysis of Metabolism

    PubMed Central

    Segrè, Daniel

    2012-01-01

    Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques. PMID:23209390

  18. A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability.

    PubMed

    De León, Héctor; Boué, Stéphanie; Schlage, Walter K; Boukharov, Natalia; Westra, Jurjen W; Gebel, Stephan; VanHooser, Aaron; Talikka, Marja; Fields, R Brett; Veljkovic, Emilija; Peck, Michael J; Mathis, Carole; Hoang, Vy; Poussin, Carine; Deehan, Renee; Stolle, Katrin; Hoeng, Julia; Peitsch, Manuel C

    2014-06-26

    Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of "omics" data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting. We constructed and evaluated the Vascular Inflammatory Processes Network (V-IPN), a model representing a collection of vascular processes modulated by inflammatory stimuli that lead to the development of atherosclerosis. Utilizing the V-IPN as a platform for biological discovery, we have identified key vascular processes and mechanisms captured by gene expression profiling data from four independent datasets from human endothelial cells (ECs) and human and murine intact vessels. Primary ECs in culture from multiple donors revealed a richer mapping of mechanisms identified by the V-IPN compared to an immortalized EC line. Furthermore, an evaluation of gene expression datasets from aortas of old ApoE-/- mice (78 weeks) and human coronary arteries with advanced atherosclerotic lesions identified significant commonalities in the two species, as well as several mechanisms specific to human arteries that are consistent with the development of unstable atherosclerotic plaques. We have generated a new biological network model of atherogenic processes that demonstrates the power of network analysis to advance integrative, systems biology-based knowledge of cross-species translatability, plaque development and potential mechanisms leading to plaque instability.

  19. Biological modelling of a computational spiking neural network with neuronal avalanches

    NASA Astrophysics Data System (ADS)

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-05-01

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

  20. LGL: creating a map of protein function with an algorithm for visualizing very large biological networks.

    PubMed

    Adai, Alex T; Date, Shailesh V; Wieland, Shannon; Marcotte, Edward M

    2004-06-25

    Networks are proving to be central to the study of gene function, protein-protein interaction, and biochemical pathway data. Visualization of networks is important for their study, but visualization tools are often inadequate for working with very large biological networks. Here, we present an algorithm, called large graph layout (LGL), which can be used to dynamically visualize large networks on the order of hundreds of thousands of vertices and millions of edges. LGL applies a force-directed iterative layout guided by a minimal spanning tree of the network in order to generate coordinates for the vertices in two or three dimensions, which are subsequently visualized and interactively navigated with companion programs. We demonstrate the use of LGL in visualizing an extensive protein map summarizing the results of approximately 21 billion sequence comparisons between 145579 proteins from 50 genomes. Proteins are positioned in the map according to sequence homology and gene fusions, with the map ultimately serving as a theoretical framework that integrates inferences about gene function derived from sequence homology, remote homology, gene fusions, and higher-order fusions. We confirm that protein neighbors in the resulting map are functionally related, and that distinct map regions correspond to distinct cellular systems, enabling a computational strategy for discovering proteins' functions on the basis of the proteins' map positions. Using the map produced by LGL, we infer general functions for 23 uncharacterized protein families.

  1. Metabolic Networks of Sodalis glossinidius: A Systems Biology Approach to Reductive Evolution

    PubMed Central

    Belda, Eugeni; Silva, Francisco J.; Peretó, Juli; Moya, Andrés

    2012-01-01

    Background Genome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius. Results The functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association. Conclusions Stoichiometric analysis reveals few gene inactivation events whose effects on the functionality of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to host-dependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with the progressive integration in the more stable environment provided by the insect host. Finally, reductive evolution simulations reveal the strong influence that external conditions exert on the evolvability of metabolic systems. PMID:22292008

  2. A biologically inspired sensor network framework for autonomous structural health monitoring

    NASA Astrophysics Data System (ADS)

    Chen, Bo

    2009-03-01

    This paper presents a biologically inspired sensor network framework for autonomous structural health monitoring (SHM). The presented sensor network framework transforms desirable characteristics and effective defense mechanisms of the natural immune system to wireless sensor networks for SHM. The autonomous structural health monitoring is achieved through an integrated sensor network framework consisting of high computational power sensors, a mobileagent- based sensor network middleware, and artificial immune pattern recognition (AIPR) methodology for structure damage detection and classification. An AIPR-based structure damage classifier (AIPR-SDC) has been developed, which incorporates several novel characteristics of the natural immune system. The performance of the AIPR-SDC has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) SHM Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms. The further study of unsupervised structure damage classification is also conducted by integrating data clustering techniques and the AIPR method.

  3. Functional characterization of bacterial sRNAs using a network biology approach.

    PubMed

    Modi, Sheetal R; Camacho, Diogo M; Kohanski, Michael A; Walker, Graham C; Collins, James J

    2011-09-13

    Small RNAs (sRNAs) are important components of posttranscriptional regulation. These molecules are prevalent in bacterial and eukaryotic organisms, and involved in a variety of responses to environmental stresses. The functional characterization of sRNAs is challenging and requires highly focused and extensive experimental procedures. Here, using a network biology approach and a compendium of gene expression profiles, we predict functional roles and regulatory interactions for sRNAs in Escherichia coli. We experimentally validate predictions for three sRNAs in our inferred network: IsrA, GlmZ, and GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in the SOS response, and we expand on current knowledge of the GcvB sRNA, demonstrating its broad role in the regulation of amino acid metabolism and transport. We also show, using the inferred network coupled with experiments, that GcvB and Lrp, a transcription factor, repress each other in a mutually inhibitory network. This work shows that a network-based approach can be used to identify the cellular function of sRNAs and characterize the relationship between sRNAs and transcription factors.

  4. Biological modelling of a computational spiking neural network with neuronal avalanches.

    PubMed

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-06-28

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'. © 2017 The Author(s).

  5. Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency

    PubMed Central

    Wang, Zengjian; Zhang, Delong; Liang, Bishan; Chang, Song; Pan, Jinghua; Huang, Ruiwang; Liu, Ming

    2016-01-01

    Biological motion perception (BMP) refers to the ability to perceive the moving form of a human figure from a limited amount of stimuli, such as from a few point lights located on the joints of a moving body. BMP is commonplace and important, but there is great inter-individual variability in this ability. This study used multiple regression model analysis to explore the association between BMP performance and intrinsic brain activity, in order to investigate the neural substrates underlying inter-individual variability of BMP performance. The resting-state functional magnetic resonance imaging (rs-fMRI) and BMP performance data were collected from 24 healthy participants, for whom intrinsic brain networks were constructed, and a graph-based network efficiency metric was measured. Then, a multiple linear regression model was used to explore the association between network regional efficiency and BMP performance. We found that the local and global network efficiency of many regions was significantly correlated with BMP performance. Further analysis showed that the local efficiency rather than global efficiency could be used to explain most of the BMP inter-individual variability, and the regions involved were predominately located in the Default Mode Network (DMN). Additionally, discrimination analysis showed that the local efficiency of certain regions such as the thalamus could be used to classify BMP performance across participants. Notably, the association pattern between network nodal efficiency and BMP was different from the association pattern of static directional/gender information perception. Overall, these findings show that intrinsic brain network efficiency may be considered a neural factor that explains BMP inter-individual variability. PMID:27853427

  6. Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective.

    PubMed

    Davidsen, Peter K; Turan, Nil; Egginton, Stuart; Falciani, Francesco

    2016-02-01

    The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of "omics" technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to "learn" biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed. Copyright © 2016 the American Physiological Society.

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

    PubMed Central

    Tareen, Samar H.K.; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  9. Biological networks in Parkinson's disease: an insight into the epigenetic mechanisms associated with this disease.

    PubMed

    Chatterjee, Paulami; Roy, Debjani; Bhattacharyya, Malay; Bandyopadhyay, Sanghamitra

    2017-09-12

    Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease. We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD. Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our

  10. Interfacing a biosurveillance portal and an international network of institutional analysts to detect biological threats.

    PubMed

    Riccardo, Flavia; Shigematsu, Mika; Chow, Catherine; McKnight, C Jason; Linge, Jens; Doherty, Brian; Dente, Maria Grazia; Declich, Silvia; Barker, Mike; Barboza, Philippe; Vaillant, Laetitia; Donachie, Alastair; Mawudeku, Abla; Blench, Michael; Arthur, Ray

    2014-01-01

    The Early Alerting and Reporting (EAR) project, launched in 2008, is aimed at improving global early alerting and risk assessment and evaluating the feasibility and opportunity of integrating the analysis of biological, chemical, radionuclear (CBRN), and pandemic influenza threats. At a time when no international collaborations existed in the field of event-based surveillance, EAR's innovative approach involved both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security Initiative, which involved the ministries of health of the G7 countries and Mexico, the World Health Organization, and the European Commission. The EAR project pooled data from 7 major internet-based biosurveillance systems onto a common portal that was progressively optimized for biological threat detection under the guidance of epidemic intelligence experts from public health institutions in Canada, the European Centre for Disease Prevention and Control, France, Germany, Italy, Japan, the United Kingdom, and the United States. The group became the first end users of the EAR portal, constituting a network of analysts working with a common standard operating procedure and risk assessment tools on a rotation basis to constantly screen and assess public information on the web for events that could suggest an intentional release of biological agents. Following the first 2-year pilot phase, the EAR project was tested in its capacity to monitor biological threats, proving that its working model was feasible and demonstrating the high commitment of the countries and international institutions involved. During the testing period, analysts using the EAR platform did not miss intentional events of a biological nature and did not issue false alarms. Through the findings of this initial assessment, this article provides insights into how the field of epidemic intelligence can advance through an

  11. Interfacing a Biosurveillance Portal and an International Network of Institutional Analysts to Detect Biological Threats

    PubMed Central

    Shigematsu, Mika; Chow, Catherine; McKnight, C. Jason; Linge, Jens; Doherty, Brian; Dente, Maria Grazia; Declich, Silvia; Barker, Mike; Barboza, Philippe; Vaillant, Laetitia; Donachie, Alastair; Mawudeku, Abla; Blench, Michael; Arthur, Ray

    2014-01-01

    The Early Alerting and Reporting (EAR) project, launched in 2008, is aimed at improving global early alerting and risk assessment and evaluating the feasibility and opportunity of integrating the analysis of biological, chemical, radionuclear (CBRN), and pandemic influenza threats. At a time when no international collaborations existed in the field of event-based surveillance, EAR's innovative approach involved both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security Initiative, which involved the ministries of health of the G7 countries and Mexico, the World Health Organization, and the European Commission. The EAR project pooled data from 7 major internet-based biosurveillance systems onto a common portal that was progressively optimized for biological threat detection under the guidance of epidemic intelligence experts from public health institutions in Canada, the European Centre for Disease Prevention and Control, France, Germany, Italy, Japan, the United Kingdom, and the United States. The group became the first end users of the EAR portal, constituting a network of analysts working with a common standard operating procedure and risk assessment tools on a rotation basis to constantly screen and assess public information on the web for events that could suggest an intentional release of biological agents. Following the first 2-year pilot phase, the EAR project was tested in its capacity to monitor biological threats, proving that its working model was feasible and demonstrating the high commitment of the countries and international institutions involved. During the testing period, analysts using the EAR platform did not miss intentional events of a biological nature and did not issue false alarms. Through the findings of this initial assessment, this article provides insights into how the field of epidemic intelligence can advance through an

  12. Systems biology beyond networks: generating order from disorder through self-organization.

    PubMed

    Saetzler, K; Sonnenschein, C; Soto, A M

    2011-06-01

    Erwin Schrödinger pointed out in his 1944 book "What is Life" that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. Systems biology beyond networks: generating order from disorder through self-organization

    PubMed Central

    Saetzler, K.; Sonnenschein, C.; Soto, A.M.

    2011-01-01

    Erwin Schrödinger pointed out in his 1944 book “What is Life” that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated. PMID:21569848

  14. Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity

    SciTech Connect

    Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.; Baddeley, Robert L.; Riensche, Roderick M.; Jensen, Russell S.; Verhagen, Marc

    2010-08-02

    Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of fully-supervised/reverse-engineering learning methods. In this paper, we explore a novel semi-supervised approach in which biological networks are learned from a reference list of genes and a partial set of links for these genes extracted automatically from PubMed abstracts, using a knowledge-driven bootstrapping algorithm. We show how new relevant links across genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. We describe an application of this approach to the TGFB pathway as a case study and show how the ensuing results prove the feasibility of the approach as an alternate or complementary technique to fully supervised methods.

  15. A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma

    PubMed Central

    Zhuang, Liwei; Wu, Yun; Han, Jiwu; Ling, Xiaohua; Wang, Liguo; Zhu, Chengyan; Fu, Yili

    2014-01-01

    In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC. PMID:24949431

  16. Structural data in synthetic biology approaches for studying general design principles of cellular signaling networks.

    PubMed

    Kiel, Christina; Serrano, Luis

    2012-11-07

    In recent years, high-throughput discovery of macromolecular protein structures and complexes has played a major role in advancing a more systems-oriented view of protein interaction and signaling networks. The design of biological systems often employs structural information or structure-based protein design to successfully implement synthetic signaling circuits or for rewiring signaling flows. Here, we summarize the latest advances in using structural information for studying protein interaction and signaling networks, and in synthetic biology approaches. We then provide a perspective of how combining structural biology with engineered cell signaling modules--using additional information from quantitative biochemistry and proteomics, gene evolution, and mathematical modeling--can provide insight into signaling modules and the general design principles of cell signaling. Ultimately, this will improve our understanding of cell- and tissue-type-specific signal transduction. Integrating the quantitative effects of disease mutations into these systems may provide a basis for elucidating the molecular mechanisms of diseases. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology.

    PubMed

    Ivanisenko, Vladimir A; Saik, Olga V; Ivanisenko, Nikita V; Tiys, Evgeny S; Ivanisenko, Timofey V; Demenkov, Pavel S; Kolchanov, Nikolay A

    2015-01-01

    Sufficient knowledge of molecular and genetic interactions, which comprise the entire basis of the functioning of living systems, is one of the necessary requirements for successfully answering almost any research question in the field of biology and medicine. To date, more than 24 million scientific papers can be found in PubMed, with many of them containing descriptions of a wide range of biological processes. The analysis of such tremendous amounts of data requires the use of automated text-mining approaches. Although a handful of tools have recently been developed to meet this need, none of them provide error-free extraction of highly detailed information. The ANDSystem package was developed for the reconstruction and analysis of molecular genetic networks based on an automated text-mining technique. It provides a detailed description of the various types of interactions between genes, proteins, microRNA's, metabolites, cellular components, pathways and diseases, taking into account the specificity of cell lines and organisms. Although the accuracy of ANDSystem is comparable to other well known text-mining tools, such as Pathway Studio and STRING, it outperforms them in having the ability to identify an increased number of interaction types. The use of ANDSystem, in combination with Pathway Studio and STRING, can improve the quality of the automated reconstruction of molecular and genetic networks. ANDSystem should provide a useful tool for researchers working in a number of different fields, including biology, biotechnology, pharmacology and medicine.

  18. Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages

    PubMed Central

    2014-01-01

    Background High-throughput methods for biological measurements generate vast amounts of quantitative data, which necessitate the development of advanced approaches to data analysis to help understand the underlying mechanisms and networks. Reconstruction of biological networks from measured data of different components is a significant challenge in systems biology. Results We use an information theoretic approach to reconstruct phosphoprotein-cytokine networks in RAW 264.7 macrophage cells. Cytokines are secreted upon activation of a wide range of regulatory signals transduced by the phosphoprotein network. Identifying these components can help identify regulatory modules responsible for the inflammatory phenotype. The information theoretic approach is based on estimation of mutual information of interactions by using kernel density estimators. Mutual information provides a measure of statistical dependencies between interacting components. Using the topology of the network derived, we develop a data-driven parsimonious input–output model of the phosphoprotein-cytokine network. Conclusions We demonstrate the applicability of our information theoretic approach to reconstruction of biological networks. For the phosphoprotein-cytokine network, this approach not only captures most of the known signaling components involved in cytokine release but also predicts new signaling components involved in the release of cytokines. The results of this study are important for gaining a clear understanding of macrophage activation during the inflammation process. PMID:24964861

  19. The shortest path is not the one you know: application of biological network resources in precision oncology research.

    PubMed

    Kuperstein, Inna; Grieco, Luca; Cohen, David P A; Thieffry, Denis; Zinovyev, Andrei; Barillot, Emmanuel

    2015-03-01

    Several decades of molecular biology research have delivered a wealth of detailed descriptions of molecular interactions in normal and tumour cells. This knowledge has been functionally organised and assembled into dedicated biological pathway resources that serve as an invaluable tool, not only for structuring the information about molecular interactions but also for making it available for biological, clinical and computational studies. With the advent of high-throughput molecular profiling of tumours, close to complete molecular catalogues of mutations, gene expression and epigenetic modifications are available and require adequate interpretation. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular profiles of tumours. Making sense out of these descriptions requires biological pathway resources for functional interpretation of the data. In this review, we describe the available biological pathway resources, their characteristics in terms of construction mode, focus, aims and paradigms of biological knowledge representation. We present a new resource that is focused on cancer-related signalling, the Atlas of Cancer Signalling Networks. We briefly discuss current approaches for data integration, visualisation and analysis, using biological networks, such as pathway scoring, guilt-by-association and network propagation. Finally, we illustrate with several examples the added value of data interpretation in the context of biological networks and demonstrate that it may help in analysis of high-throughput data like mutation, gene expression or small interfering RNA screening and can guide in patients stratification. Finally, we discuss perspectives for improving precision medicine using biological network resources and tools. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular patterns of tumours and enable to put precision

  20. Networks and their applications to biological systems: From ecological dynamics to gene regulation

    NASA Astrophysics Data System (ADS)

    Sevim, Volkan

    In this dissertation, we study three biological applications of networks. The first one is a biological coevolution model, in which a species is defined by a genome in the form of a finite bitstring and the interactions between species are given by a fixed matrix with randomly distributed elements. Here we study a version of the model, in which the matrix elements are correlated to a controllable degree by means of an averaging scheme. This method allows creation of mutants resembling their ancestors (wildtype). We compare long kinetic Monte Carlo simulations of models with uncorrelated and correlated interactions. We find that while there are quantitative differences, most qualitative features, such as 1/f behavior in power spectral densities for the diversity indices and the power-law distribution of species lifetimes, are not significantly affected by the correlations in the interaction matrix. The second application is the growth of a directed network, in which the growth is constrained by the cost of adding links to the existing nodes. This is a new preferential-attachment scheme, in which a new node attaches to an existing node i with probability pi(k i, k'i ) ∝ ( k'i /ki)gamma, where ki and k'i are the number of outgoing and incoming links at i, respectively, and gamma is a constant. First, we calculate the degree distribution for the outgoing links for a simplified form of this function, pi( ki) ∝ k-1i , both analytically and by Monte Carlo simulations. The distribution decays like kmuk/Gamma(k) for large k, where mu is a constant. We relate this mechanism to simple food-web models by implementing it in the cascade model. We also study the generalized case, pi(ki, k'i ) ∝ ( k'i /ki)gamma, by simulations. The third application is the evolution of robustness to mutations and noise in gene regulatory networks. It has been shown that robustness to mutations and noise can evolve through stabilizing selection for optimal phenotypes in model gene regulatory

  1. Why Traditional Expository Teaching-Learning Approaches May Founder? An Experimental Examination of Neural Networks in Biology Learning

    ERIC Educational Resources Information Center

    Lee, Jun-Ki; Kwon, Yong-Ju

    2011-01-01

    Using functional magnetic resonance imaging (fMRI), this study investigates and discusses neurological explanations for, and the educational implications of, the neural network activations involved in hypothesis-generating and hypothesis-understanding for biology education. Two sets of task paradigms about biological phenomena were designed:…

  2. Why Traditional Expository Teaching-Learning Approaches May Founder? An Experimental Examination of Neural Networks in Biology Learning

    ERIC Educational Resources Information Center

    Lee, Jun-Ki; Kwon, Yong-Ju

    2011-01-01

    Using functional magnetic resonance imaging (fMRI), this study investigates and discusses neurological explanations for, and the educational implications of, the neural network activations involved in hypothesis-generating and hypothesis-understanding for biology education. Two sets of task paradigms about biological phenomena were designed:…

  3. Social insect colony as a biological regulatory system: modelling information flow in dominance networks

    PubMed Central

    Nandi, Anjan K.; Sumana, Annagiri; Bhattacharya, Kunal

    2014-01-01

    Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata—a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure—the ‘feed-forward loop’—a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony. PMID:25320069

  4. Social insect colony as a biological regulatory system: modelling information flow in dominance networks.

    PubMed

    Nandi, Anjan K; Sumana, Annagiri; Bhattacharya, Kunal

    2014-12-06

    Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  5. Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

    PubMed Central

    Toni, Tina; Tidor, Bruce

    2013-01-01

    Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady

  6. B-cell lymphoma gene regulatory networks: biological consistency among inference methods.

    PubMed

    de Matos Simoes, Ricardo; Dehmer, Matthias; Emmert-Streib, Frank

    2013-01-01

    Despite the development of numerous gene regulatory network (GRN) inference methods in the last years, their application, usage and the biological significance of the resulting GRN remains unclear for our general understanding of large-scale gene expression data in routine practice. In our study, we conduct a structural and a functional analysis of B-cell lymphoma GRNs that were inferred using 3 mutual information-based GRN inference methods: C3Net, BC3Net and Aracne. From a comparative analysis on the global level, we find that the inferred B-cell lymphoma GRNs show major differences. However, on the edge-level and the functional-level-that are more important for our biological understanding-the B-cell lymphoma GRNs were highly similar among each other. Also, the ranks of the degree centrality values and major hub genes in the inferred networks are highly conserved as well. Interestingly, the major hub genes of all GRNs are associated with the G-protein-coupled receptor pathway, cell-cell signaling and cell cycle. This implies that hub genes of the GRNs can be highly consistently inferred with C3Net, BC3Net, and Aracne, representing prominent targets for signaling pathways. Finally, we describe the functional and structural relationship between C3Net, BC3Net and Aracne gene regulatory networks. Our study shows that these GRNs that are inferred from large-scale gene expression data are promising for the identification of novel candidate interactions and pathways that play a key role in the underlying mechanisms driving cancer hallmarks. Overall, our comparative analysis reveals that these GRNs inferred with considerably different inference methods contain large amounts of consistent, method independent, biological information.

  7. Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.

    PubMed

    Toni, Tina; Tidor, Bruce

    2013-01-01

    Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA--for example, on the same transcript--was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state

  8. Inference of biological networks using Bi-directional Random Forest Granger causality.

    PubMed

    Furqan, Mohammad Shaheryar; Siyal, Mohammad Yakoob

    2016-01-01

    The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality. This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets, i.e., fMRI and HeLa cell. fMRI data was used to map brain network involved in deductive reasoning while HeLa cell dataset was used to map gene network involved in cancer.

  9. CoNet app: inference of biological association networks using Cytoscape

    PubMed Central

    Faust, Karoline; Raes, Jeroen

    2016-01-01

    Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet. PMID:27853510

  10. Visual data mining of biological networks: one size does not fit all.

    PubMed

    Pastrello, Chiara; Otasek, David; Fortney, Kristen; Agapito, Giuseppe; Cannataro, Mario; Shirdel, Elize; Jurisica, Igor

    2013-01-01

    High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

  11. Collaboration Networks in the Brazilian Scientific Output in Evolutionary Biology: 2000-2012.

    PubMed

    Santin, Dirce M; Vanz, Samile A S; Stumpf, Ida R C

    2016-03-01

    This article analyzes the existing collaboration networks in the Brazilian scientific output in Evolutionary Biology, considering articles published during the period from 2000 to 2012 in journals indexed by Web of Science. The methodology integrates bibliometric techniques and Social Network Analysis resources to describe the growth of Brazilian scientific output and understand the levels, dynamics and structure of collaboration between authors, institutions and countries. The results unveil an enhancement and consolidation of collaborative relationships over time and suggest the existence of key institutions and authors, whose influence on research is expressed by the variety and intensity of the relationships established in the co-authorship of articles. International collaboration, present in more than half of the publications, is highly significant and unusual in Brazilian science. The situation indicates the internationalization of scientific output and the ability of the field to take part in the science produced by the international scientific community.

  12. International institute for collaborative cell biology and biochemistry--history and memoirs from an international network for biological sciences.

    PubMed

    Cameron, L C

    2013-01-01

    I was invited to write this essay on the occasion of my selection as the recipient of the 2012 Bruce Alberts Award for Excellence in Science Education from the American Society for Cell Biology (ASCB). Receiving this award is an enormous honor. When I read the email announcement for the first time, it was more than a surprise to me, it was unbelievable. I joined ASCB in 1996, when I presented a poster and received a travel award. Since then, I have attended almost every ASCB meeting. I will try to use this essay to share with readers one of the best experiences in my life. Because this is an essay, I take the liberty of mixing some of my thoughts with data in a way that it not usual in scientific writing. I hope that this sacrifice of the format will achieve the goal of conveying what I have learned over the past 20 yr, during which time a group of colleagues and friends created a nexus of knowledge and wisdom. We have worked together to build a network capable of sharing and inspiring science all over the world.

  13. International Institute for Collaborative Cell Biology and Biochemistry—History and Memoirs from an International Network for Biological Sciences

    PubMed Central

    Cameron, L. C.

    2013-01-01

    I was invited to write this essay on the occasion of my selection as the recipient of the 2012 Bruce Alberts Award for Excellence in Science Education from the American Society for Cell Biology (ASCB). Receiving this award is an enormous honor. When I read the email announcement for the first time, it was more than a surprise to me, it was unbelievable. I joined ASCB in 1996, when I presented a poster and received a travel award. Since then, I have attended almost every ASCB meeting. I will try to use this essay to share with readers one of the best experiences in my life. Because this is an essay, I take the liberty of mixing some of my thoughts with data in a way that it not usual in scientific writing. I hope that this sacrifice of the format will achieve the goal of conveying what I have learned over the past 20 yr, during which time a group of colleagues and friends created a nexus of knowledge and wisdom. We have worked together to build a network capable of sharing and inspiring science all over the world. PMID:24006381

  14. Biological neural networks as model systems for designing future parallel processing computers

    NASA Technical Reports Server (NTRS)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  15. Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE)

    PubMed Central

    Le Novère, Nicolas; Hucka, Michael; Anwar, Nadia; Bader, Gary D; Demir, Emek; Moodie, Stuart; Sorokin, Anatoly

    2011-01-01

    The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner. PMID:22180826

  16. Physical and biological regulation of neuron regenerative growth and network formation on recombinant dragline silks.

    PubMed

    An, Bo; Tang-Schomer, Min D; Huang, Wenwen; He, Jiuyang; Jones, Justin A; Lewis, Randolph V; Kaplan, David L

    2015-04-01

    Recombinant spider silks produced in transgenic goat milk were studied as cell culture matrices for neuronal growth. Major ampullate spidroin 1 (MaSp1) supported neuronal growth, axon extension and network connectivity, with cell morphology comparable to the gold standard poly-lysine. In addition, neurons growing on MaSp1 films had increased neural cell adhesion molecule (NCAM) expression at both mRNA and protein levels. The results indicate that MaSp1 films present useful surface charge and substrate stiffness to support the growth of primary rat cortical neurons. Moreover, a putative neuron-specific surface binding sequence GRGGL within MaSp1 may contribute to the biological regulation of neuron growth. These findings indicate that MaSp1 could regulate neuron growth through its physical and biological features. This dual regulation mode of MaSp1 could provide an alternative strategy for generating functional silk materials for neural tissue engineering.

  17. Mueller-matrix differentiation of fibrillar networks of biological tissues with different phase and amplitude anisotropy

    NASA Astrophysics Data System (ADS)

    Ushenko, A. G.; Dubolazov, A. V.; Ushenko, V. A.; Ushenko, Yu. A.; Kushnerick, L. Y.; Olar, O. V.; Pashkovskaya, N. V.; Marchuk, Yu. F.

    2016-09-01

    The work consists of investigation results of diagnostic efficiency of a new azimuthally stable Mueller-matrix method of analysis of laser autofluorescence coordinate distributions of biological tissues histological sections. A new model of generalized optical anisotropy of biological tissues protein networks is proposed in order to define the processes of laser autofluorescence. The influence of complex mechanisms of both phase anisotropy (linear birefringence and optical activity) and linear (circular) dichroism is taken into account. The interconnections between the azimuthally stable Mueller-matrix elements characterizing laser autofluorescence and different mechanisms of optical anisotropy are determined. The statistic analysis of coordinate distributions of such Mueller-matrix rotation invariants is proposed. Thereupon the quantitative criteria (statistic moments of the 1st to the 4th order) of differentiation of histological sections of uterus wall tumor - group 1 (dysplasia) and group 2 (adenocarcinoma) are estimated.

  18. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

    PubMed

    Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

  19. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

    PubMed Central

    Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method. PMID:27847827

  20. The need to connect: on the cell biology of synapses, behaviors, and networks in science.

    PubMed

    Colón-Ramos, Daniel A

    2016-11-01

    My laboratory is interested in the cell biology of the synapse. Synapses, which are points of cellular communication between neurons, were first described by Santiago Ramón y Cajal as "protoplasmic kisses that appear to constitute the final ecstasy of an epic love story." Who would not want to work on that?! My lab examines the biological mechanisms neurons use to find and connect to each other. How are synapses formed during development, maintained during growth, and modified during learning? In this essay, I reflect about my scientific journey to the synapse, the cell biological one, but also a metaphorical synapse-my role as a point of contact between the production of knowledge and its dissemination. In particular, I discuss how the architecture of scientific networks propels knowledge production but can also exclude certain groups in science. © 2016 Colón-Ramos This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  1. Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

    PubMed Central

    Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S.; Niu, Xufeng; Zhang, Jinfeng

    2011-01-01

    A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. PMID:21738677

  2. The role of computer networking in investigating unusual disease outbreaks and allegations of biological and toxin weapons use.

    PubMed

    Woodall, J

    1998-01-01

    Computer networking can aid in the epidemiological investigation of unusual disease outbreaks and possible uses of biological weapons. Exchange of computerized data over the Internet has many advantages in facilitating the investigation of the source of a disease outbreak. It is especially useful in the investigation of suspected or alleged releases of biological weapons. Computer networking through the Internet a fosters a truly global disease outbreak early warning system in which both government and non-government sources are contributing. Such information exchange is of great potential benefit to the Biological Weapons Convention and the attempts to develop a verification protocol.

  3. WDNfinder: A method for minimum driver node set detection and analysis in directed and weighted biological network.

    PubMed

    Yanshuo, Chu; Zhenxing, Wang; Rongjie, Wang; Ningyi, Zhang; Jie, Li; Yang, Hu; Mingxiang, Teng; Yadong, Wang

    2017-09-04

    Structural controllability is the generalization of traditional controllability for dynamical systems. During the last decade, interesting biological discoveries have been inferred by applied structural controllability analysis to biological networks. However, false positive/negative information (i.e. nodes and edges) widely exists in biological networks that documented in public data sources, which can hinder accurate analysis of structural controllability. In this study, we propose WDNfinder, a comprehensive analysis package that provides structural controllability with consideration of node connection strength in biological networks. When applied to the human cancer signaling network and p53-mediate DNA damage response network, WDNfinder shows high accuracy on essential nodes prediction in these networks. Compared to existing methods, WDNfinder can significantly narrow down the set of minimum driver node set (MDS) under the restriction of domain knowledge. When using p53-mediate DNA damage response network as illustration, we find more meaningful MDSs by WDNfinder. The source code is implemented in python and publicly available together with relevant data on GitHub: https://github.com/dustincys/WDNfinder .

  4. Frontotemporal dementia: insights into the biological underpinnings of disease through gene co-expression network analysis.

    PubMed

    Ferrari, Raffaele; Forabosco, Paola; Vandrovcova, Jana; Botía, Juan A; Guelfi, Sebastian; Warren, Jason D; Momeni, Parastoo; Weale, Michael E; Ryten, Mina; Hardy, John

    2016-02-24

    In frontotemporal dementia (FTD) there is a critical lack in the understanding of biological and molecular mechanisms involved in disease pathogenesis. The heterogeneous genetic features associated with FTD suggest that multiple disease-mechanisms are likely to contribute to the development of this neurodegenerative condition. We here present a systems biology approach with the scope of i) shedding light on the biological processes potentially implicated in the pathogenesis of FTD and ii) identifying novel potential risk factors for FTD. We performed a gene co-expression network analysis of microarray expression data from 101 individuals without neurodegenerative diseases to explore regional-specific co-expression patterns in the frontal and temporal cortices for 12 genes (MAPT, GRN, CHMP2B, CTSC, HLA-DRA, TMEM106B, C9orf72, VCP, UBQLN2, OPTN, TARDBP and FUS) associated with FTD and we then carried out gene set enrichment and pathway analyses, and investigated known protein-protein interactors (PPIs) of FTD-genes products. Gene co-expression networks revealed that several FTD-genes (such as MAPT and GRN, CTSC and HLA-DRA, TMEM106B, and C9orf72, VCP, UBQLN2 and OPTN) were clustering in modules of relevance in the frontal and temporal cortices. Functional annotation and pathway analyses of such modules indicated enrichment for: i) DNA metabolism, i.e. transcription regulation, DNA protection and chromatin remodelling (MAPT and GRN modules); ii) immune and lysosomal processes (CTSC and HLA-DRA modules), and; iii) protein meta/catabolism (C9orf72, VCP, UBQLN2 and OPTN, and TMEM106B modules). PPI analysis supported the results of the functional annotation and pathway analyses. This work further characterizes known FTD-genes and elaborates on their biological relevance to disease: not only do we indicate likely impacted regional-specific biological processes driven by FTD-genes containing modules, but also do we suggest novel potential risk factors among the FTD

  5. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology.

    PubMed

    Bahramali, Golnaz; Goliaei, Bahram; Minuchehr, Zarrin; Marashi, Sayed-Amir

    2017-02-01

    Chameleon proteins are proteins which include sequences that can adopt α-helix-β-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or β-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.

  6. Biological Dosimetry by the Triage Dicentric Chromosome Assay – Further validation of International Networking

    PubMed Central

    Wilkins, Ruth C.; Romm, Horst; Oestreicher, Ursula; Marro, Leonora; Yoshida, Mitsuaki A.; Suto, Y.; Prasanna, Pataje G.S.

    2011-01-01

    Biological dosimetry is an essential tool for estimating radiation doses received to personnel when physical dosimetry is not available or inadequate. The current preferred biodosimetry method is based on the measurement of radiation-specific dicentric chromosomes in exposed individuals' peripheral blood lymphocytes. However, this method is labour-, time- and expertise-demanding. Consequently, for mass casualty applications, strategies have been developed to increase its throughput. One such strategy is to develop validated cytogenetic biodosimetry laboratory networks, both national and international. In a previous study, the dicentric chromosome assay (DCA) was validated in our cytogenetic biodosimetry network involving five geographically dispersed laboratories. A complementary strategy to further enhance the throughput of the DCA among inter-laboratory networks is to use a triage DCA where dose assessments are made by truncating the labour-demanding and time-consuming metaphase-spread analysis to 20 to 50 metaphase spreads instead of routine 500 to 1000 metaphase spread analysis. Our laboratory network also validated this triage DCA, however, these dose estimates were made using calibration curves generated in each laboratory from the blood samples irradiated in a single laboratory. In an emergency situation, dose estimates made using pre-existing calibration curves which may vary according to radiation type and dose rate and therefore influence the assessed dose. Here, we analyze the effect of using a pre-existing calibration curve on assessed dose among our network laboratories. The dose estimates were made by analyzing 1000 metaphase spreads as well as triage quality scoring and compared to actual physical doses applied to the samples for validation. The dose estimates in the laboratory partners were in good agreement with the applied physical doses and determined to be adequate for guidance in the treatment of acute radiation syndrome. PMID:21949482

  7. Biological Dosimetry by the Triage Dicentric Chromosome Assay - Further validation of International Networking.

    PubMed

    Wilkins, Ruth C; Romm, Horst; Oestreicher, Ursula; Marro, Leonora; Yoshida, Mitsuaki A; Suto, Y; Prasanna, Pataje G S

    2011-09-01

    Biological dosimetry is an essential tool for estimating radiation doses received to personnel when physical dosimetry is not available or inadequate. The current preferred biodosimetry method is based on the measurement of radiation-specific dicentric chromosomes in exposed individuals' peripheral blood lymphocytes. However, this method is labour-, time- and expertise-demanding. Consequently, for mass casualty applications, strategies have been developed to increase its throughput. One such strategy is to develop validated cytogenetic biodosimetry laboratory networks, both national and international. In a previous study, the dicentric chromosome assay (DCA) was validated in our cytogenetic biodosimetry network involving five geographically dispersed laboratories. A complementary strategy to further enhance the throughput of the DCA among inter-laboratory networks is to use a triage DCA where dose assessments are made by truncating the labour-demanding and time-consuming metaphase-spread analysis to 20 to 50 metaphase spreads instead of routine 500 to 1000 metaphase spread analysis. Our laboratory network also validated this triage DCA, however, these dose estimates were made using calibration curves generated in each laboratory from the blood samples irradiated in a single laboratory. In an emergency situation, dose estimates made using pre-existing calibration curves which may vary according to radiation type and dose rate and therefore influence the assessed dose. Here, we analyze the effect of using a pre-existing calibration curve on assessed dose among our network laboratories. The dose estimates were made by analyzing 1000 metaphase spreads as well as triage quality scoring and compared to actual physical doses applied to the samples for validation. The dose estimates in the laboratory partners were in good agreement with the applied physical doses and determined to be adequate for guidance in the treatment of acute radiation syndrome.

  8. A systems biology approach for understanding the collagen regulatory network in alcoholic liver disease.

    PubMed

    Nieto, Natalia

    2012-02-01

    Among the pathogenesis and risk factors of alcoholic liver disease (ALD) are the source of dietary fat, obesity, insulin resistance, adipokines and acetaldehyde. Translocation of Gram-negative bacteria from the gut, the subsequent effects mediated by endotoxin, and the increased production of matricellular proteins, cytokines, chemokines and growth factors, actively participate in the progression of liver injury. In addition, generation of reactive oxygen and nitrogen species and the activation of non-parenchymal cells also contribute to the pathophysiology of ALD. A key event leading to liver damage is the transition of quiescent hepatic stellate cells into activated myofibroblasts, with the consequent deposition of fibrillar collagen I resulting in significant scarring. Thus, it is becoming clearer that matricellular proteins are critical players in the pathophysiology of liver disease; however, additional mechanistic insight is needed to understand the signalling pathways involved in the up-regulation of collagen I protein. At present, systems biology approaches are helping to answer the many unresolved questions in this field and are allowing to more comprehensively identify protein networks regulating pathological collagen I deposition in hopes of determining how to prevent the onset of liver fibrosis and/or to slow disease progression. Thus, this review article provides a snapshot on current efforts for identifying pathological protein regulatory networks in the liver using systems biology tools. These approaches hold great promise for future research in liver disease.

  9. Logic-based models in systems biology: a predictive and parameter-free network analysis method†

    PubMed Central

    Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

    2012-01-01

    Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

  10. ProteoLens: a visual analytic t