WorldWideScience

Sample records for biological regulatory networks

  1. Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster

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    Kim Man-Sun

    2012-05-01

    Full Text Available Abstract Background Network motifs provided a “conceptual tool” for understanding the functional principles of biological networks, but such motifs have primarily been used to consider static network structures. Static networks, however, cannot be used to reveal time- and region-specific traits of biological systems. To overcome this limitation, we proposed the concept of a “spatiotemporal network motif,” a spatiotemporal sequence of network motifs of sub-networks which are active only at specific time points and body parts. Results On the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns. Conclusions Taken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks.

  2. A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

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    Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

    2013-01-01

    One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

  3. Integrating external biological knowledge in the construction of regulatory networks from time-series expression data

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    Lo Kenneth

    2012-08-01

    Full Text Available Abstract Background Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge. Results We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models. Conclusions We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.

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

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    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.

  5. A Systems’ Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

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    Xin Lai

    2013-01-01

    Full Text Available MicroRNAs (miRNAs are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

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

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    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

  7. Social insect colony as a biological regulatory system: modelling information flow in dominance networks.

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    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.

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

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    Samra Khalid

    2016-10-01

    Full Text Available 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

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

    NARCIS (Netherlands)

    He, F.; Murabito, E.; Westerhoff, H.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 throughin silicotheoretical studies with the aim to guide and complement furtherin vitroandin vivoexperimental

  10. Sieve-based relation extraction of gene regulatory networks from biological literature.

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    Žitnik, Slavko; Žitnik, Marinka; Zupan, Blaž; Bajec, Marko

    2015-01-01

    Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming

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

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    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. © 2016 The Author(s).

  12. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

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    Lan Chung-Yu

    2008-09-01

    Full Text Available Abstract Background Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs such as NF-κB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin. Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network

  13. Networks in Cell Biology

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    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.

  14. Synthetic biological networks

    International Nuclear Information System (INIS)

    Archer, Eric; Süel, Gürol M

    2013-01-01

    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. (review article)

  15. Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.

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    Saithong, Treenut; Painter, Kevin J; Millar, Andrew J

    2010-12-16

    A number of studies have previously demonstrated that "goodness of fit" is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain. Here, we propose a novel robustness analysis that aims to determine the "common robustness" of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network. Consistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.

  16. A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data.

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    Baur, Brittany; Bozdag, Serdar

    2015-04-01

    One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.

  17. From cell biology to immunology: Controlling metastatic progression of cancer via microRNA regulatory networks.

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    Park, Jae Hyon; Theodoratou, Evropi; Calin, George A; Shin, Jae Il

    2016-01-01

    Recently, the study of microRNAs has expanded our knowledge of the fundamental processes of cancer biology and the underlying mechanisms behind tumor metastasis. Extensive research in the fields of microRNA and its novel mechanisms of actions against various cancers has more recently led to the trial of a first cancer-targeted microRNA drug, MRX34. Yet, these microRNAs are mostly being studied and clinically trialed solely based on the understanding of their cell biologic effects, thus, neglecting the important immunologic effects that are sometimes opposite of the cell biologic effects. Here, we summarize both the cell biologic and immunologic effects of various microRNAs and discuss the importance of considering both effects before using them in clinical settings. We stress the importance of understanding the miRNA's effect on cancer metastasis from a "systems" perspective before developing a miRNA-targeted therapeutic in treating cancer metastasis.

  18. Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

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    Gidrol Xavier

    2008-02-01

    Full Text Available Abstract Background Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge. Results We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC devoted to BN structure learning. Conclusion We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.

  19. Mutational robustness of gene regulatory networks.

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    Aalt D J van Dijk

    Full Text Available Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive. In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

  20. Transcription regulatory networks analysis using CAGE

    KAUST Repository

    Tegnér, Jesper N.

    2009-10-01

    Mapping out cellular networks in general and transcriptional networks in particular has proved to be a bottle-neck hampering our understanding of biological processes. Integrative approaches fusing computational and experimental technologies for decoding transcriptional networks at a high level of resolution is therefore of uttermost importance. Yet, this is challenging since the control of gene expression in eukaryotes is a complex multi-level process influenced by several epigenetic factors and the fine interplay between regulatory proteins and the promoter structure governing the combinatorial regulation of gene expression. In this chapter we review how the CAGE data can be integrated with other measurements such as expression, physical interactions and computational prediction of regulatory motifs, which together can provide a genome-wide picture of eukaryotic transcriptional regulatory networks at a new level of resolution. © 2010 by Pan Stanford Publishing Pte. Ltd. All rights reserved.

  1. Reconstructing transcriptional regulatory networks through genomics data

    OpenAIRE

    Sun, Ning; Zhao, Hongyu

    2009-01-01

    One central problem in biology is to understand how gene expression is regulated under different conditions. Microarray gene expression data and other high throughput data have made it possible to dissect transcriptional regulatory networks at the genomics level. Owing to the very large number of genes that need to be studied, the relatively small number of data sets available, the noise in the data and the different natures of the distinct data types, network inference presents great challen...

  2. Sparsity in Model Gene Regulatory Networks

    International Nuclear Information System (INIS)

    Zagorski, M.

    2011-01-01

    We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

  3. Systems Biology Approach to the Dissection of the Complexity of Regulatory Networks in the S. scrofa Cardiocirculatory System

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    Paolo Martini

    2013-11-01

    Full Text Available Genome-wide experiments are routinely used to increase the understanding of the biological processes involved in the development and maintenance of a variety of pathologies. Although the technical feasibility of this type of experiment has improved in recent years, data analysis remains challenging. In this context, gene set analysis has emerged as a fundamental tool for the interpretation of the results. Here, we review strategies used in the gene set approach, and using datasets for the pig cardiocirculatory system as a case study, we demonstrate how the use of a combination of these strategies can enhance the interpretation of results. Gene set analyses are able to distinguish vessels from the heart and arteries from veins in a manner that is consistent with the different cellular composition of smooth muscle cells. By integrating microRNA elements in the regulatory circuits identified, we find that vessel specificity is maintained through specific miRNAs, such as miR-133a and miR-143, which show anti-correlated expression with their mRNA targets.

  4. Measuring the evolutionary rewiring of biological networks.

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    Chong Shou

    Full Text Available 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.

  5. Dominating biological networks.

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    Tijana Milenković

    Full Text Available Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC" genes (i.e., their protein products, such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

  6. A systems biology approach identified different regulatory networks targeted by KSHV miR-K12-11 in B cells and endothelial cells.

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    Yang, Yajie; Boss, Isaac W; McIntyre, Lauren M; Renne, Rolf

    2014-08-08

    Kaposi's sarcoma associated herpes virus (KSHV) is associated with tumors of endothelial and lymphoid origin. During latent infection, KSHV expresses miR-K12-11, an ortholog of the human tumor gene hsa-miR-155. Both gene products are microRNAs (miRNAs), which are important post-transcriptional regulators that contribute to tissue specific gene expression. Advances in target identification technologies and molecular interaction databases have allowed a systems biology approach to unravel the gene regulatory networks (GRNs) triggered by miR-K12-11 in endothelial and lymphoid cells. Understanding the tissue specific function of miR-K12-11 will help to elucidate underlying mechanisms of KSHV pathogenesis. Ectopic expression of miR-K12-11 differentially affected gene expression in BJAB cells of lymphoid origin and TIVE cells of endothelial origin. Direct miRNA targeting accounted for a small fraction of the observed transcriptome changes: only 29 genes were identified as putative direct targets of miR-K12-11 in both cell types. However, a number of commonly affected biological pathways, such as carbohydrate metabolism and interferon response related signaling, were revealed by gene ontology analysis. Integration of transcriptome profiling, bioinformatic algorithms, and databases of protein-protein interactome from the ENCODE project identified different nodes of GRNs utilized by miR-K12-11 in a tissue-specific fashion. These effector genes, including cancer associated transcription factors and signaling proteins, amplified the regulatory potential of a single miRNA, from a small set of putative direct targets to a larger set of genes. This is the first comparative analysis of miRNA-K12-11's effects in endothelial and B cells, from tissues infected with KSHV in vivo. MiR-K12-11 was able to broadly modulate gene expression in both cell types. Using a systems biology approach, we inferred that miR-K12-11 establishes its GRN by both repressing master TFs and influencing

  7. Regulatory Office for Network Industries

    International Nuclear Information System (INIS)

    2005-01-01

    The main goal of the economic regulation of network industries is to ensure a balance between the interests of consumers and investors and to encourage providing high-quality goods and services. The task of the regulatory authority is to protect the interests of consumers against monopolistic behaviour of regulated enterprises. At the same time, the regulatory office has to protect the interests of investors by giving them an opportunity to achieve an adequate return on their investments. And last, but not least, the regulatory office has to provide regulated enterprises with appropriate incentives to make them function in an efficient and effective manner and to guarantee the security of delivery of energies and related services. All this creates an efficient regulatory framework that is capable of attracting the required amount and type of investments. This also means providing third party access to the grids, the opening of energy markets, the un-bundling of accounts according to production, distribution, transmission and other activities and the establishment of a transparent and stable legislative environment for regulated companies, investors and consumers. Otherwise, in the long run consumers may suffer from a serious deterioration of service quality, although in the short run they are protected against increased prices. Under the Act No. 276/2001 Coll. on Regulation of Network Industries and on amendment of some acts the Office for Regulation of Network Industries has been commissioned to implement the main objectives of regulation of network industries. By network industries the Act No. 276/2001 Coll. on Regulation means the following areas: (a) Production, purchase, transit and distribution of electricity; (b) Production, purchase, transit and distribution of gas; (c) Production, purchase and distribution of heat; (d) Water management activities relating to the operation of the public water supply system or the public sewerage system; (e) Water management

  8. Design principles in biological networks

    Science.gov (United States)

    Goyal, Sidhartha

    Much of biology emerges from networks of interactions. Even in a single bacterium such as Escherichia coli, there are hundreds of coexisting gene and protein networks. Although biological networks are the outcome of evolution, various physical and biological constraints limit their functional capacity. The focus of this thesis is to understand how functional constraints such as optimal growth in mircoorganisms and information flow in signaling pathways shape the metabolic network of bacterium E. coli and the quorum sensing network of marine bacterium Vibrio harveyi, respectively. Metabolic networks convert basic elemental sources into complex building-blocks eventually leading to cell's growth. Therefore, typically, metabolic pathways are often coupled both by the use of a common substrate and by stoichiometric utilization of their products for cell growth. We showed that such a coupled network with product-feedback inhibition may exhibit limit-cycle oscillations which arise via a Hopf bifurcation. Furthermore, we analyzed several representative metabolic modules and find that, in all cases, simple product-feedback inhibition allows nearly optimal growth, in agreement with the predicted growth-rate by the flux-balance analysis (FBA). Bacteria have fascinating and diverse social lives. They display coordinated group behaviors regulated by quorum sensing (QS) systems. The QS circuit of V. harveyi integrates and funnels different ecological information through a common phosphorelay cascade to a set of small regulatory RNAs (sRNAs) that enables collective behavior. We analyzed the signaling properties and information flow in the QS circuit, which provides a model for information flow in signaling networks more generally. A comparative study of post-transcriptional and conventional transcriptional regulation suggest a niche for sRNAs in allowing cells to transition quickly yet reliably between distinct states. Furthermore, we develop a new framework for analyzing signal

  9. Generic Properties of Random Gene Regulatory Networks.

    Science.gov (United States)

    Li, Zhiyuan; Bianco, Simone; Zhang, Zhaoyang; Tang, Chao

    2013-12-01

    Modeling gene regulatory networks (GRNs) is an important topic in systems biology. Although there has been much work focusing on various specific systems, the generic behavior of GRNs with continuous variables is still elusive. In particular, it is not clear typically how attractors partition among the three types of orbits: steady state, periodic and chaotic, and how the dynamical properties change with network's topological characteristics. In this work, we first investigated these questions in random GRNs with different network sizes, connectivity, fraction of inhibitory links and transcription regulation rules. Then we searched for the core motifs that govern the dynamic behavior of large GRNs. We show that the stability of a random GRN is typically governed by a few embedding motifs of small sizes, and therefore can in general be understood in the context of these short motifs. Our results provide insights for the study and design of genetic networks.

  10. The transcriptional regulatory network of Mycobacterium tuberculosis.

    Directory of Open Access Journals (Sweden)

    Joaquín Sanz

    Full Text Available Under the perspectives of network science and systems biology, the characterization of transcriptional regulatory (TR networks beyond the context of model organisms offers a versatile tool whose potential remains yet mainly unexplored. In this work, we present an updated version of the TR network of Mycobacterium tuberculosis (M.tb, which incorporates newly characterized transcriptional regulations coming from 31 recent, different experimental works available in the literature. As a result of the incorporation of these data, the new network doubles the size of previous data collections, incorporating more than a third of the entire genome of the bacterium. We also present an exhaustive topological analysis of the new assembled network, focusing on the statistical characterization of motifs significances and the comparison with other model organisms. The expanded M.tb transcriptional regulatory network, considering its volume and completeness, constitutes an important resource for diverse tasks such as dynamic modeling of gene expression and signaling processes, computational reliability determination or protein function prediction, being the latter of particular relevance, given that the function of only a small percent of the proteins of M.tb is known.

  11. Regulatory networks, legal federalism, and multi-level regulatory systems

    OpenAIRE

    Kerber, Wolfgang; Wendel, Julia

    2016-01-01

    Transnational regulatory networks play important roles in multi-level regulatory regimes, as e.g, the European Union. In this paper we analyze the role of regulatory networks from the perspective of the economic theory of legal federalism. Often sophisticated intermediate institutional solutions between pure centralisation and pure decentralisation can help to solve complex tradeoff problems between the benefits and problems of centralised and decentralised solutions. Drawing upon the insight...

  12. A flood-based information flow analysis and network minimization method for gene regulatory networks.

    Science.gov (United States)

    Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

    2013-04-24

    Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

  13. Review of Biological Network Data and Its Applications

    Directory of Open Access Journals (Sweden)

    Donghyeon Yu

    2013-12-01

    Full Text Available Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

  14. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  15. Probabilistic biological network alignment.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-01-01

    Interactions between molecules are probabilistic events. An interaction may or may not happen with some probability, depending on a variety of factors such as the size, abundance, or proximity of the interacting molecules. In this paper, we consider the problem of aligning two biological networks. Unlike existing methods, we allow one of the two networks to contain probabilistic interactions. Allowing interaction probabilities makes the alignment more biologically relevant at the expense of explosive growth in the number of alternative topologies that may arise from different subsets of interactions that take place. We develop a novel method that efficiently and precisely characterizes this massive search space. We represent the topological similarity between pairs of aligned molecules (i.e., proteins) with the help of random variables and compute their expected values. We validate our method showing that, without sacrificing the running time performance, it can produce novel alignments. Our results also demonstrate that our method identifies biologically meaningful mappings under a comprehensive set of criteria used in the literature as well as the statistical coherence measure that we developed to analyze the statistical significance of the similarity of the functions of the aligned protein pairs.

  16. Towards a predictive theory for genetic regulatory networks

    Science.gov (United States)

    Tkacik, Gasper

    When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.

  17. Semi-supervised prediction of gene regulatory networks using ...

    Indian Academy of Sciences (India)

    2015-09-28

    Sep 28, 2015 ... Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging ... two types of methods differ primarily based on whether ..... negligible, allowing us to draw the qualitative conclusions .... research will be conducted to develop additional biologically.

  18. Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

    Science.gov (United States)

    Ferreira, Gustavo Rodrigues; Nakaya, Helder Imoto; Costa, Luciano da Fontoura

    2018-04-01

    The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

  19. Dissecting microregulation of a master regulatory network

    Directory of Open Access Journals (Sweden)

    Kaimal Vivek

    2008-02-01

    Full Text Available Abstract Background The master regulator p53 tumor-suppressor protein through coordination of several downstream target genes and upstream transcription factors controls many pathways important for tumor suppression. While it has been reported that some of the p53's functions are microRNA-mediated, it is not known as to how many other microRNAs might contribute to the p53-mediated tumorigenesis. Results Here, we use bioinformatics-based integrative approach to identify and prioritize putative p53-regulated miRNAs, and unravel the miRNA-based microregulation of the p53 master regulatory network. Specifically, we identify putative microRNA regulators of a transcription factors that are upstream or downstream to p53 and b p53 interactants. The putative p53-miRs and their targets are prioritized using current knowledge of cancer biology and literature-reported cancer-miRNAs. Conclusion Our predicted p53-miRNA-gene networks strongly suggest that coordinated transcriptional and p53-miR mediated networks could be integral to tumorigenesis and the underlying processes and pathways.

  20. Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

    Science.gov (United States)

    Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  1. Functional alignment of regulatory networks: a study of temperate phages.

    Directory of Open Access Journals (Sweden)

    Ala Trusina

    2005-12-01

    Full Text Available The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage lambda and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective.

  2. Querying Large Biological Network Datasets

    Science.gov (United States)

    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.…

  3. Modular analysis of biological networks.

    Science.gov (United States)

    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.

  4. The Reconstruction and Analysis of Gene Regulatory Networks.

    Science.gov (United States)

    Zheng, Guangyong; Huang, Tao

    2018-01-01

    In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

  5. Information transmission in genetic regulatory networks: a review

    International Nuclear Information System (INIS)

    Tkacik, Gasper; Walczak, Aleksandra M

    2011-01-01

    Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'. (topical review)

  6. Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother

    OpenAIRE

    Khan, Jehandad; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

    2014-01-01

    It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inf...

  7. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  8. Modeling stochasticity and robustness in gene regulatory networks.

    Science.gov (United States)

    Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; De Micheli, Giovanni; Xenarios, Ioannis

    2009-06-15

    Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

  9. [Network structures in biological systems].

    Science.gov (United States)

    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.

  10. Splitting Strategy for Simulating Genetic Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Xiong You

    2014-01-01

    Full Text Available The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions.

  11. Recurrent rewiring and emergence of RNA regulatory networks.

    Science.gov (United States)

    Wilinski, Daniel; Buter, Natascha; Klocko, Andrew D; Lapointe, Christopher P; Selker, Eric U; Gasch, Audrey P; Wickens, Marvin

    2017-04-04

    Alterations in regulatory networks contribute to evolutionary change. Transcriptional networks are reconfigured by changes in the binding specificity of transcription factors and their cognate sites. The evolution of RNA-protein regulatory networks is far less understood. The PUF (Pumilio and FBF) family of RNA regulatory proteins controls the translation, stability, and movements of hundreds of mRNAs in a single species. We probe the evolution of PUF-RNA networks by direct identification of the mRNAs bound to PUF proteins in budding and filamentous fungi and by computational analyses of orthologous RNAs from 62 fungal species. Our findings reveal that PUF proteins gain and lose mRNAs with related and emergent biological functions during evolution. We demonstrate at least two independent rewiring events for PUF3 orthologs, independent but convergent evolution of PUF4/5 binding specificity and the rewiring of the PUF4/5 regulons in different fungal lineages. These findings demonstrate plasticity in RNA regulatory networks and suggest ways in which their rewiring occurs.

  12. Inferring the conservative causal core of gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Emmert-Streib Frank

    2010-09-01

    Full Text Available Abstract Background Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. Results In this paper, we introduce a novel gene regulatory network inference (GRNI algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. Conclusions For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

  13. Inferring the conservative causal core of gene regulatory networks.

    Science.gov (United States)

    Altay, Gökmen; Emmert-Streib, Frank

    2010-09-28

    Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

  14. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Introduction. Over the past few years, network science has drawn attention from a large number of ... The qualitative properties of biological networks cannot ... Here, we study the underlying undirected structure of empirical biological networks.

  15. Fused Regression for Multi-source Gene Regulatory Network Inference.

    Directory of Open Access Journals (Sweden)

    Kari Y Lam

    2016-12-01

    Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

  16. SELANSI: a toolbox for simulation of stochastic gene regulatory networks.

    Science.gov (United States)

    Pájaro, Manuel; Otero-Muras, Irene; Vázquez, Carlos; Alonso, Antonio A

    2018-03-01

    Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi. antonio@iim.csic.es. © The Author(s) 2017. Published by Oxford University Press.

  17. Genomic analysis of the hierarchical structure of regulatory networks

    Science.gov (United States)

    Yu, Haiyuan; Gerstein, Mark

    2006-01-01

    A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace “chain-of-command” structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein–protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are “control bottlenecks” in the hierarchy, and this great degree of control for “middle managers” has parallels in efficient social structures in various corporate and governmental settings. PMID:17003135

  18. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Vipin Narang

    Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

  19. Mining biological networks from full-text articles.

    Science.gov (United States)

    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. Computational Genetic Regulatory Networks Evolvable, Self-organizing Systems

    CERN Document Server

    Knabe, Johannes F

    2013-01-01

    Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting fr...

  1. Deconstructing the pluripotency gene regulatory network

    KAUST Repository

    Li, Mo

    2018-04-04

    Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

  2. Deconstructing the pluripotency gene regulatory network

    KAUST Repository

    Li, Mo; Belmonte, Juan Carlos Izpisua

    2018-01-01

    Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

  3. Sequence-based model of gap gene regulatory network.

    Science.gov (United States)

    Kozlov, Konstantin; Gursky, Vitaly; Kulakovskiy, Ivan; Samsonova, Maria

    2014-01-01

    The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded. The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3

  4. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

    2015-01-01

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation

  5. An algebra-based method for inferring gene regulatory networks.

    Science.gov (United States)

    Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

    2014-03-26

    The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

  6. Applying differential dynamic logic to reconfigurable biological networks.

    Science.gov (United States)

    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.

  7. RMOD: a tool for regulatory motif detection in signaling network.

    Directory of Open Access Journals (Sweden)

    Jinki Kim

    Full Text Available Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

  8. Analysis of deterministic cyclic gene regulatory network models with delays

    CERN Document Server

    Ahsen, Mehmet Eren; Niculescu, Silviu-Iulian

    2015-01-01

    This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.

  9. Node fingerprinting: an efficient heuristic for aligning biological networks.

    Science.gov (United States)

    Radu, Alex; Charleston, Michael

    2014-10-01

    With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

  10. A parallel attractor-finding algorithm based on Boolean satisfiability for genetic regulatory networks.

    Directory of Open Access Journals (Sweden)

    Wensheng Guo

    Full Text Available In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.

  11. Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

    Science.gov (United States)

    Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

    2017-01-01

    The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

  12. On the Interplay between Entropy and Robustness of Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Bor-Sen Chen

    2010-05-01

    Full Text Available The interplay between entropy and robustness of gene network is a core mechanism of systems biology. The entropy is a measure of randomness or disorder of a physical system due to random parameter fluctuation and environmental noises in gene regulatory networks. The robustness of a gene regulatory network, which can be measured as the ability to tolerate the random parameter fluctuation and to attenuate the effect of environmental noise, will be discussed from the robust H∞ stabilization and filtering perspective. In this review, we will also discuss their balancing roles in evolution and potential applications in systems and synthetic biology.

  13. Trichomes: different regulatory networks lead to convergent structures.

    Science.gov (United States)

    Serna, Laura; Martin, Cathie

    2006-06-01

    Sometimes, proteins, biological structures or even organisms have similar functions and appearances but have evolved through widely divergent pathways. There is experimental evidence to suggest that different developmental pathways have converged to produce similar outgrowths of the aerial plant epidermis, referred to as trichomes. The emerging picture suggests that trichomes in Arabidopsis thaliana and, perhaps, in cotton develop through a transcriptional regulatory network that differs from those regulating trichome formation in Antirrhinum and Solanaceous species. Several lines of evidence suggest that the duplication of a gene controlling anthocyanin production and subsequent divergence might be the major force driving trichome formation in Arabidopsis, whereas the multicellular trichomes of Antirrhinum and Solanaceous species appear to have a different regulatory origin.

  14. Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

    Directory of Open Access Journals (Sweden)

    Masanao Sato

    Full Text Available Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2. This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i the components of the network are highly interconnected; and (ii negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector

  15. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

    Science.gov (United States)

    Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

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

    Science.gov (United States)

    Lee, Deok-Sun

    2014-11-01

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

  17. Recurrent neural network based hybrid model for reconstructing gene regulatory network.

    Science.gov (United States)

    Raza, Khalid; Alam, Mansaf

    2016-10-01

    One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Mapping biological systems to network systems

    CERN Document Server

    Rathore, Heena

    2016-01-01

    The book presents the challenges inherent in the paradigm shift of network systems from static to highly dynamic distributed systems – it proposes solutions that the symbiotic nature of biological systems can provide into altering networking systems to adapt to these changes. The author discuss how biological systems – which have the inherent capabilities of evolving, self-organizing, self-repairing and flourishing with time – are inspiring researchers to take opportunities from the biology domain and map them with the problems faced in network domain. The book revolves around the central idea of bio-inspired systems -- it begins by exploring why biology and computer network research are such a natural match. This is followed by presenting a broad overview of biologically inspired research in network systems -- it is classified by the biological field that inspired each topic and by the area of networking in which that topic lies. Each case elucidates how biological concepts have been most successfully ...

  19. The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks

    Directory of Open Access Journals (Sweden)

    Frank eEmmert-Streib

    2014-02-01

    Full Text Available In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of $351$ patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome $21$ is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

  20. Comparison of evolutionary algorithms in gene regulatory network model inference.

    LENUS (Irish Health Repository)

    2010-01-01

    ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

  1. Synchronous versus asynchronous modeling of gene regulatory networks.

    Science.gov (United States)

    Garg, Abhishek; Di Cara, Alessandro; Xenarios, Ioannis; Mendoza, Luis; De Micheli, Giovanni

    2008-09-01

    In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

  2. Small Rna Regulatory Networks In Pseudomonas Putida

    DEFF Research Database (Denmark)

    Bojanovic, Klara; Long, Katherine

    2015-01-01

    chemicals and has a potential to be used as an efficient cell factory for various products. P. putida KT2240 is a genome-sequenced strain and a well characterized pseudomonad. Our major aim is to identify small RNA molecules (sRNAs) and their regulatory networks. A previous study has identified 37 sRNAs...... in this strain, while in other pseudomonads many more sRNAs have been found so far.P. putida KT2440 has been grown in different conditions which are likely to be encountered in industrial fermentations with the aim of using sRNAs for generation of improved cell factories. For that, cells have been grown in LB......Pseudomonas putida is a ubiquitous Gram-negative soil bacterium with a versatile metabolism and ability to degrade various toxic compounds. It has a high tolerance to different future biobased building blocks and various other stringent conditions. It is used in industry to produce some important...

  3. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  4. Recombinant biologic products versus nutraceuticals from plants - a regulatory choice?

    Science.gov (United States)

    Drake, Pascal M W; Szeto, Tim H; Paul, Mathew J; Teh, Audrey Y-H; Ma, Julian K-C

    2017-01-01

    Biotechnology has transformed the potential for plants to be a manufacturing source of pharmaceutical compounds. Now, with transgenic and transient expression techniques, virtually any biologic, including vaccines and therapeutics, could be manufactured in plants. However, uncertainty over the regulatory path for such new pharmaceuticals has been a deterrent. Consideration has been given to using alternative regulatory paths, including those for nutraceuticals or cosmetic agents. This review will consider these possibilities, and discuss the difficulties in establishing regulatory guidelines for new pharmaceutical manufacturing technologies. © 2016 The British Pharmacological Society.

  5. Attentional Networks and Biological Motion

    Directory of Open Access Journals (Sweden)

    Chandramouli Chandrasekaran

    2010-03-01

    Full Text Available Our ability to see meaningful actions when presented with pointlight traces of human movement is commonly referred to as the perception of biological motion. While traditionalexplanations have emphasized the spontaneous and automatic nature of this ability, morerecent findings suggest that attention may play a larger role than is typically assumed. Intwo studies we show that the speed and accuracy of responding to point-light stimuli is highly correlated with the ability to control selective attention. In our first experiment we measured thresholds for determining the walking direction of a masked point-light figure, and performance on a range of attention-related tasks in the same set of observers. Mask-density thresholds for the direction discrimination task varied quite considerably from observer to observer and this variation was highly correlated with performance on both Stroop and flanker interference tasks. Other components of attention, such as orienting, alerting and visual search efficiency, showed no such relationship. In a second experiment, we examined the relationship between the ability to determine the orientation of unmasked point-light actions and Stroop interference, again finding a strong correlation. Our results are consistent with previous research suggesting that biological motion processing may requite attention, and specifically implicate networks of attention related to executive control and selection.

  6. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

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

    Directory of Open Access Journals (Sweden)

    Ponomarenko Julia

    2010-12-01

    Full Text Available Abstract 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.

  8. Delay-independent stability of genetic regulatory networks.

    Science.gov (United States)

    Wu, Fang-Xiang

    2011-11-01

    Genetic regulatory networks can be described by nonlinear differential equations with time delays. In this paper, we study both locally and globally delay-independent stability of genetic regulatory networks, taking messenger ribonucleic acid alternative splicing into consideration. Based on nonnegative matrix theory, we first develop necessary and sufficient conditions for locally delay-independent stability of genetic regulatory networks with multiple time delays. Compared to the previous results, these conditions are easy to verify. Then we develop sufficient conditions for global delay-independent stability for genetic regulatory networks. Compared to the previous results, this sufficient condition is less conservative. To illustrate theorems developed in this paper, we analyze delay-independent stability of two genetic regulatory networks: a real-life repressilatory network with three genes and three proteins, and a synthetic gene regulatory network with five genes and seven proteins. The simulation results show that the theorems developed in this paper can effectively determine the delay-independent stability of genetic regulatory networks.

  9. Self-sustained oscillations of complex genomic regulatory networks

    International Nuclear Information System (INIS)

    Ye Weiming; Huang Xiaodong; Huang Xuhui; Li Pengfei; Xia Qinzhi; Hu Gang

    2010-01-01

    Recently, self-sustained oscillations in complex networks consisting of non-oscillatory nodes have attracted great interest in diverse natural and social fields. Oscillatory genomic regulatory networks are one of the most typical examples of this kind. Given an oscillatory genomic network, it is important to reveal the central structure generating the oscillation. However, if the network consists of large numbers of genes and interactions, the oscillation generator is deeply hidden in the complicated interactions. We apply the dominant phase-advanced driving path method proposed in Qian et al. (2010) to reduce complex genomic regulatory networks to one-dimensional and unidirectionally linked network graphs where negative regulatory loops are explored to play as the central generators of the oscillations, and oscillation propagation pathways in the complex networks are clearly shown by tree branches radiating from the loops. Based on the above understanding we can control oscillations of genomic networks with high efficiency.

  10. Comparing biological networks via graph compression

    Directory of Open Access Journals (Sweden)

    Hayashida Morihiro

    2010-09-01

    Full Text Available Abstract 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.

  11. The Latin American Biological Dosimetry Network (LBDNet)

    International Nuclear Information System (INIS)

    Garcia, O.; Lamadrid, A.I.; Gonzalez, J.E.; Romero, I.; Mandina, T.; Di Giorgio, M.; Radl, A.; Taja, M.R.; Sapienza, C.E.; Deminge, M.M.; Fernandez Rearte, J.; Stuck Oliveira, M.; Valdivia, P.; Guerrero-Carbajal, C.; Arceo Maldonado, C.; Cortina Ramirez, G.E.; Espinoza, M.; Martinez-Lopez, W.; Di Tomasso, M.

    2016-01-01

    Biological Dosimetry is a necessary support for national radiation protection programmes and emergency response schemes. The Latin American Biological Dosimetry Network (LBDNet) was formally founded in 2007 to provide early biological dosimetry assistance in case of radiation emergencies in the Latin American Region. Here are presented the main topics considered in the foundational document of the network, which comprise: mission, partners, concept of operation, including the mechanism to request support for biological dosimetry assistance in the region, and the network capabilities. The process for network activation and the role of the coordinating laboratory during biological dosimetry emergency response is also presented. This information is preceded by historical remarks on biological dosimetry cooperation in Latin America. A summary of the main experimental and practical results already obtained by the LBDNet is also included. (authors)

  12. The Latin American Biological Dosimetry Network (LBDNet).

    Science.gov (United States)

    García, O; Di Giorgio, M; Radl, A; Taja, M R; Sapienza, C E; Deminge, M M; Fernández Rearte, J; Stuck Oliveira, M; Valdivia, P; Lamadrid, A I; González, J E; Romero, I; Mandina, T; Guerrero-Carbajal, C; ArceoMaldonado, C; Cortina Ramírez, G E; Espinoza, M; Martínez-López, W; Di Tomasso, M

    2016-09-01

    Biological Dosimetry is a necessary support for national radiation protection programmes and emergency response schemes. The Latin American Biological Dosimetry Network (LBDNet) was formally founded in 2007 to provide early biological dosimetry assistance in case of radiation emergencies in the Latin American Region. Here are presented the main topics considered in the foundational document of the network, which comprise: mission, partners, concept of operation, including the mechanism to request support for biological dosimetry assistance in the region, and the network capabilities. The process for network activation and the role of the coordinating laboratory during biological dosimetry emergency response is also presented. This information is preceded by historical remarks on biological dosimetry cooperation in Latin America. A summary of the main experimental and practical results already obtained by the LBDNet is also included. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  13. Network Reconstruction of Dynamic Biological Systems

    OpenAIRE

    Asadi, Behrang

    2013-01-01

    Inference of network topology from experimental data is a central endeavor in biology, since knowledge of the underlying signaling mechanisms a requirement for understanding biological phenomena. As one of the most important tools in bioinformatics area, development of methods to reconstruct biological networks has attracted remarkable attention in the current decade. Integration of different data types can lead to remarkable improvements in our ability to identify the connectivity of differe...

  14. Large-scale modeling of condition-specific gene regulatory networks by information integration and inference.

    Science.gov (United States)

    Ellwanger, Daniel Christian; Leonhardt, Jörn Florian; Mewes, Hans-Werner

    2014-12-01

    Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η(2) (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Takeshi Hase

    Full Text Available Elucidating gene regulatory network (GRN from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

  16. An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

    Science.gov (United States)

    Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

    2018-05-14

    A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives

  17. Evolution of Cis-Regulatory Elements and Regulatory Networks in Duplicated Genes of Arabidopsis.

    Science.gov (United States)

    Arsovski, Andrej A; Pradinuk, Julian; Guo, Xu Qiu; Wang, Sishuo; Adams, Keith L

    2015-12-01

    Plant genomes contain large numbers of duplicated genes that contribute to the evolution of new functions. Following duplication, genes can exhibit divergence in their coding sequence and their expression patterns. Changes in the cis-regulatory element landscape can result in changes in gene expression patterns. High-throughput methods developed recently can identify potential cis-regulatory elements on a genome-wide scale. Here, we use a recent comprehensive data set of DNase I sequencing-identified cis-regulatory binding sites (footprints) at single-base-pair resolution to compare binding sites and network connectivity in duplicated gene pairs in Arabidopsis (Arabidopsis thaliana). We found that duplicated gene pairs vary greatly in their cis-regulatory element architecture, resulting in changes in regulatory network connectivity. Whole-genome duplicates (WGDs) have approximately twice as many footprints in their promoters left by potential regulatory proteins than do tandem duplicates (TDs). The WGDs have a greater average number of footprint differences between paralogs than TDs. The footprints, in turn, result in more regulatory network connections between WGDs and other genes, forming denser, more complex regulatory networks than shown by TDs. When comparing regulatory connections between duplicates, WGDs had more pairs in which the two genes are either partially or fully diverged in their network connections, but fewer genes with no network connections than the TDs. There is evidence of younger TDs and WGDs having fewer unique connections compared with older duplicates. This study provides insights into cis-regulatory element evolution and network divergence in duplicated genes. © 2015 American Society of Plant Biologists. All Rights Reserved.

  18. Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

    Science.gov (United States)

    Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

    2014-12-01

    Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

  19. Global Analysis of Photosynthesis Transcriptional Regulatory Networks

    Science.gov (United States)

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.

    2014-01-01

    Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888), which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis. PMID:25503406

  20. Global analysis of photosynthesis transcriptional regulatory networks.

    Directory of Open Access Journals (Sweden)

    Saheed Imam

    2014-12-01

    Full Text Available Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888, which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis.

  1. Integrated Approach to Reconstruction of Microbial Regulatory Networks

    Energy Technology Data Exchange (ETDEWEB)

    Rodionov, Dmitry A [Sanford-Burnham Medical Research Institute; Novichkov, Pavel S [Lawrence Berkeley National Laboratory

    2013-11-04

    This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated in RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.

  2. An integer optimization algorithm for robust identification of non-linear gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Chemmangattuvalappil Nishanth

    2012-09-01

    Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

  3. The impact of measurement errors in the identification of regulatory networks

    Directory of Open Access Journals (Sweden)

    Sato João R

    2009-12-01

    Full Text Available Abstract Background There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent and non-time series (independent data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models and dependent (autoregressive models data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error. The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

  4. CoryneRegNet 4.0 – A reference database for corynebacterial gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Baumbach Jan

    2007-11-01

    transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB. Conclusion The release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at http://www.CoryneRegNet.DE.

  5. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo; Burger, Martin; Haskovec, Jan; Markowich, Peter A.; Schlottbom, Matthias

    2017-01-01

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes

  6. 4th IEA International CCS Regulatory Network Meeting

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2012-07-01

    On 9 and 10 May 2012, the IEA International CCS Regulatory Network (Network), launched in Paris in May 2008 to provide a neutral forum for CCS regulators, policy makers and stakeholders to share updates and views on CCS regulatory developments, held its fourth meeting at the International Energy Agency (IEA) offices in Paris, France. The aim of the meeting was to: provide an update on government efforts to develop and implement carbon capture and storage (CCS) legal and regulatory frameworks; and consider ways in which governments are dealing with some of the more difficult or complex aspects of CCS regulation. This report summarises the proceedings of the meeting.

  7. Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Liang Jinghang

    2012-08-01

    network inferred from a T cell immune response dataset. An SBN can also implement the function of an asynchronous PBN and is potentially useful in a hybrid approach in combination with a continuous or single-molecule level stochastic model. Conclusions Stochastic Boolean networks (SBNs are proposed as an efficient approach to modelling gene regulatory networks (GRNs. The SBN approach is able to recover biologically-proven regulatory behaviours, such as the oscillatory dynamics of the p53-Mdm2 network and the dynamic attractors in a T cell immune response network. The proposed approach can further predict the network dynamics when the genes are under perturbation, thus providing biologically meaningful insights for a better understanding of the dynamics of GRNs. The algorithms and methods described in this paper have been implemented in Matlab packages, which are attached as Additional files.

  8. Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks.

    Science.gov (United States)

    Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P; Gerstein, Mark

    2010-05-18

    The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers' continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.

  9. Transcription regulatory networks analysis using CAGE

    KAUST Repository

    Tegné r, Jesper N.; Bjö rkegren, Johan L M; Ravasi, Timothy; Bajic, Vladimir

    2009-01-01

    and the fine interplay between regulatory proteins and the promoter structure governing the combinatorial regulation of gene expression. In this chapter we review how the CAGE data can be integrated with other measurements such as expression, physical

  10. A Regulatory Network Analysis of Orphan Genes in Arabidopsis Thaliana

    Science.gov (United States)

    Singh, Pramesh; Chen, Tianlong; Arendsee, Zebulun; Wurtele, Eve S.; Bassler, Kevin E.

    Orphan genes, which are genes unique to each particular species, have recently drawn significant attention for their potential usefulness for organismal robustness. Their origin and regulatory interaction patterns remain largely undiscovered. Recently, methods that use the context likelihood of relatedness to infer a network followed by modularity maximizing community detection algorithms on the inferred network to find the functional structure of regulatory networks were shown to be effective. We apply improved versions of these methods to gene expression data from Arabidopsis thaliana, identify groups (clusters) of interacting genes with related patterns of expression and analyze the structure within those groups. Focusing on clusters that contain orphan genes, we compare the identified clusters to gene ontology (GO) terms, regulons, and pathway designations and analyze their hierarchical structure. We predict new regulatory interactions and unravel the structure of the regulatory interaction patterns of orphan genes. Work supported by the NSF through Grants DMR-1507371 and IOS-1546858.

  11. Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

    Directory of Open Access Journals (Sweden)

    Xiaosheng Wang

    2010-03-01

    Full Text Available Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

  12. Inference of cancer-specific gene regulatory networks using soft computing rules.

    Science.gov (United States)

    Wang, Xiaosheng; Gotoh, Osamu

    2010-03-24

    Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

  13. Finding gene regulatory network candidates using the gene expression knowledge base.

    Science.gov (United States)

    Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

    2014-12-10

    Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

  14. On the role of sparseness in the evolution of modularity in gene regulatory networks.

    Science.gov (United States)

    Espinosa-Soto, Carlos

    2018-05-01

    Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

  15. Learning a Markov Logic network for supervised gene regulatory network inference.

    Science.gov (United States)

    Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

    2013-09-12

    Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

  16. Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network

    Directory of Open Access Journals (Sweden)

    Barabási Albert-László

    2004-01-01

    Full Text Available Abstract Background Transcriptional regulation of cellular functions is carried out through a complex network of interactions among transcription factors and the promoter regions of genes and operons regulated by them.To better understand the system-level function of such networks simplification of their architecture was previously achieved by identifying the motifs present in the network, which are small, overrepresented, topologically distinct regulatory interaction patterns (subgraphs. However, the interaction of such motifs with each other, and their form of integration into the full network has not been previously examined. Results By studying the transcriptional regulatory network of the bacterium, Escherichia coli, we demonstrate that the two previously identified motif types in the network (i.e., feed-forward loops and bi-fan motifs do not exist in isolation, but rather aggregate into homologous motif clusters that largely overlap with known biological functions. Moreover, these clusters further coalesce into a supercluster, thus establishing distinct topological hierarchies that show global statistical properties similar to the whole network. Targeted removal of motif links disintegrates the network into small, isolated clusters, while random disruptions of equal number of links do not cause such an effect. Conclusion Individual motifs aggregate into homologous motif clusters and a supercluster forming the backbone of the E. coli transcriptional regulatory network and play a central role in defining its global topological organization.

  17. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

    Science.gov (United States)

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

    2012-01-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

  18. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

    Science.gov (United States)

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A; Kellis, Manolis

    2012-07-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

  19. Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks.

    Science.gov (United States)

    Tian, Ye; Zhang, Bai; Hoffman, Eric P; Clarke, Robert; Zhang, Zhen; Shih, Ie-Ming; Xuan, Jianhua; Herrington, David M; Wang, Yue

    2014-07-24

    Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context-specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential networks cannot be constructed by existing knowledge alone, novel incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. However, the major unresolved roadblocks include: big solution space but a small sample size; highly complex networks; imperfect prior knowledge; missing significance assessment; and heuristic structural parameter learning. To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to "random" knowledge. Based on that scheme, we developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results. The open-source R software package and the experimental data are freely available at http://www.cbil.ece.vt.edu/software.htm. Experiments on both synthetic and real data demonstrate the effectiveness of the knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological

  20. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

    Science.gov (United States)

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

  1. Robustness and accuracy in sea urchin developmental gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Smadar eBen-Tabou De-Leon

    2016-02-01

    Full Text Available Developmental gene regulatory networks robustly control the timely activation of regulatory and differentiation genes. The structure of these networks underlies their capacity to buffer intrinsic and extrinsic noise and maintain embryonic morphology. Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust control of cell fate decisions. The Wnt-βcatenin signaling pathway patterns the primary embryonic axis while the BMP signaling pathway patterns the secondary embryonic axis in the sea urchin embryo and across bilateria. Interestingly, in the sea urchin in both cases, the signaling pathway that defines the axis controls directly the expression of a set of downstream regulatory genes. I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell fate decision in the endoderm and in the dorsal ectoderm. The specification of the mesodermal pigment cell lineage is activated by Delta signaling that initiates a triple positive feedback loop that locks down the pigment specification state. I propose that the use of compound positive feedback circuitry provides the endodermal cells enough time to turn off mesodermal genes and ensures correct mesoderm vs. endoderm fate decision. Thus, I argue that understanding the control properties of repeatedly used regulatory architectures illuminates their role in embryogenesis and provides possible explanations to their resistance to evolutionary change.

  2. Network Analysis Tools: from biological networks to clusters and pathways.

    Science.gov (United States)

    Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

    2008-01-01

    Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

  3. Characterizing the topology of probabilistic biological networks.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-01-01

    Biological interactions are often uncertain events, that may or may not take place with some probability. This uncertainty leads to a massive number of alternative interaction topologies for each such network. The existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. In this paper, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. Using our mathematical representation, we develop a method that can accurately describe the degree distribution of such networks. We also take one more step and extend our method to accurately compute the joint-degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. Our method works quickly even for entire protein-protein interaction (PPI) networks. It also helps us find an adequate mathematical model using MLE. We perform a comparative study of node-degree and joint-degree distributions in two types of biological networks: the classical deterministic networks and the more flexible probabilistic networks. Our results confirm that power-law and log-normal models best describe degree distributions for both probabilistic and deterministic networks. Moreover, the inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected. We also show that probabilistic networks are more robust for node-degree distribution computation than the deterministic ones. all the data sets used, the software

  4. Information processing in the transcriptional regulatory network of yeast: Functional robustness

    Directory of Open Access Journals (Sweden)

    Dehmer Matthias

    2009-03-01

    Full Text Available Abstract Background Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism. Results In this paper we study the functional robustness of the transcriptional regulatory network of S. cerevisiae. We model the information processing in the network as a first order Markov chain and study the influence of single gene perturbations on the global, asymptotic communication among genes. Modification in the communication is measured by an information theoretic measure allowing to predict genes that are 'fragile' with respect to single gene knockouts. Our results demonstrate that the predicted set of fragile genes contains a statistically significant enrichment of so called essential genes that are experimentally found to be necessary to ensure vital yeast. Further, a structural analysis of the transcriptional regulatory network reveals that there are significant differences between fragile genes, hub genes and genes with a high betweenness centrality value. Conclusion Our study does not only demonstrate that a combination of graph theoretical, information theoretical and statistical methods leads to meaningful biological results but also that such methods allow to study information processing in gene networks instead of just their structural properties.

  5. Regulatory Holidays and Optimal Network Expansion

    NARCIS (Netherlands)

    Willems, Bert; Zwart, Gijsbert

    2016-01-01

    We model the optimal regulation of continuous, irreversible, capacity expansion, in a model in which the regulated network firm has private information about its capacity costs, investments need to be financed out of the firm’s cash flows from selling network access and demand is stochastic. If

  6. Exploring biological network structure with clustered random networks

    Directory of Open Access Journals (Sweden)

    Bansal Shweta

    2009-12-01

    Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in

  7. Discriminative topological features reveal biological network mechanisms

    Directory of Open Access Journals (Sweden)

    Levovitz Chaya

    2004-11-01

    Full Text Available Abstract Background Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. Results We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model. Conclusions Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.

  8. Integration of Bacterial Small RNAs in Regulatory Networks.

    Science.gov (United States)

    Nitzan, Mor; Rehani, Rotem; Margalit, Hanah

    2017-05-22

    Small RNAs (sRNAs) are central regulators of gene expression in bacteria, controlling target genes posttranscriptionally by base pairing with their mRNAs. sRNAs are involved in many cellular processes and have unique regulatory characteristics. In this review, we discuss the properties of regulation by sRNAs and how it differs from and combines with transcriptional regulation. We describe the global characteristics of the sRNA-target networks in bacteria using graph-theoretic approaches and review the local integration of sRNAs in mixed regulatory circuits, including feed-forward loops and their combinations, feedback loops, and circuits made of an sRNA and another regulator, both derived from the same transcript. Finally, we discuss the competition effects in posttranscriptional regulatory networks that may arise over shared targets, shared regulators, and shared resources and how they may lead to signal propagation across the network.

  9. Physical limits of feedback noise-suppression in biological networks

    International Nuclear Information System (INIS)

    Zhang, Jiajun; Yuan, Zhanjiang; Zhou, Tianshou

    2009-01-01

    Feedback is a ubiquitous control mechanism of biological networks, and has also been identified in a variety of regulatory systems and organisms. It has been shown that, for a given gain and with negligible intrinsic noise, negative feedback impairs noise buffering whereas positive feedback enhances noise buffering. We further investigate the influence of negative and positive feedback on noise in output signals by considering both intrinsic and extrinsic noise as well as operator noise. We find that, while maintaining the system sensitivity, either there exists a minimum of the output noise intensity corresponding to a biologically feasible feedback strength, or the output noise intensity is a monotonic function of feedback strength bounded by both biological and dynamical constraints. In both cases, feedback noise-suppression is physically limited. In other words, noise suppressed by negative or positive feedback cannot be reduced without limitation even in the case of slow transcription

  10. Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

    Science.gov (United States)

    Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar

    2017-08-01

    Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

  11. Biological and Environmental Research Network Requirements

    Energy Technology Data Exchange (ETDEWEB)

    Balaji, V. [Princeton Univ., NJ (United States). Earth Science Grid Federation (ESGF); Boden, Tom [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Cowley, Dave [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Dart, Eli [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Dattoria, Vince [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Desai, Narayan [Argonne National Lab. (ANL), Argonne, IL (United States); Egan, Rob [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Foster, Ian [Argonne National Lab. (ANL), Argonne, IL (United States); Goldstone, Robin [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Gregurick, Susan [U.S. Dept. of Energy, Washington, DC (United States). Biological Systems Science Division; Houghton, John [U.S. Dept. of Energy, Washington, DC (United States). Biological and Environmental Research (BER) Program; Izaurralde, Cesar [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Johnston, Bill [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Joseph, Renu [U.S. Dept. of Energy, Washington, DC (United States). Climate and Environmental Sciences Division; Kleese-van Dam, Kerstin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lipton, Mary [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Monga, Inder [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Pritchard, Matt [British Atmospheric Data Centre (BADC), Oxon (United Kingdom); Rotman, Lauren [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Strand, Gary [National Center for Atmospheric Research (NCAR), Boulder, CO (United States); Stuart, Cory [Argonne National Lab. (ANL), Argonne, IL (United States); Tatusova, Tatiana [National Inst. of Health (NIH), Bethesda, MD (United States); Tierney, Brian [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Thomas, Brian [Univ. of California, Berkeley, CA (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Zurawski, Jason [Internet2, Washington, DC (United States)

    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.

  12. Portrait of Candida Species Biofilm Regulatory Network Genes.

    Science.gov (United States)

    Araújo, Daniela; Henriques, Mariana; Silva, Sónia

    2017-01-01

    Most cases of candidiasis have been attributed to Candida albicans, but Candida glabrata, Candida parapsilosis and Candida tropicalis, designated as non-C. albicans Candida (NCAC), have been identified as frequent human pathogens. Moreover, Candida biofilms are an escalating clinical problem associated with significant rates of mortality. Biofilms have distinct developmental phases, including adhesion/colonisation, maturation and dispersal, controlled by complex regulatory networks. This review discusses recent advances regarding Candida species biofilm regulatory network genes, which are key components for candidiasis. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

    Directory of Open Access Journals (Sweden)

    Christian L Barrett

    2006-05-01

    Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.

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

    Science.gov (United States)

    Jalili, Mahdi

    2018-03-01

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

  15. Determining Regulatory Networks Governing the Differentiation of Embryonic Stem Cells to Pancreatic Lineage

    Science.gov (United States)

    Banerjee, Ipsita

    2009-03-01

    Knowledge of pathways governing cellular differentiation to specific phenotype will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of the cellular environment. With this aim, we have developed a novel method to reconstruct the underlying regulatory architecture of a differentiating cell population from discrete temporal gene expression data. We utilize an inherent feature of biological networks, that of sparsity, in formulating the network reconstruction problem as a bi-level mixed-integer programming problem. The formulation optimizes the network topology at the upper level and the network connectivity strength at the lower level. The method is first validated by in-silico data, before applying it to the complex system of embryonic stem (ES) cell differentiation. This formulation enables efficient identification of the underlying network topology which could accurately predict steps necessary for directing differentiation to subsequent stages. Concurrent experimental verification demonstrated excellent agreement with model prediction.

  16. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

    Science.gov (United States)

    Zhu, Mingzhu; Dahmen, Jeremy L; Stacey, Gary; Cheng, Jianlin

    2013-09-22

    High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.

  17. Dense module enumeration in biological networks

    Science.gov (United States)

    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.

  18. Dense module enumeration in biological networks

    International Nuclear Information System (INIS)

    Tsuda, Koji; Georgii, Elisabeth

    2009-01-01

    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.

  19. Learning gene regulatory networks from only positive and unlabeled data

    Directory of Open Access Journals (Sweden)

    Elkan Charles

    2010-05-01

    Full Text Available Abstract Background Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact. Results A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement. Conclusions Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

  20. Discriminating response groups in metabolic and regulatory pathway networks.

    Science.gov (United States)

    Van Hemert, John L; Dickerson, Julie A

    2012-04-01

    Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events. The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies. We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material. julied@iastate.edu Supplementary data are available at Bioinformatics online.

  1. The capacity for multistability in small gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Grotewold Erich

    2009-09-01

    Full Text Available Abstract Background Recent years have seen a dramatic increase in the use of mathematical modeling to gain insight into gene regulatory network behavior across many different organisms. In particular, there has been considerable interest in using mathematical tools to understand how multistable regulatory networks may contribute to developmental processes such as cell fate determination. Indeed, such a network may subserve the formation of unicellular leaf hairs (trichomes in the model plant Arabidopsis thaliana. Results In order to investigate the capacity of small gene regulatory networks to generate multiple equilibria, we present a chemical reaction network (CRN-based modeling formalism and describe a number of methods for CRN analysis in a parameter-free context. These methods are compared and applied to a full set of one-component subnetworks, as well as a large random sample from 40,680 similarly constructed two-component subnetworks. We find that positive feedback and cooperativity mediated by transcription factor (TF dimerization is a requirement for one-component subnetwork bistability. For subnetworks with two components, the presence of these processes increases the probability that a randomly sampled subnetwork will exhibit multiple equilibria, although we find several examples of bistable two-component subnetworks that do not involve cooperative TF-promoter binding. In the specific case of epidermal differentiation in Arabidopsis, dimerization of the GL3-GL1 complex and cooperative sequential binding of GL3-GL1 to the CPC promoter are each independently sufficient for bistability. Conclusion Computational methods utilizing CRN-specific theorems to rule out bistability in small gene regulatory networks are far superior to techniques generally applicable to deterministic ODE systems. Using these methods to conduct an unbiased survey of parameter-free deterministic models of small networks, and the Arabidopsis epidermal cell

  2. Empirical Bayes conditional independence graphs for regulatory network recovery

    Science.gov (United States)

    Mahdi, Rami; Madduri, Abishek S.; Wang, Guoqing; Strulovici-Barel, Yael; Salit, Jacqueline; Hackett, Neil R.; Crystal, Ronald G.; Mezey, Jason G.

    2012-01-01

    Motivation: Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods. Methods: We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures. Results: Using in silico data, we show that ELMM has better performance than commonly used network inference algorithms including GeneNet, ARACNE, FOCI, GENIE3 and GLASSO. We also apply ELMM to reconstruct a network among 5492 genes expressed in human lung airway epithelium of healthy non-smokers, healthy smokers and individuals with chronic obstructive pulmonary disease assayed using microarrays. The analysis identifies dense sub-networks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress and secretion. Availability and implementation: Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx. Contact: ramimahdi@yahoo.com or jgm45@cornell.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22685074

  3. Dose response relationship in anti-stress gene regulatory networks.

    Science.gov (United States)

    Zhang, Qiang; Andersen, Melvin E

    2007-03-02

    To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on

  4. Dose response relationship in anti-stress gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2007-03-01

    Full Text Available To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear depends on changes in the specific values of local response coefficients (gains distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear

  5. Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice

    Directory of Open Access Journals (Sweden)

    Shuchi eSmita

    2015-12-01

    Full Text Available MYB transcription factor (TF is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by top down and guide gene approaches. More than 50% of OsMYBs were strongly correlated under fifty experimental conditions with 51 hub genes via top down approach. Further, clusters were identified using Markov Clustering (MCL. To maximize the clustering performance, parameter evaluation of the MCL inflation score (I was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by guide gene approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought

  6. GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML.

    Science.gov (United States)

    Vlaic, Sebastian; Hoffmann, Bianca; Kupfer, Peter; Weber, Michael; Dräger, Andreas

    2013-09-01

    GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage. GRN2SBML is freely available under the GNU Public License version 3 and can be downloaded from http://www.hki-jena.de/index.php/0/2/490. General information on GRN2SBML, examples and tutorials are available at the tool's web page.

  7. Network perturbation by recurrent regulatory variants in cancer.

    Directory of Open Access Journals (Sweden)

    Kiwon Jang

    2017-03-01

    Full Text Available Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes.

  8. Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

    KAUST Repository

    Fujii, Chisato

    2015-04-16

    Gene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and non-linear which makes learning the networks a challenging problem and existing gene network inference methods do not give consistent results. Current state-of-the-art study uses the average-ranking-based consensus method to combine and average the ranked predictions from individual methods. However each individual method has an equal contribution to the consen- sus prediction. We have developed a linear programming-based consensus approach which uses learned weights from linear programming among individual methods such that the methods have di↵erent weights depending on their performance. Our result reveals that assigning di↵erent weights to individual methods rather than giving them equal weights improves the performance of the consensus. The linear programming- based consensus method is evaluated and it had the best performance on in silico and Saccharomyces cerevisiae networks, and the second best on the Escherichia coli network outperformed by Inferelator Pipeline method which gives inconsistent results across a wide range of microarray data sets.

  9. Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

    Directory of Open Access Journals (Sweden)

    Shuo Gu

    2017-01-01

    Full Text Available With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

  10. Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective.

    Science.gov (United States)

    Gu, Shuo; Pei, Jianfeng

    2017-01-01

    With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

  11. Characterizing Topology of Probabilistic Biological Networks.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-09-06

    Biological interactions are often uncertain events, that may or may not take place with some probability. Existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. Here, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. We develop a method that accurately describes the degree distribution of such networks. We also extend our method to accurately compute the joint degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. It also helps us find an adequate mathematical model using maximum likelihood estimation. Our results demonstrate that power law and log-normal models best describe degree distributions for probabilistic networks. The inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected.

  12. Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.

    Directory of Open Access Journals (Sweden)

    Yinyin Yuan

    Full Text Available Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/.

  13. Do motifs reflect evolved function?--No convergent evolution of genetic regulatory network subgraph topologies.

    Science.gov (United States)

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

    2008-01-01

    Methods that analyse the topological structure of networks have recently become quite popular. Whether motifs (subgraph patterns that occur more often than in randomized networks) have specific functions as elementary computational circuits has been cause for debate. As the question is difficult to resolve with currently available biological data, we approach the issue using networks that abstractly model natural genetic regulatory networks (GRNs) which are evolved to show dynamical behaviors. Specifically one group of networks was evolved to be capable of exhibiting two different behaviors ("differentiation") in contrast to a group with a single target behavior. In both groups we find motif distribution differences within the groups to be larger than differences between them, indicating that evolutionary niches (target functions) do not necessarily mold network structure uniquely. These results show that variability operators can have a stronger influence on network topologies than selection pressures, especially when many topologies can create similar dynamics. Moreover, analysis of motif functional relevance by lesioning did not suggest that motifs were of greater importance to the functioning of the network than arbitrary subgraph patterns. Only when drastically restricting network size, so that one motif corresponds to a whole functionally evolved network, was preference for particular connection patterns found. This suggests that in non-restricted, bigger networks, entanglement with the rest of the network hinders topological subgraph analysis.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  15. Regulatory variation: an emerging vantage point for cancer biology.

    Science.gov (United States)

    Li, Luolan; Lorzadeh, Alireza; Hirst, Martin

    2014-01-01

    Transcriptional regulation involves complex and interdependent interactions of noncoding and coding regions of the genome with proteins that interact and modify them. Genetic variation/mutation in coding and noncoding regions of the genome can drive aberrant transcription and disease. In spite of accounting for nearly 98% of the genome comparatively little is known about the contribution of noncoding DNA elements to disease. Genome-wide association studies of complex human diseases including cancer have revealed enrichment for variants in the noncoding genome. A striking finding of recent cancer genome re-sequencing efforts has been the previously underappreciated frequency of mutations in epigenetic modifiers across a wide range of cancer types. Taken together these results point to the importance of dysregulation in transcriptional regulatory control in genesis of cancer. Powered by recent technological advancements in functional genomic profiling, exploration of normal and transformed regulatory networks will provide novel insight into the initiation and progression of cancer and open new windows to future prognostic and diagnostic tools. © 2013 Wiley Periodicals, Inc.

  16. Ground rules of the pluripotency gene regulatory network.

    KAUST Repository

    Li, Mo

    2017-01-03

    Pluripotency is a state that exists transiently in the early embryo and, remarkably, can be recapitulated in vitro by deriving embryonic stem cells or by reprogramming somatic cells to become induced pluripotent stem cells. The state of pluripotency, which is stabilized by an interconnected network of pluripotency-associated genes, integrates external signals and exerts control over the decision between self-renewal and differentiation at the transcriptional, post-transcriptional and epigenetic levels. Recent evidence of alternative pluripotency states indicates the regulatory flexibility of this network. Insights into the underlying principles of the pluripotency network may provide unprecedented opportunities for studying development and for regenerative medicine.

  17. Ground rules of the pluripotency gene regulatory network.

    KAUST Repository

    Li, Mo; Belmonte, Juan Carlos Izpisua

    2017-01-01

    Pluripotency is a state that exists transiently in the early embryo and, remarkably, can be recapitulated in vitro by deriving embryonic stem cells or by reprogramming somatic cells to become induced pluripotent stem cells. The state of pluripotency, which is stabilized by an interconnected network of pluripotency-associated genes, integrates external signals and exerts control over the decision between self-renewal and differentiation at the transcriptional, post-transcriptional and epigenetic levels. Recent evidence of alternative pluripotency states indicates the regulatory flexibility of this network. Insights into the underlying principles of the pluripotency network may provide unprecedented opportunities for studying development and for regenerative medicine.

  18. On the dynamics of a gene regulatory network

    International Nuclear Information System (INIS)

    Grammaticos, B; Carstea, A S; Ramani, A

    2006-01-01

    We examine the dynamics of a network of genes focusing on a periodic chain of genes, of arbitrary length. We show that within a given class of sigmoids representing the equilibrium probability of the binding of the RNA polymerase to the core promoter, the system possesses a single stable fixed point. By slightly modifying the sigmoid, introducing 'stiffer' forms, we show that it is possible to find network configurations exhibiting bistable behaviour. Our results do not depend crucially on the length of the chain considered: calculations with finite chains lead to similar results. However, a realistic study of regulatory genetic networks would require the consideration of more complex topologies and interactions

  19. CellNet: Network Biology Applied to Stem Cell Engineering

    Science.gov (United States)

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

    2014-01-01

    SUMMARY 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. PMID:25126793

  20. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    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.

  1. Gene regulatory networks elucidating huanglongbing disease mechanisms.

    Directory of Open Access Journals (Sweden)

    Federico Martinelli

    Full Text Available Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas, especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation, sucrose metabolism (upregulation, and starch biosynthesis (upregulation. In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70 was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur.

  2. Biological impacts and context of network theory

    Energy Technology Data Exchange (ETDEWEB)

    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.

  3. Network biology concepts in complex disease comorbidities

    DEFF Research Database (Denmark)

    Hu, Jessica Xin; Thomas, Cecilia Engel; Brunak, Søren

    2016-01-01

    collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge......The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being...

  4. A gene regulatory network armature for T-lymphocyte specification

    Energy Technology Data Exchange (ETDEWEB)

    Fung, Elizabeth-sharon [Los Alamos National Laboratory

    2008-01-01

    Choice of a T-lymphoid fate by hematopoietic progenitor cells depends on sustained Notch-Delta signaling combined with tightly-regulated activities of multiple transcription factors. To dissect the regulatory network connections that mediate this process, we have used high-resolution analysis of regulatory gene expression trajectories from the beginning to the end of specification; tests of the short-term Notchdependence of these gene expression changes; and perturbation analyses of the effects of overexpression of two essential transcription factors, namely PU.l and GATA-3. Quantitative expression measurements of >50 transcription factor and marker genes have been used to derive the principal components of regulatory change through which T-cell precursors progress from primitive multipotency to T-lineage commitment. Distinct parts of the path reveal separate contributions of Notch signaling, GATA-3 activity, and downregulation of PU.l. Using BioTapestry, the results have been assembled into a draft gene regulatory network for the specification of T-cell precursors and the choice of T as opposed to myeloid dendritic or mast-cell fates. This network also accommodates effects of E proteins and mutual repression circuits of Gfil against Egr-2 and of TCF-l against PU.l as proposed elsewhere, but requires additional functions that remain unidentified. Distinctive features of this network structure include the intense dose-dependence of GATA-3 effects; the gene-specific modulation of PU.l activity based on Notch activity; the lack of direct opposition between PU.l and GATA-3; and the need for a distinct, late-acting repressive function or functions to extinguish stem and progenitor-derived regulatory gene expression.

  5. Elucidating MicroRNA Regulatory Networks Using Transcriptional, Post-transcriptional, and Histone Modification Measurements

    Directory of Open Access Journals (Sweden)

    Sara J.C. Gosline

    2016-01-01

    Full Text Available MicroRNAs (miRNAs regulate diverse biological processes by repressing mRNAs, but their modest effects on direct targets, together with their participation in larger regulatory networks, make it challenging to delineate miRNA-mediated effects. Here, we describe an approach to characterizing miRNA-regulatory networks by systematically profiling transcriptional, post-transcriptional and epigenetic activity in a pair of isogenic murine fibroblast cell lines with and without Dicer expression. By RNA sequencing (RNA-seq and CLIP (crosslinking followed by immunoprecipitation sequencing (CLIP-seq, we found that most of the changes induced by global miRNA loss occur at the level of transcription. We then introduced a network modeling approach that integrated these data with epigenetic data to identify specific miRNA-regulated transcription factors that explain the impact of miRNA perturbation on gene expression. In total, we demonstrate that combining multiple genome-wide datasets spanning diverse regulatory modes enables accurate delineation of the downstream miRNA-regulated transcriptional network and establishes a model for studying similar networks in other systems.

  6. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

    Directory of Open Access Journals (Sweden)

    Xiaodong Cai

    Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

  7. Social traits, social networks and evolutionary biology.

    Science.gov (United States)

    Fisher, D N; McAdam, A G

    2017-12-01

    effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

  8. LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni

    Directory of Open Access Journals (Sweden)

    Priyanka Patel

    2016-03-01

    Full Text Available A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level. Keywords: L.major, S.mansoni, Regulatory networks, Transcription factors, Database

  9. Exploring the miRNA regulatory network using evolutionary correlations.

    Directory of Open Access Journals (Sweden)

    Benedikt Obermayer

    2014-10-01

    Full Text Available Post-transcriptional regulation by miRNAs is a widespread and highly conserved phenomenon in metazoans, with several hundreds to thousands of conserved binding sites for each miRNA, and up to two thirds of all genes under miRNA regulation. At the same time, the effect of miRNA regulation on mRNA and protein levels is usually quite modest and associated phenotypes are often weak or subtle. This has given rise to the notion that the highly interconnected miRNA regulatory network exerts its function less through any individual link and more via collective effects that lead to a functional interdependence of network links. We present a Bayesian framework to quantify conservation of miRNA target sites using vertebrate whole-genome alignments. The increased statistical power of our phylogenetic model allows detection of evolutionary correlation in the conservation patterns of site pairs. Such correlations could result from collective functions in the regulatory network. For instance, co-conservation of target site pairs supports a selective benefit of combinatorial regulation by multiple miRNAs. We find that some miRNA families are under pronounced co-targeting constraints, indicating a high connectivity in the regulatory network, while others appear to function in a more isolated way. By analyzing coordinated targeting of different curated gene sets, we observe distinct evolutionary signatures for protein complexes and signaling pathways that could reflect differences in control strategies. Our method is easily scalable to analyze upcoming larger data sets, and readily adaptable to detect high-level selective constraints between other genomic loci. We thus provide a proof-of-principle method to understand regulatory networks from an evolutionary perspective.

  10. Novel topological descriptors for analyzing biological networks

    Directory of Open Access Journals (Sweden)

    Varmuza Kurt K

    2010-06-01

    Full Text Available Abstract Background Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information. Results In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem. Conclusions Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.

  11. Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques

    Directory of Open Access Journals (Sweden)

    Wei Kong

    2014-01-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA, which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.

  12. Predictive minimum description length principle approach to inferring gene regulatory networks.

    Science.gov (United States)

    Chaitankar, Vijender; Zhang, Chaoyang; Ghosh, Preetam; Gong, Ping; Perkins, Edward J; Deng, Youping

    2011-01-01

    Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold that defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we propose a new inference algorithm that incorporates mutual information (MI), conditional mutual information (CMI), and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm is evaluated using both synthetic time series data sets and a biological time series data set (Saccharomyces cerevisiae). The results show that the proposed algorithm produced fewer false edges and significantly improved the precision when compared to existing MDL algorithm.

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

    Science.gov (United States)

    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.

  14. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo

    2017-04-10

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.

  15. Small RNA-Controlled Gene Regulatory Networks in Pseudomonas putida

    DEFF Research Database (Denmark)

    Bojanovic, Klara

    evolved numerous mechanisms to controlgene expression in response to specific environmental signals. In addition to two-component systems, small regulatory RNAs (sRNAs) have emerged as major regulators of gene expression. The majority of sRNAs bind to mRNA and regulate their expression. They often have...... multiple targets and are incorporated into large regulatory networks and the RNA chaper one Hfq in many cases facilitates interactions between sRNAs and their targets. Some sRNAs also act by binding to protein targets and sequestering their function. In this PhD thesis we investigated the transcriptional....... Detailed insights into the mechanisms through which P. putida responds to different stress conditions and increased understanding of bacterial adaptation in natural and industrial settings were gained. Additionally, we identified genome-wide transcription start sites, andmany regulatory RNA elements...

  16. Leveraging network utility management practices for regulatory purposes

    International Nuclear Information System (INIS)

    2009-11-01

    Electric utilities around the globe are entering a phase where they must modernize and implement smart grid technologies. In order to optimize system architecture, asset replacement, and future operating costs, it the utilities must implement robust and flexible asset management structures. This report discussed the ways in which regulators assess investment plans. It focused on the implicit or explicit use of an asset management approach, including principles; processes; input and outputs; decision-making criteria and prioritization methods. The Ontario Energy Board staff were familiarized with the principles and objectives of established and emerging asset management processes and underlying analytic processes, systems and tools in order to ensure that investment information provided by network utilities regarding rates and other applications could be evaluated effectively. Specifically, the report discussed the need for and importance of asset management and provided further details of international markets and their regulatory approaches to asset management. The report also discussed regulatory approaches for review of asset management underlying investment plans as well as an overview of international regulatory practice for review of network utility asset management. It was concluded that options for strengthening regulatory guidance and assessment included utilizing appropriate and effective benchmarking to assess, promote and provide incentives for best practices and steer clear of the potential perverse incentives. 21 tabs., 17 figs., 1 appendix.

  17. Network Biology (http://www.iaees.org/publications/journals/nb/online-version.asp

    Directory of Open Access Journals (Sweden)

    networkbiology@iaees.org

    Full Text Available Network Biology ISSN 2220-8879 URL: http://www.iaees.org/publications/journals/nb/online-version.asp RSS: http://www.iaees.org/publications/journals/nb/rss.xml E-mail: networkbiology@iaees.org Editor-in-Chief: WenJun Zhang Aims and Scope NETWORK BIOLOGY (ISSN 2220-8879; CODEN NBEICS is an open access, peer-reviewed international journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology. It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to: •Theories, algorithms and programs of network analysis •Innovations and applications of biological networks •Ecological networks, food webs and natural equilibrium •Co-evolution, co-extinction, biodiversity conservation •Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs •Physiological networksNetwork regulation of metabolic processes, human diseases and ecological systems •Social networks, epidemiological networks •System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, and other network-based modeling, etc. We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc. Authors can submit their works to the email box of this journal, networkbiology@iaees.org. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal

  18. Developmental evolution in social insects: regulatory networks from genes to societies.

    Science.gov (United States)

    Linksvayer, Timothy A; Fewell, Jennifer H; Gadau, Jürgen; Laubichler, Manfred D

    2012-05-01

    The evolution and development of complex phenotypes in social insect colonies, such as queen-worker dimorphism or division of labor, can, in our opinion, only be fully understood within an expanded mechanistic framework of Developmental Evolution. Conversely, social insects offer a fertile research area in which fundamental questions of Developmental Evolution can be addressed empirically. We review the concept of gene regulatory networks (GRNs) that aims to fully describe the battery of interacting genomic modules that are differentially expressed during the development of individual organisms. We discuss how distinct types of network models have been used to study different levels of biological organization in social insects, from GRNs to social networks. We propose that these hierarchical networks spanning different organizational levels from genes to societies should be integrated and incorporated into full GRN models to elucidate the evolutionary and developmental mechanisms underlying social insect phenotypes. Finally, we discuss prospects and approaches to achieve such an integration. © 2012 WILEY PERIODICALS, INC.

  19. Memory functions reveal structural properties of gene regulatory networks

    Science.gov (United States)

    Perez-Carrasco, Ruben

    2018-01-01

    Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs. PMID:29470492

  20. Computing chemical organizations in biological networks.

    Science.gov (United States)

    Centler, Florian; Kaleta, Christoph; di Fenizio, Pietro Speroni; Dittrich, Peter

    2008-07-15

    Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states. We present three algorithms to compute the hierarchy of organizations for network models provided in SBML format. Two of them compute the complete organization hierarchy, while the third one uses heuristics to obtain a subset of all organizations for large models. While the constructive approach computes the hierarchy starting from the smallest organization in a bottom-up fashion, the flux-based approach employs self-maintaining flux distributions to determine organizations. A runtime comparison on 16 different network models of natural systems showed that none of the two exhaustive algorithms is superior in all cases. Studying a 'genome-scale' network model with 762 species and 1193 reactions, we demonstrate how the organization hierarchy helps to uncover the model structure and allows to evaluate the model's quality, for example by detecting components and subsystems of the model whose maintenance is not explained by the model. All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.

  1. Inferring regulatory networks from expression data using tree-based methods.

    Directory of Open Access Journals (Sweden)

    Vân Anh Huynh-Thu

    2010-09-01

    Full Text Available One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene is predicted from the expression patterns of all the other genes (input genes, using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.

  2. Singular Perturbation Analysis and Gene Regulatory Networks with Delay

    Science.gov (United States)

    Shlykova, Irina; Ponosov, Arcady

    2009-09-01

    There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

  3. Learning and coding in biological neural networks

    Science.gov (United States)

    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

  4. A developmental systems perspective on epistasis: computational exploration of mutational interactions in model developmental regulatory networks.

    Directory of Open Access Journals (Sweden)

    Jayson Gutiérrez

    2009-09-01

    Full Text Available The way in which the information contained in genotypes is translated into complex phenotypic traits (i.e. embryonic expression patterns depends on its decoding by a multilayered hierarchy of biomolecular systems (regulatory networks. Each layer of this hierarchy displays its own regulatory schemes (i.e. operational rules such as +/- feedback and associated control parameters, resulting in characteristic variational constraints. This process can be conceptualized as a mapping issue, and in the context of highly-dimensional genotype-phenotype mappings (GPMs epistatic events have been shown to be ubiquitous, manifested in non-linear correspondences between changes in the genotype and their phenotypic effects. In this study I concentrate on epistatic phenomena pervading levels of biological organization above the genetic material, more specifically the realm of molecular networks. At this level, systems approaches to studying GPMs are specially suitable to shed light on the mechanistic basis of epistatic phenomena. To this aim, I constructed and analyzed ensembles of highly-modular (fully interconnected networks with distinctive topologies, each displaying dynamic behaviors that were categorized as either arbitrary or functional according to early patterning processes in the Drosophila embryo. Spatio-temporal expression trajectories in virtual syncytial embryos were simulated via reaction-diffusion models. My in silico mutational experiments show that: 1 the average fitness decay tendency to successively accumulated mutations in ensembles of functional networks indicates the prevalence of positive epistasis, whereas in ensembles of arbitrary networks negative epistasis is the dominant tendency; and 2 the evaluation of epistatic coefficients of diverse interaction orders indicates that, both positive and negative epistasis are more prevalent in functional networks than in arbitrary ones. Overall, I conclude that the phenotypic and fitness effects of

  5. Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

    Directory of Open Access Journals (Sweden)

    Jacques Demongeot

    2018-01-01

    Full Text Available Networks used in biological applications at different scales (molecule, cell and population are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system as well as in their discrete Boolean versions (e.g., non-linear Hopfield system; in both cases, the notion of interaction graph G(J associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J, kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i attractor entropy, (ii isochronal entropy and (iii entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.

  6. Characterization of WRKY co-regulatory networks in rice and Arabidopsis

    Directory of Open Access Journals (Sweden)

    Kikuchi Shoshi

    2009-09-01

    Full Text Available Abstract Background The WRKY transcription factor gene family has a very ancient origin and has undergone extensive duplications in the plant kingdom. Several studies have pointed out their involvement in a range of biological processes, revealing that a large number of WRKY genes are transcriptionally regulated under conditions of biotic and/or abiotic stress. To investigate the existence of WRKY co-regulatory networks in plants, a whole gene family WRKYs expression study was carried out in rice (Oryza sativa. This analysis was extended to Arabidopsis thaliana taking advantage of an extensive repository of gene expression data. Results The presented results suggested that 24 members of the rice WRKY gene family (22% of the total were differentially-regulated in response to at least one of the stress conditions tested. We defined the existence of nine OsWRKY gene clusters comprising both phylogenetically related and unrelated genes that were significantly co-expressed, suggesting that specific sets of WRKY genes might act in co-regulatory networks. This hypothesis was tested by Pearson Correlation Coefficient analysis of the Arabidopsis WRKY gene family in a large set of Affymetrix microarray experiments. AtWRKYs were found to belong to two main co-regulatory networks (COR-A, COR-B and two smaller ones (COR-C and COR-D, all including genes belonging to distinct phylogenetic groups. The COR-A network contained several AtWRKY genes known to be involved mostly in response to pathogens, whose physical and/or genetic interaction was experimentally proven. We also showed that specific co-regulatory networks were conserved between the two model species by identifying Arabidopsis orthologs of the co-expressed OsWRKY genes. Conclusion In this work we identified sets of co-expressed WRKY genes in both rice and Arabidopsis that are functionally likely to cooperate in the same signal transduction pathways. We propose that, making use of data from co-regulatory

  7. Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

    Science.gov (United States)

    Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

    2017-10-01

    The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

  8. Statistical identification of gene association by CID in application of constructing ER regulatory network

    Directory of Open Access Journals (Sweden)

    Lien Huang-Chun

    2009-03-01

    Full Text Available Abstract Background A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID, is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs (X and their downstream genes (Y based on clinical data. More specifically, we use estrogen receptor α (ERα as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A. Results The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC, Student's t-test (STT, coefficient of determination (CoD, and mutual information (MI. When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y against a discrete variable (X, it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. Conclusion CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the

  9. Regulatory Compliance in Multi-Tier Supplier Networks

    Science.gov (United States)

    Goossen, Emray R.; Buster, Duke A.

    2014-01-01

    Over the years, avionics systems have increased in complexity to the point where 1st tier suppliers to an aircraft OEM find it financially beneficial to outsource designs of subsystems to 2nd tier and at times to 3rd tier suppliers. Combined with challenging schedule and budgetary pressures, the environment in which safety-critical systems are being developed introduces new hurdles for regulatory agencies and industry. This new environment of both complex systems and tiered development has raised concerns in the ability of the designers to ensure safety considerations are fully addressed throughout the tier levels. This has also raised questions about the sufficiency of current regulatory guidance to ensure: proper flow down of safety awareness, avionics application understanding at the lower tiers, OEM and 1st tier oversight practices, and capabilities of lower tier suppliers. Therefore, NASA established a research project to address Regulatory Compliance in a Multi-tier Supplier Network. This research was divided into three major study efforts: 1. Describe Modern Multi-tier Avionics Development 2. Identify Current Issues in Achieving Safety and Regulatory Compliance 3. Short-term/Long-term Recommendations Toward Higher Assurance Confidence This report presents our findings of the risks, weaknesses, and our recommendations. It also includes a collection of industry-identified risks, an assessment of guideline weaknesses related to multi-tier development of complex avionics systems, and a postulation of potential modifications to guidelines to close the identified risks and weaknesses.

  10. Data-driven integration of genome-scale regulatory and metabolic network models

    Science.gov (United States)

    Imam, Saheed; Schäuble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

    2015-01-01

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert—a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system. PMID:25999934

  11. Data-driven integration of genome-scale regulatory and metabolic network models

    Directory of Open Access Journals (Sweden)

    Saheed eImam

    2015-05-01

    Full Text Available Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription and signaling have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert – a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

  12. Model checking optimal finite-horizon control for probabilistic gene regulatory networks.

    Science.gov (United States)

    Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan

    2017-12-14

    Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

  13. Reconstruction of biological networks based on life science data integration

    Directory of Open Access Journals (Sweden)

    Kormeier Benjamin

    2010-06-01

    Full Text Available For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  14. Reconstruction of biological networks based on life science data integration.

    Science.gov (United States)

    Kormeier, Benjamin; Hippe, Klaus; Arrigo, Patrizio; Töpel, Thoralf; Janowski, Sebastian; Hofestädt, Ralf

    2010-10-27

    For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  15. Inferring the role of transcription factors in regulatory networks

    Directory of Open Access Journals (Sweden)

    Le Borgne Michel

    2008-05-01

    Full Text Available Abstract Background Expression profiles obtained from multiple perturbation experiments are increasingly used to reconstruct transcriptional regulatory networks, from well studied, simple organisms up to higher eukaryotes. Admittedly, a key ingredient in developing a reconstruction method is its ability to integrate heterogeneous sources of information, as well as to comply with practical observability issues: measurements can be scarce or noisy. In this work, we show how to combine a network of genetic regulations with a set of expression profiles, in order to infer the functional effect of the regulations, as inducer or repressor. Our approach is based on a consistency rule between a network and the signs of variation given by expression arrays. Results We evaluate our approach in several settings of increasing complexity. First, we generate artificial expression data on a transcriptional network of E. coli extracted from the literature (1529 nodes and 3802 edges, and we estimate that 30% of the regulations can be annotated with about 30 profiles. We additionally prove that at most 40.8% of the network can be inferred using our approach. Second, we use this network in order to validate the predictions obtained with a compendium of real expression profiles. We describe a filtering algorithm that generates particularly reliable predictions. Finally, we apply our inference approach to S. cerevisiae transcriptional network (2419 nodes and 4344 interactions, by combining ChIP-chip data and 15 expression profiles. We are able to detect and isolate inconsistencies between the expression profiles and a significant portion of the model (15% of all the interactions. In addition, we report predictions for 14.5% of all interactions. Conclusion Our approach does not require accurate expression levels nor times series. Nevertheless, we show on both data, real and artificial, that a relatively small number of perturbation experiments are enough to determine

  16. A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-01-01

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional

  17. Organization of excitable dynamics in hierarchical biological networks.

    Directory of Open Access Journals (Sweden)

    Mark Müller-Linow

    Full Text Available 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.

  18. Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration.

    Directory of Open Access Journals (Sweden)

    Daniel Lobo

    2015-06-01

    Full Text Available Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method

  19. Modularity of gene-regulatory networks revealed in sea-star development

    Directory of Open Access Journals (Sweden)

    Degnan Bernard M

    2011-01-01

    Full Text Available Abstract Evidence that conserved developmental gene-regulatory networks can change as a unit during deutersostome evolution emerges from a study published in BMC Biology. This shows that genes consistently expressed in anterior brain patterning in hemichordates and chordates are expressed in a similar spatial pattern in another deuterostome, an asteroid echinoderm (sea star, but in a completely different developmental context (the animal-vegetal axis. This observation has implications for hypotheses on the type of development present in the deuterostome common ancestor. See research article: http://www.biomedcentral.com/1741-7007/8/143/abstract

  20. Control of Metastatic Progression by microRNA Regulatory Networks

    Science.gov (United States)

    Pencheva, Nora; Tavazoie, Sohail F.

    2015-01-01

    Aberrant microRNA (miRNA) expression is a defining feature of human malignancy. Specific miRNAs have been identified as promoters or suppressors of metastatic progression. These miRNAs control metastasis through divergent or convergent regulation of metastatic gene pathways. Some miRNA regulatory networks govern cell-autonomous cancer phenotypes, while others modulate the cell-extrinsic composition of the metastatic microenvironment. The use of small RNAs as probes into the molecular and cellular underpinnings of metastasis holds promise for the identification of candidate genes for potential therapeutic intervention. PMID:23728460

  1. Regulatory network of GATA3 in pediatric acute lymphoblastic leukemia

    OpenAIRE

    Hou, Qianqian; Liao, Fei; Zhang, Shouyue; Zhang, Duyu; Zhang, Yan; Zhou, Xueyan; Xia, Xuyang; Ye, Yuanxin; Yang, Hanshuo; Li, Zhaozhi; Wang, Leiming; Wang, Xi; Ma, Zhigui; Zhu, Yiping; Ouyang, Liang

    2017-01-01

    GATA3 polymorphisms were reported to be significantly associated with susceptibility of pediatric B-lineage acute lymphoblastic leukemia (ALL), by impacting on GATA3 expression. We noticed that ALL-related GATA3 polymorphism located around in the tissue-specific enhancer, and significantly associated with GATA3 expression. Although the regulatory network of GATA3 has been well reported in T cells, the functional status of GATA3 is poorly understood in B-ALL. We thus conducted genome-wide gene...

  2. A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

    Science.gov (United States)

    Kentzoglanakis, Kyriakos; Poole, Matthew

    2012-01-01

    In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

  3. Complex and unexpected dynamics in simple genetic regulatory networks

    Science.gov (United States)

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

    2014-03-01

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

  4. Algebraic model checking for Boolean gene regulatory networks.

    Science.gov (United States)

    Tran, Quoc-Nam

    2011-01-01

    We present a computational method in which modular and Groebner bases (GB) computation in Boolean rings are used for solving problems in Boolean gene regulatory networks (BN). In contrast to other known algebraic approaches, the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulting in a significant improvement in running time and memory space consumption. We also show how calculation in temporal logic for model checking can be done by means of our direct and efficient Groebner basis computation in Boolean rings. We present our experimental results in finding attractors and control strategies of Boolean networks to illustrate our theoretical arguments. The results are promising. Our algebraic approach is more efficient than the state-of-the-art model checker NuSMV on BNs. More importantly, our approach finds all solutions for the BN problems.

  5. Take it of leave it : Mechanisms underlying bacterial bistable regulatory networks

    NARCIS (Netherlands)

    Siebring, Jeroen; Sorg, Robin; Herber, Martijn; Kuipers, Oscar; Filloux, Alain A.M.

    2012-01-01

    Bistable switches occur in regulatory networks that can exist in two distinct stable states. Such networks allow distinct switching of individual cells. In bacteria these switches coexist with regulatory networks that respond gradually to environmental input. Bistable switches play key roles in high

  6. Integration of genomic information with biological networks using Cytoscape.

    Science.gov (United States)

    Bauer-Mehren, Anna

    2013-01-01

    Cytoscape is an open-source software for visualizing, analyzing, and modeling biological networks. This chapter explains how to use Cytoscape to analyze the functional effect of sequence variations in the context of biological networks such as protein-protein interaction networks and signaling pathways. The chapter is divided into five parts: (1) obtaining information about the functional effect of sequence variation in a Cytoscape readable format, (2) loading and displaying different types of biological networks in Cytoscape, (3) integrating the genomic information (SNPs and mutations) with the biological networks, and (4) analyzing the effect of the genomic perturbation onto the network structure using Cytoscape built-in functions. Finally, we briefly outline how the integrated data can help in building mathematical network models for analyzing the effect of the sequence variation onto the dynamics of the biological system. Each part is illustrated by step-by-step instructions on an example use case and visualized by many screenshots and figures.

  7. Heart morphogenesis gene regulatory networks revealed by temporal expression analysis.

    Science.gov (United States)

    Hill, Jonathon T; Demarest, Bradley; Gorsi, Bushra; Smith, Megan; Yost, H Joseph

    2017-10-01

    During embryogenesis the heart forms as a linear tube that then undergoes multiple simultaneous morphogenetic events to obtain its mature shape. To understand the gene regulatory networks (GRNs) driving this phase of heart development, during which many congenital heart disease malformations likely arise, we conducted an RNA-seq timecourse in zebrafish from 30 hpf to 72 hpf and identified 5861 genes with altered expression. We clustered the genes by temporal expression pattern, identified transcription factor binding motifs enriched in each cluster, and generated a model GRN for the major gene batteries in heart morphogenesis. This approach predicted hundreds of regulatory interactions and found batteries enriched in specific cell and tissue types, indicating that the approach can be used to narrow the search for novel genetic markers and regulatory interactions. Subsequent analyses confirmed the GRN using two mutants, Tbx5 and nkx2-5 , and identified sets of duplicated zebrafish genes that do not show temporal subfunctionalization. This dataset provides an essential resource for future studies on the genetic/epigenetic pathways implicated in congenital heart defects and the mechanisms of cardiac transcriptional regulation. © 2017. Published by The Company of Biologists Ltd.

  8. A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks

    Directory of Open Access Journals (Sweden)

    Page Michel

    2009-12-01

    Full Text Available Abstract Background The study of biological networks has led to the development of increasingly large and detailed models. Computer tools are essential for the simulation of the dynamical behavior of the networks from the model. However, as the size of the models grows, it becomes infeasible to manually verify the predictions against experimental data or identify interesting features in a large number of simulation traces. Formal verification based on temporal logic and model checking provides promising methods to automate and scale the analysis of the models. However, a framework that tightly integrates modeling and simulation tools with model checkers is currently missing, on both the conceptual and the implementational level. Results We have developed a generic and modular web service, based on a service-oriented architecture, for integrating the modeling and formal verification of genetic regulatory networks. The architecture has been implemented in the context of the qualitative modeling and simulation tool GNA and the model checkers NUSMV and CADP. GNA has been extended with a verification module for the specification and checking of biological properties. The verification module also allows the display and visual inspection of the verification results. Conclusions The practical use of the proposed web service is illustrated by means of a scenario involving the analysis of a qualitative model of the carbon starvation response in E. coli. The service-oriented architecture allows modelers to define the model and proceed with the specification and formal verification of the biological properties by means of a unified graphical user interface. This guarantees a transparent access to formal verification technology for modelers of genetic regulatory networks.

  9. Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach

    Directory of Open Access Journals (Sweden)

    Buer Jan

    2004-12-01

    Full Text Available Abstract Background Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear. Results In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators. Conclusion The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E

  10. Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

    DEFF Research Database (Denmark)

    Österlund, Tobias; Bordel, Sergio; Nielsen, Jens

    2015-01-01

    % for the human network. The high controllability (low number of drivers needed to control the system) in yeast, mouse and human is due to the presence of internal loops in their regulatory networks where the TFs regulate each other in a circular fashion. We refer to these internal loops as circular control...... motifs (CCM). The E. coli transcriptional regulatory network, which does not have any CCMs, shows a hierarchical structure of the transcriptional regulatory network in contrast to the eukaryal networks. The presence of CCMs also has influence on the stability of these networks, as the presence of cycles...

  11. NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

    Directory of Open Access Journals (Sweden)

    Joeri Ruyssinck

    Full Text Available One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made

  12. Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors

    Directory of Open Access Journals (Sweden)

    Frank Emmert-Streib

    2013-02-01

    Full Text Available The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I observational gene expression data: normal environmental condition, (II interventional gene expression data: growth in rich media, (III interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

  13. Reduction of regulatory risk: a network economic approach

    OpenAIRE

    Knieps, Günter; Weiß, Hans-Jörg

    2007-01-01

    Several definitions of regulatory risk are known from the literature. From the perspective of regulatory reform it is important to differentiate between the impact of a given regulatory scheme on the firm's risk exposure and the risk arising from discretionary behavior of regulatory agencies. Whereas the conse-quences of effective regulation in principle are known and accepted, excessive regulatory discretion may cause a strong need for regulatory reform. Regulatory reform focussing on the re...

  14. Piecing together cis-regulatory networks: insights from epigenomics studies in plants.

    Science.gov (United States)

    Huang, Shao-Shan C; Ecker, Joseph R

    2018-05-01

    5-Methylcytosine, a chemical modification of DNA, is a covalent modification found in the genomes of both plants and animals. Epigenetic inheritance of phenotypes mediated by DNA methylation is well established in plants. Most of the known mechanisms of establishing, maintaining and modifying DNA methylation have been worked out in the reference plant Arabidopsis thaliana. Major functions of DNA methylation in plants include regulation of gene expression and silencing of transposable elements (TEs) and repetitive sequences, both of which have parallels in mammalian biology, involve interaction with the transcriptional machinery, and may have profound effects on the regulatory networks in the cell. Methylome and transcriptome dynamics have been investigated in development and environmental responses in Arabidopsis and agriculturally and ecologically important plants, revealing the interdependent relationship among genomic context, methylation patterns, and expression of TE and protein coding genes. Analyses of methylome variation among plant natural populations and species have begun to quantify the extent of genetic control of methylome variation vs. true epimutation, and model the evolutionary forces driving methylome evolution in both short and long time scales. The ability of DNA methylation to positively or negatively modulate binding affinity of transcription factors (TFs) provides a natural link from genome sequence and methylation changes to transcription. Technologies that allow systematic determination of methylation sensitivities of TFs, in native genomic and methylation context without confounding factors such as histone modifications, will provide baseline datasets for building cell-type- and individual-specific regulatory networks that underlie the establishment and inheritance of complex traits. This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods Biological Mechanisms > Regulatory Biology. © 2017 Wiley

  15. Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.

    Science.gov (United States)

    Gu, Jinghua; Xuan, Jianhua; Riggins, Rebecca B; Chen, Li; Wang, Yue; Clarke, Robert

    2012-08-01

    Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context. In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm. xuan@vt.edu Supplementary data are available at Bioinformatics online.

  16. Dissection of regulatory networks that are altered in disease via differential co-expression.

    Directory of Open Access Journals (Sweden)

    David Amar

    Full Text Available Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks. Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples. We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer's disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.

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

    Science.gov (United States)

    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.

  18. Robustness leads close to the edge of chaos in coupled map networks: toward the understanding of biological networks

    International Nuclear Information System (INIS)

    Saito, Nen; Kikuchi, Macoto

    2013-01-01

    Dynamics in biological networks are, in general, robust against several perturbations. We investigate a coupled map network as a model motivated by gene regulatory networks and design systems that are robust against phenotypic perturbations (perturbations in dynamics), as well as systems that are robust against mutation (perturbations in network structure). To achieve such a design, we apply a multicanonical Monte Carlo method. Analysis based on the maximum Lyapunov exponent and parameter sensitivity shows that systems with marginal stability, which are regarded as systems at the edge of chaos, emerge when robustness against network perturbations is required. This emergence of the edge of chaos is a self-organization phenomenon and does not need a fine tuning of parameters. (paper)

  19. Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviours

    Directory of Open Access Journals (Sweden)

    Daria eMolodtsova

    2014-12-01

    Full Text Available It is increasingly apparent that genes and networks that influence complex behaviour are evolutionary conserved, which is paradoxical considering that behaviour is labile over evolutionary timescales. How does adaptive change in behaviour arise if behaviour is controlled by conserved, pleiotropic, and likely evolutionary constrained genes? Pleiotropy and connectedness are known to constrain the general rate of protein evolution, prompting some to suggest that the evolution of complex traits, including behaviour, is fuelled by regulatory sequence evolution. However, we seldom have data on the strength of selection on mutations in coding and regulatory sequences, and this hinders our ability to study how pleiotropy influences coding and regulatory sequence evolution. Here we use population genomics to estimate the strength of selection on coding and regulatory mutations for a transcriptional regulatory network that influences complex behaviour of honey bees. We found that replacement mutations in highly connected transcription factors and target genes experience significantly stronger negative selection relative to weakly connected transcription factors and targets. Adaptively evolving proteins were significantly more likely to reside at the periphery of the regulatory network, while proteins with signs of negative selection were near the core of the network. Interestingly, connectedness and network structure had minimal influence on the strength of selection on putative regulatory sequences for both transcription factors and their targets. Our study indicates that adaptive evolution of complex behaviour can arise because of positive selection on protein-coding mutations in peripheral genes, and on regulatory sequence mutations in both transcription factors and their targets throughout the network.

  20. Application of random matrix theory to biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Luo Feng [Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, SC 29634 (United States); Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhong Jianxin [Department of Physics, Xiangtan University, Hunan 411105 (China) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhongjn@ornl.gov; Yang Yunfeng [Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Scheuermann, Richard H. [Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhou Jizhong [Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019 (United States) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhouj@ornl.gov

    2006-09-25

    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.

  1. Virtual private networks application in Nuclear Regulatory Authority of Argentina

    International Nuclear Information System (INIS)

    Glidewell, Donnie D.; Smartt, Heidi A.; Caskey, Susan A.; Bonino, Anibal D.; Perez, Adrian C.; Pardo, German R.; Vigile, Rodolfo S.; Krimer, Mario

    2004-01-01

    As the result of the existence of several regional delegations all over the country, a requirement was made to conform a secure data interchange structure. This would make possible the interconnection of these facilities and their communication with the Autoridad Regulatoria Nuclear (ARN) headquarters. The records these parts exchange are often of classified nature, including sensitive data by the local safeguards inspectors. On the other hand, the establishment of this network should simplify the access of authorized nuclear and radioactive materials users to the ARN databases, from remote sites and with significant trust levels. These requirements called for a network that should be not only private but also secure, providing data centralization and integrity assurance with a strict user control. The first proposal was to implement a point to point link between the installations. This proposal was deemed as economically not viable, and it had the disadvantage of not being easily reconfigurable. The availability of new technologies, and the accomplishment of the Action Sheet 11 under an agreement between Argentine Nuclear Regulatory Authority and the United States Department of Energy (DOE), opened a new path towards the resolution of this problem. By application of updated tunneling security protocols it was possible to project a manageable and secure network through the use of Virtual Private Networking (VPN) hardware. A first trial installation of this technology was implemented between ARN headquarters at Buenos Aires and the Southern Region Office at Bariloche, Argentina. This private net is at the moment under test, and it is planned to expand to more sites in this country, reaching for example to nuclear power plants. The Bariloche installation had some interesting peculiarities. The solutions proposed to them revealed to be very useful during the development of the network expansion plans, as they showed how to adapt the VPN technical requisites to the

  2. Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

    Science.gov (United States)

    Wang, Yi Kan; Hurley, Daniel G; Schnell, Santiago; Print, Cristin G; Crampin, Edmund J

    2013-01-01

    We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

  3. Regulatory networks and connected components of the neutral space. A look at functional islands

    Science.gov (United States)

    Boldhaus, G.; Klemm, K.

    2010-09-01

    The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality - typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into ≈ 4.7 × 108 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.

  4. A new method for discovering disease-specific MiRNA-target regulatory networks.

    Directory of Open Access Journals (Sweden)

    Miriam Baglioni

    Full Text Available Genes and their expression regulation are among the key factors in the comprehension of the genesis and development of complex diseases. In this context, microRNAs (miRNAs are post-transcriptional regulators that play an important role in gene expression since they are frequently deregulated in pathologies like cardiovascular disease and cancer. In vitro validation of miRNA--targets regulation is often too expensive and time consuming to be carried out for every possible alternative. As a result, a tool able to provide some criteria to prioritize trials is becoming a pressing need. Moreover, before planning in vitro experiments, the scientist needs to evaluate the miRNA-target genes interaction network. In this paper we describe the miRable method whose purpose is to identify new potentially relevant genes and their interaction networks associate to a specific pathology. To achieve this goal miRable follows a system biology approach integrating together general-purpose medical knowledge (literature, Protein-Protein Interaction networks, prediction tools and pathology specific data (gene expression data. A case study on Prostate Cancer has shown that miRable is able to: 1 find new potential miRNA-targets pairs, 2 highlight novel genes potentially involved in a disease but never or little studied before, 3 reconstruct all possible regulatory subnetworks starting from the literature to expand the knowledge on the regulation of miRNA regulatory mechanisms.

  5. OWL Reasoning Framework over Big Biological Knowledge Network

    Science.gov (United States)

    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

  6. Biology Question Generation from a Semantic Network

    Science.gov (United States)

    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

  7. Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity

    DEFF Research Database (Denmark)

    Green, Sara; Serban, Maria; Scholl, Raphael

    2018-01-01

    of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological...... strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties...

  8. Robust and global delay-dependent stability for genetic regulatory networks with parameter uncertainties.

    Science.gov (United States)

    Tian, Li-Ping; Wang, Jianxin; Wu, Fang-Xiang

    2012-09-01

    The study of stability is essential for designing or controlling genetic regulatory networks, which can be described by nonlinear differential equations with time delays. Much attention has been paid to the study of delay-independent stability of genetic regulatory networks and as a result, many sufficient conditions have been derived for delay-independent stability. Although it might be more interesting in practice, delay-dependent stability of genetic regulatory networks has been studied insufficiently. Based on the linear matrix inequality (LMI) approach, in this study we will present some delay-dependent stability conditions for genetic regulatory networks. Then we extend these results to genetic regulatory networks with parameter uncertainties. To illustrate the effectiveness of our theoretical results, gene repressilatory networks are analyzed .

  9. Risk management of biologicals: a regulatory and clinical perspective

    NARCIS (Netherlands)

    Giezen, T.J.|info:eu-repo/dai/nl/304823112

    2011-01-01

    Biologicals are a relatively new class of drugs and are important treatment options for a variety of chronic and/or serious conditions. Biologicals have different characteristics as compared to the traditional chemically synthesised drugs, which may result in different risk profiles and the need for

  10. Network science of biological systems at different scales: A review

    Science.gov (United States)

    Gosak, Marko; Markovič, Rene; Dolenšek, Jurij; Slak Rupnik, Marjan; Marhl, Marko; Stožer, Andraž; Perc, Matjaž

    2018-03-01

    Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present

  11. Event-based cluster synchronization of coupled genetic regulatory networks

    Science.gov (United States)

    Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

    2017-09-01

    In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

  12. Stability analysis of delayed genetic regulatory networks with stochastic disturbances

    Energy Technology Data Exchange (ETDEWEB)

    Zhou Qi, E-mail: zhouqilhy@yahoo.com.c [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Xu Shengyuan [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Chen Bing [Institute of Complexity Science, Qingdao University, Qingdao 266071, Shandong (China); Li Hongyi [Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China); Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)

    2009-10-05

    This Letter considers the problem of stability analysis of a class of delayed genetic regulatory networks with stochastic disturbances. The delays are assumed to be time-varying and bounded. By utilizing Ito's differential formula and Lyapunov-Krasovskii functionals, delay-range-dependent and rate-dependent (rate-independent) stability criteria are proposed in terms of linear matrices inequalities. An important feature of the proposed results is that all the stability conditions are dependent on the upper and lower bounds of the delays. Another important feature is that the obtained stability conditions are less conservative than certain existing ones in the literature due to introducing some appropriate free-weighting matrices. A simulation example is employed to illustrate the applicability and effectiveness of the proposed methods.

  13. Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

    Directory of Open Access Journals (Sweden)

    Qi Yuan(Alan

    2010-01-01

    Full Text Available Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( status and Estrogen Receptor negative ( status, respectively.

  14. Complex Regulatory Networks Governing Production of the Glycopeptide A40926

    Directory of Open Access Journals (Sweden)

    Rosa Alduina

    2018-04-01

    Full Text Available Glycopeptides (GPAs are an important class of antibiotics, with vancomycin and teicoplanin being used in the last 40 years as drugs of last resort to treat infections caused by Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus. A few new GPAs have since reached the market. One of them is dalbavancin, a derivative of A40926 produced by the actinomycete Nonomuraea sp. ATCC 39727, recently classified as N. gerenzanensis. This review summarizes what we currently know on the multilevel regulatory processes governing production of the glycopeptide A40926 and the different approaches used to increase antibiotic yields. Some nutrients, e.g., valine, l-glutamine and maltodextrin, and some endogenous proteins, e.g., Dbv3, Dbv4 and RpoBR, have a positive role on A40926 biosynthesis, while other factors, e.g., phosphate, ammonium and Dbv23, have a negative effect. Overall, the results available so far point to a complex regulatory network controlling A40926 in the native producing strain.

  15. Complex Regulatory Networks Governing Production of the Glycopeptide A40926.

    Science.gov (United States)

    Alduina, Rosa; Sosio, Margherita; Donadio, Stefano

    2018-04-05

    Glycopeptides (GPAs) are an important class of antibiotics, with vancomycin and teicoplanin being used in the last 40 years as drugs of last resort to treat infections caused by Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus . A few new GPAs have since reached the market. One of them is dalbavancin, a derivative of A40926 produced by the actinomycete Nonomuraea sp. ATCC 39727, recently classified as N. gerenzanensis . This review summarizes what we currently know on the multilevel regulatory processes governing production of the glycopeptide A40926 and the different approaches used to increase antibiotic yields. Some nutrients, e.g., valine, l-glutamine and maltodextrin, and some endogenous proteins, e.g., Dbv3, Dbv4 and RpoB R , have a positive role on A40926 biosynthesis, while other factors, e.g., phosphate, ammonium and Dbv23, have a negative effect. Overall, the results available so far point to a complex regulatory network controlling A40926 in the native producing strain.

  16. A generic algorithm for layout of biological networks.

    Science.gov (United States)

    Schreiber, Falk; Dwyer, Tim; Marriott, Kim; Wybrow, Michael

    2009-11-12

    Biological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration. We present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks. The presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.

  17. Identification and Characterization of 5′ Untranslated Regions (5′UTRs in Zymomonas mobilis as Regulatory Biological Parts

    Directory of Open Access Journals (Sweden)

    Seung Hee Cho

    2017-12-01

    Full Text Available Regulatory RNA regions within a transcript, particularly in the 5′ untranslated region (5′UTR, have been shown in a variety of organisms to control the expression levels of these mRNAs in response to various metabolites or environmental conditions. Considering the unique tolerance of Zymomonas mobilis to ethanol and the growing interest in engineering microbial strains with enhanced tolerance to industrial inhibitors, we searched natural cis-regulatory regions in this microorganism using transcriptomic data and bioinformatics analysis. Potential regulatory 5′UTRs were identified and filtered based on length, gene function, relative gene counts, and conservation in other organisms. An in vivo fluorescence-based screening system was developed to confirm the responsiveness of 36 5′UTR candidates to ethanol, acetate, and xylose stresses. UTR_ZMO0347 (5′UTR of gene ZMO0347 encoding the RNA binding protein Hfq was found to down-regulate downstream gene expression under ethanol stress. Genomic deletion of UTR_ZMO0347 led to a general decrease of hfq expression at the transcript level and increased sensitivity for observed changes in Hfq expression at the protein level. The role of UTR_ZMO0347 and other 5′UTRs gives us insight into the regulatory network of Z. mobilis in response to stress and unlocks new strategies for engineering robust industrial strains as well as for harvesting novel responsive regulatory biological parts for controllable gene expression platforms in this organism.

  18. BioNSi: A Discrete Biological Network Simulator Tool.

    Science.gov (United States)

    Rubinstein, Amir; Bracha, Noga; Rudner, Liat; Zucker, Noga; Sloin, Hadas E; Chor, Benny

    2016-08-05

    Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

  19. Power Laws, Scale-Free Networks and Genome Biology

    CERN Document Server

    Koonin, Eugene V; Karev, Georgy P

    2006-01-01

    Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...

  20. Activating and inhibiting connections in biological network dynamics

    Directory of Open Access Journals (Sweden)

    Knight Rob

    2008-12-01

    Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.

  1. Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data.

    Science.gov (United States)

    Tsou, Ann-Ping; Sun, Yi-Ming; Liu, Chia-Lin; Huang, Hsien-Da; Horng, Jorng-Tzong; Tsai, Meng-Feng; Liu, Baw-Juine

    2006-07-01

    Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a microarray study. Both of the binding sites of known transcription factors and predicted over-represented (OR) oligonucleotides were demonstrated for the gene group. The potential biological roles of both known nucleotides and one OR nucleotide were demonstrated using bioassays. Therefore, the results from the wet-lab experiments reinforce the power and utility of the data warehouse as an approach to the genome-wide search for important transcription regulatory elements that are the key to many complex biological systems.

  2. Antagonistic Coevolution Drives Whack-a-Mole Sensitivity in Gene Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Jeewoen Shin

    2015-10-01

    Full Text Available Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems. However, robustness often coexists with its counterpart, evolvability--the ability of perturbations to generate new phenotypes. Previous models of gene regulatory network evolution have shown that robustness evolves under stabilizing selection, but it is unclear how robustness and evolvability will emerge in common coevolutionary scenarios. We consider a two-species model of coevolution involving one host and one parasite population. By using two interacting species, key model parameters that determine the fitness landscapes become emergent properties of the model, avoiding the need to impose these parameters externally. In our study, parasites are modeled on species such as cuckoos where mimicry of the host phenotype confers high fitness to the parasite but lower fitness to the host. Here, frequent phenotype changes are favored as each population continually adapts to the other population. Sensitivity evolves at the network level such that point mutations can induce large phenotype changes. Crucially, the sensitive points of the network are broadly distributed throughout the network and continually relocate. Each time sensitive points in the network are mutated, new ones appear to take their place. We have therefore named this phenomenon "whack-a-mole" sensitivity, after a popular fun park game. We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.

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

    Science.gov (United States)

    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 © The Author(s) 2015. Published by Oxford University Press.

  4. A Kalman-filter based approach to identification of time-varying gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Jie Xiong

    Full Text Available MOTIVATION: Conventional identification methods for gene regulatory networks (GRNs have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs. RESULTS: It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.

  5. Modulation of dynamic modes by interplay between positive and negative feedback loops in gene regulatory networks

    Science.gov (United States)

    Wang, Liu-Suo; Li, Ning-Xi; Chen, Jing-Jia; Zhang, Xiao-Peng; Liu, Feng; Wang, Wei

    2018-04-01

    A positive and a negative feedback loop can induce bistability and oscillation, respectively, in biological networks. Nevertheless, they are frequently interlinked to perform more elaborate functions in many gene regulatory networks. Coupled positive and negative feedback loops may exhibit either oscillation or bistability depending on the intensity of the stimulus in some particular networks. It is less understood how the transition between the two dynamic modes is modulated by the positive and negative feedback loops. We developed an abstract model of such systems, largely based on the core p53 pathway, to explore the mechanism for the transformation of dynamic behaviors. Our results show that enhancing the positive feedback may promote or suppress oscillations depending on the strength of both feedback loops. We found that the system oscillates with low amplitudes in response to a moderate stimulus and switches to the on state upon a strong stimulus. When the positive feedback is activated much later than the negative one in response to a strong stimulus, the system exhibits long-term oscillations before switching to the on state. We explain this intriguing phenomenon using quasistatic approximation. Moreover, early switching to the on state may occur when the system starts from a steady state in the absence of stimuli. The interplay between the positive and negative feedback plays a key role in the transitions between oscillation and bistability. Of note, our conclusions should be applicable only to some specific gene regulatory networks, especially the p53 network, in which both oscillation and bistability exist in response to a certain type of stimulus. Our work also underscores the significance of transient dynamics in determining cellular outcome.

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

    Science.gov (United States)

    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

  7. CMRegNet-An interspecies reference database for corynebacterial and mycobacterial regulatory networks

    DEFF Research Database (Denmark)

    Abreu, Vinicius A C; Almeida, Sintia; Tiwari, Sandeep

    2015-01-01

    gene regulatory network can lead to various practical applications, creating a greater understanding of how organisms control their cellular behavior. DESCRIPTION: In this work, we present a new database, CMRegNet for the gene regulatory networks of Corynebacterium glutamicum ATCC 13032......Net to date the most comprehensive database of regulatory interactions of CMNR bacteria. The content of CMRegNet is publicly available online via a web interface found at http://lgcm.icb.ufmg.br/cmregnet ....

  8. Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Alina Sîrbu

    2015-05-01

    Full Text Available Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions. Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

  9. Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks.

    Science.gov (United States)

    Sîrbu, Alina; Crane, Martin; Ruskin, Heather J

    2015-05-14

    Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

  10. An overview of the gene regulatory network controlling trichome development in the model plant, Arabidopsis

    Directory of Open Access Journals (Sweden)

    Sitakanta ePattanaik

    2014-06-01

    Full Text Available Trichomes are specialized epidermal cells located on aerial parts of plants and are associated with a wide array of biological processes. Trichomes protect plants from adverse conditions including UV light and herbivore attack and are also an important source of a number of phytochemicals. The simple unicellular trichomes of Arabidopsis serve as an excellent model to study molecular mechanism of cell differentiation and pattern formation in plants. The emerging picture suggests that the developmental process is controlled by a transcriptional network involving three major groups of transcription factors: the R2R3 MYB, basic helix-loop-helix (bHLH and WD40 repeat (WDR protein. These regulatory proteins form a trimeric activator complex that positively regulates trichome development. The single repeat R3 MYBs act as negative regulators of trichome development. They compete with the R2R3 MYBs to bind the bHLH factor and form a repressor complex. In addition to activator-repressor mechanism, a depletion mechanism may operate in parallel during trichome development. In this mechanism, the bHLH factor traps the WDR protein which results in depletion of WDR protein in neighboring cells. Consequently, the cells with high levels of bHLH and WDR proteins are developed into trichomes. A group of C2H2 zinc finger TFs has also been implicated in trichome development. Phytohormones, including gibberellins and jasmonic acid, play significant roles in this developmental process. Recently, microRNAs have been shown to be involved in trichome development. Furthermore, it has been demonstrated that the activities of the key regulatory proteins involved in trichome development are controlled by the 26S/ubiquitin proteasome system (UPS, highlighting the complexity of the regulatory network controlling this developmental process. To complement several excellent recent relevant reviews, this review focuses on the transcriptional network and hormonal interplay

  11. Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae.

    Directory of Open Access Journals (Sweden)

    Maria Simak

    Full Text Available The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN. For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these problems, we introduce a Boolean Function Network (BFN model based on techniques of hidden Markov model (HMM, likelihood ratio test and Boolean logic functions. BFN consists of two consecutive tests to establish links between pairs of genes and check their directness. We evaluate the performance of BFN through the application to S. cerevisiae time course data. BFN produces regulatory relations which show consistency with succession of cell cycle phases. Furthermore, it also improves sensitivity and specificity when compared with alternative methods of genetic network reverse engineering. Moreover, we demonstrate that BFN can provide proper resolution for GO enrichment of gene sets. Finally, the Boolean functions discovered by BFN can provide useful insights for the identification of control mechanisms of regulatory processes, which is the special advantage of the proposed approach. In combination with low computational complexity, BFN can serve as an efficient screening tool to reconstruct genes relations on the whole genome level. In addition, the BFN approach is also feasible to a wide range of time course datasets.

  12. Detection of the dominant direction of information flow and feedback links in densely interconnected regulatory networks

    Directory of Open Access Journals (Sweden)

    Ispolatov Iaroslav

    2008-10-01

    Full Text Available Abstract Background Finding the dominant direction of flow of information in densely interconnected regulatory or signaling networks is required in many applications in computational biology and neuroscience. This is achieved by first identifying and removing links which close up feedback loops in the original network and hierarchically arranging nodes in the remaining network. In mathematical language this corresponds to a problem of making a graph acyclic by removing as few links as possible and thus altering the original graph in the least possible way. The exact solution of this problem requires enumeration of all cycles and combinations of removed links, which, as an NP-hard problem, is computationally prohibitive even for modest-size networks. Results We introduce and compare two approximate numerical algorithms for solving this problem: the probabilistic one based on a simulated annealing of the hierarchical layout of the network which minimizes the number of "backward" links going from lower to higher hierarchical levels, and the deterministic, "greedy" algorithm that sequentially cuts the links that participate in the largest number of feedback cycles. We find that the annealing algorithm outperforms the deterministic one in terms of speed, memory requirement, and the actual number of removed links. To further improve a visual perception of the layout produced by the annealing algorithm, we perform an additional minimization of the length of hierarchical links while keeping the number of anti-hierarchical links at their minimum. The annealing algorithm is then tested on several examples of regulatory and signaling networks/pathways operating in human cells. Conclusion The proposed annealing algorithm is powerful enough to performs often optimal layouts of protein networks in whole organisms, consisting of around ~104 nodes and ~105 links, while the applicability of the greedy algorithm is limited to individual pathways with ~100

  13. Intervention in gene regulatory networks with maximal phenotype alteration.

    Science.gov (United States)

    Yousefi, Mohammadmahdi R; Dougherty, Edward R

    2013-07-15

    A basic issue for translational genomics is to model gene interaction via gene regulatory networks (GRNs) and thereby provide an informatics environment to study the effects of intervention (say, via drugs) and to derive effective intervention strategies. Taking the view that the phenotype is characterized by the long-run behavior (steady-state distribution) of the network, we desire interventions to optimally move the probability mass from undesirable to desirable states Heretofore, two external control approaches have been taken to shift the steady-state mass of a GRN: (i) use a user-defined cost function for which desirable shift of the steady-state mass is a by-product and (ii) use heuristics to design a greedy algorithm. Neither approach provides an optimal control policy relative to long-run behavior. We use a linear programming approach to optimally shift the steady-state mass from undesirable to desirable states, i.e. optimization is directly based on the amount of shift and therefore must outperform previously proposed methods. Moreover, the same basic linear programming structure is used for both unconstrained and constrained optimization, where in the latter case, constraints on the optimization limit the amount of mass that may be shifted to 'ambiguous' states, these being states that are not directly undesirable relative to the pathology of interest but which bear some perceived risk. We apply the method to probabilistic Boolean networks, but the theory applies to any Markovian GRN. Supplementary materials, including the simulation results, MATLAB source code and description of suboptimal methods are available at http://gsp.tamu.edu/Publications/supplementary/yousefi13b. edward@ece.tamu.edu Supplementary data are available at Bioinformatics online.

  14. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an important role in communication and spreading of ...

  15. The effect of network biology on drug toxicology

    DEFF Research Database (Denmark)

    Gautier, Laurent; Taboureau, Olivier; Audouze, Karine Marie Laure

    2013-01-01

    Introduction: 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. Areas covered: 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. Expert opinion: 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...

  16. Local and global control of ecological and biological networks

    OpenAIRE

    Alessandro Ferrarini

    2014-01-01

    Recently, I introduced a methodological framework so that ecological and biological networks can be controlled both from inside and outside by coupling network dynamics and evolutionary modelling. The endogenous control requires the network to be optimized at the beginning of its dynamics (by acting upon nodes, edges or both) so that it will then go inertially to the desired state. Instead, the exogenous control requires that exogenous controllers act upon the network at each time step. By th...

  17. A High-Level Petri Net Framework for Genetic Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Banks Richard

    2007-12-01

    Full Text Available To understand the function of genetic regulatory networks in the development of cellular systems, we must not only realise the individual network entities, but also the manner by which they interact. Multi-valued networks are a promising qualitative approach for modelling such genetic regulatory networks, however, at present they have limited formal analysis techniques and tools. We present a flexible formal framework for modelling and analysing multi-valued genetic regulatory networks using high-level Petri nets and logic minimization techniques. We demonstrate our approach with a detailed case study in which part of the genetic regulatory network responsible for the carbon starvation stress response in Escherichia coli is modelled and analysed. We then compare and contrast this multivalued model to a corresponding Boolean model and consider their formal relationship.

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

    Directory of Open Access Journals (Sweden)

    Pei Wang

    Full Text Available 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.

  19. Neutral forces acting on intragenomic variability shape the Escherichia coli regulatory network topology.

    Science.gov (United States)

    Ruths, Troy; Nakhleh, Luay

    2013-05-07

    Cis-regulatory networks (CRNs) play a central role in cellular decision making. Like every other biological system, CRNs undergo evolution, which shapes their properties by a combination of adaptive and nonadaptive evolutionary forces. Teasing apart these forces is an important step toward functional analyses of the different components of CRNs, designing regulatory perturbation experiments, and constructing synthetic networks. Although tests of neutrality and selection based on molecular sequence data exist, no such tests are currently available based on CRNs. In this work, we present a unique genotype model of CRNs that is grounded in a genomic context and demonstrate its use in identifying portions of the CRN with properties explainable by neutral evolutionary forces at the system, subsystem, and operon levels. We leverage our model against experimentally derived data from Escherichia coli. The results of this analysis show statistically significant and substantial neutral trends in properties previously identified as adaptive in origin--degree distribution, clustering coefficient, and motifs--within the E. coli CRN. Our model captures the tightly coupled genome-interactome of an organism and enables analyses of how evolutionary events acting at the genome level, such as mutation, and at the population level, such as genetic drift, give rise to neutral patterns that we can quantify in CRNs.

  20. Recombinant biologic products versus nutraceuticals from plants – a regulatory choice?

    Science.gov (United States)

    Drake, Pascal M. W.; Szeto, Tim H.; Paul, Mathew J.; Teh, Audrey Y.‐H.

    2016-01-01

    Biotechnology has transformed the potential for plants to be a manufacturing source of pharmaceutical compounds. Now, with transgenic and transient expression techniques, virtually any biologic, including vaccines and therapeutics, could be manufactured in plants. However, uncertainty over the regulatory path for such new pharmaceuticals has been a deterrent. Consideration has been given to using alternative regulatory paths, including those for nutraceuticals or cosmetic agents. This review will consider these possibilities, and discuss the difficulties in establishing regulatory guidelines for new pharmaceutical manufacturing technologies. PMID:27297459

  1. An efficient grid layout algorithm for biological networks utilizing various biological attributes

    Directory of Open Access Journals (Sweden)

    Kato Mitsuru

    2007-03-01

    Full Text Available Abstract Background Clearly visualized biopathways provide a great help in understanding biological systems. However, manual drawing of large-scale biopathways is time consuming. We proposed a grid layout algorithm that can handle gene-regulatory networks and signal transduction pathways by considering edge-edge crossing, node-edge crossing, distance measure between nodes, and subcellular localization information from Gene Ontology. Consequently, the layout algorithm succeeded in drastically reducing these crossings in the apoptosis model. However, for larger-scale networks, we encountered three problems: (i the initial layout is often very far from any local optimum because nodes are initially placed at random, (ii from a biological viewpoint, human layouts still exceed automatic layouts in understanding because except subcellular localization, it does not fully utilize biological information of pathways, and (iii it employs a local search strategy in which the neighborhood is obtained by moving one node at each step, and automatic layouts suggest that simultaneous movements of multiple nodes are necessary for better layouts, while such extension may face worsening the time complexity. Results We propose a new grid layout algorithm. To address problem (i, we devised a new force-directed algorithm whose output is suitable as the initial layout. For (ii, we considered that an appropriate alignment of nodes having the same biological attribute is one of the most important factors of the comprehension, and we defined a new score function that gives an advantage to such configurations. For solving problem (iii, we developed a search strategy that considers swapping nodes as well as moving a node, while keeping the order of the time complexity. Though a naïve implementation increases by one order, the time complexity, we solved this difficulty by devising a method that caches differences between scores of a layout and its possible updates

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

    Science.gov (United States)

    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.

  3. VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications

    Directory of Open Access Journals (Sweden)

    Brinkrolf Christoph

    2014-06-01

    Full Text Available 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.

  4. Controllability and observability of Boolean networks arising from biology

    Science.gov (United States)

    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.

  5. Regulatory cascade and biological activity of Beauveria bassiana oosporein that limits bacterial growth after host death.

    Science.gov (United States)

    Fan, Yanhua; Liu, Xi; Keyhani, Nemat O; Tang, Guirong; Pei, Yan; Zhang, Wenwen; Tong, Sheng

    2017-02-28

    The regulatory network and biological functions of the fungal secondary metabolite oosporein have remained obscure. Beauveria bassiana has evolved the ability to parasitize insects and outcompete microbial challengers for assimilation of host nutrients. A novel zinc finger transcription factor, BbSmr1 ( B. bassiana secondary metabolite regulator 1), was identified in a screen for oosporein overproduction. Deletion of Bbsmr1 resulted in up-regulation of the oosporein biosynthetic gene cluster ( OpS genes) and constitutive oosporein production. Oosporein production was abolished in double mutants of Bbsmr1 and a second transcription factor, OpS3 , within the oosporein gene cluster ( ΔBbsmr1ΔOpS3 ), indicating that BbSmr1 acts as a negative regulator of OpS3 expression. Real-time quantitative PCR and a GFP promoter fusion construct of OpS1 , the oosporein polyketide synthase, indicated that OpS1 is expressed mainly in insect cadavers at 24-48 h after death. Bacterial colony analysis in B. bassiana -infected insect hosts revealed increasing counts until host death, with a dramatic decrease (∼90%) after death that correlated with oosporein production. In vitro studies verified the inhibitory activity of oosporein against bacteria derived from insect cadavers. These results suggest that oosporein acts as an antimicrobial compound to limit microbial competition on B. bassiana -killed hosts, allowing the fungus to maximally use host nutrients to grow and sporulate on infected cadavers.

  6. In silico transcriptional regulatory networks involved in tomato fruit ripening

    Directory of Open Access Journals (Sweden)

    Stilianos Arhondakis

    2016-08-01

    Full Text Available ABSTRACTTomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37 and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening.

  7. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

    Directory of Open Access Journals (Sweden)

    Yeh Cheng-Yu

    2009-12-01

    . Conclusions We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

  8. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

    Science.gov (United States)

    Yeh, Hsiang-Yuan; Cheng, Shih-Wu; Lin, Yu-Chun; Yeh, Cheng-Yu; Lin, Shih-Fang; Soo, Von-Wun

    2009-12-21

    the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

  9. Regulatory network of GATA3 in pediatric acute lymphoblastic leukemia.

    Science.gov (United States)

    Hou, Qianqian; Liao, Fei; Zhang, Shouyue; Zhang, Duyu; Zhang, Yan; Zhou, Xueyan; Xia, Xuyang; Ye, Yuanxin; Yang, Hanshuo; Li, Zhaozhi; Wang, Leiming; Wang, Xi; Ma, Zhigui; Zhu, Yiping; Ouyang, Liang; Wang, Yuelan; Zhang, Hui; Yang, Li; Xu, Heng; Shu, Yang

    2017-05-30

    GATA3 polymorphisms were reported to be significantly associated with susceptibility of pediatric B-lineage acute lymphoblastic leukemia (ALL), by impacting on GATA3 expression. We noticed that ALL-related GATA3 polymorphism located around in the tissue-specific enhancer, and significantly associated with GATA3 expression. Although the regulatory network of GATA3 has been well reported in T cells, the functional status of GATA3 is poorly understood in B-ALL. We thus conducted genome-wide gene expression association analyses to reveal expression associated genes and pathways in nine independent B-ALL patient cohorts. In B-ALL patients, 173 candidates were identified to be significantly associated with GATA3 expression, including some reported GATA3-related genes (e.g., ITM2A) and well-known tumor-related genes (e.g., STAT4). Some of the candidates exhibit tissue-specific and subtype-specific association with GATA3. Through overexpression and down-regulation of GATA3 in leukemia cell lines, several reported and novel GATA3 regulated genes were validated. Moreover, association of GATA3 expression and its targets can be impacted by SNPs (e.g., rs4894953), which locate in the potential GATA3 binding motif. Our findings suggest that GATA3 may be involved in multiple tumor-related pathways (e.g., STAT/JAK pathway) in B-ALL to impact leukemogenesis through epigenetic regulation.

  10. Systems biology: properties of reconstructed networks

    National Research Council Canada - National Science Library

    Palsson, Bernhard

    2006-01-01

    ... between the mathematical ideas and biological processes are made clear, the book reflects the irreversible trend of increasing mathematical content in biology education. Therefore to assist both teacher and student, Palsson provides problem sets, projects, and PowerPoint slides in an associated web site and keeps the presentation in the book concrete with illustrat...

  11. The Structure and Function of Biological Networks

    Science.gov (United States)

    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…

  12. Sub-circuits of a gene regulatory network control a developmental epithelial-mesenchymal transition.

    Science.gov (United States)

    Saunders, Lindsay R; McClay, David R

    2014-04-01

    Epithelial-mesenchymal transition (EMT) is a fundamental cell state change that transforms epithelial to mesenchymal cells during embryonic development, adult tissue repair and cancer metastasis. EMT includes a complex series of intermediate cell state changes including remodeling of the basement membrane, apical constriction, epithelial de-adhesion, directed motility, loss of apical-basal polarity, and acquisition of mesenchymal adhesion and polarity. Transcriptional regulatory state changes must ultimately coordinate the timing and execution of these cell biological processes. A well-characterized gene regulatory network (GRN) in the sea urchin embryo was used to identify the transcription factors that control five distinct cell changes during EMT. Single transcription factors were perturbed and the consequences followed with in vivo time-lapse imaging or immunostaining assays. The data show that five different sub-circuits of the GRN control five distinct cell biological activities, each part of the complex EMT process. Thirteen transcription factors (TFs) expressed specifically in pre-EMT cells were required for EMT. Three TFs highest in the GRN specified and activated EMT (alx1, ets1, tbr) and the 10 TFs downstream of those (tel, erg, hex, tgif, snail, twist, foxn2/3, dri, foxb, foxo) were also required for EMT. No single TF functioned in all five sub-circuits, indicating that there is no EMT master regulator. Instead, the resulting sub-circuit topologies suggest EMT requires multiple simultaneous regulatory mechanisms: forward cascades, parallel inputs and positive-feedback lock downs. The interconnected and overlapping nature of the sub-circuits provides one explanation for the seamless orchestration by the embryo of cell state changes leading to successful EMT.

  13. Morphogenesis in sea urchin embryos: linking cellular events to gene regulatory network states

    Science.gov (United States)

    Lyons, Deidre; Kaltenbach, Stacy; McClay, David R.

    2013-01-01

    Gastrulation in the sea urchin begins with ingression of the primary mesenchyme cells (PMCs) at the vegetal pole of the embryo. After entering the blastocoel the PMCs migrate, form a syncitium, and synthesize the skeleton of the embryo. Several hours after the PMCs ingress the vegetal plate buckles to initiate invagination of the archenteron. That morphogenetic process occurs in several steps. The non-skeletogenic cells produce the initial inbending of the vegetal plate. Endoderm cells then rearrange and extend the length of the gut across the blastocoel to a target near the animal pole. Finally, cells that will form part of the midgut and hindgut are added to complete gastrulation. Later, the stomodeum invaginates from the oral ectoderm and fuses with the foregut to complete the archenteron. In advance of, and during these morphogenetic events an increasingly complex gene regulatory network controls the specification and the cell biological events that conduct the gastrulation movements. PMID:23801438

  14. State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

    Directory of Open Access Journals (Sweden)

    Tuqyah Abdullah Al Qazlan

    2015-01-01

    Full Text Available To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

  15. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

    Science.gov (United States)

    Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

    2017-12-19

    Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

  16. Mechanistically Distinct Pathways of Divergent Regulatory DNA Creation Contribute to Evolution of Human-Specific Genomic Regulatory Networks Driving Phenotypic Divergence of Homo sapiens.

    Science.gov (United States)

    Glinsky, Gennadi V

    2016-09-19

    Thousands of candidate human-specific regulatory sequences (HSRS) have been identified, supporting the hypothesis that unique to human phenotypes result from human-specific alterations of genomic regulatory networks. Collectively, a compendium of multiple diverse families of HSRS that are functionally and structurally divergent from Great Apes could be defined as the backbone of human-specific genomic regulatory networks. Here, the conservation patterns analysis of 18,364 candidate HSRS was carried out requiring that 100% of bases must remap during the alignments of human, chimpanzee, and bonobo sequences. A total of 5,535 candidate HSRS were identified that are: (i) highly conserved in Great Apes; (ii) evolved by the exaptation of highly conserved ancestral DNA; (iii) defined by either the acceleration of mutation rates on the human lineage or the functional divergence from non-human primates. The exaptation of highly conserved ancestral DNA pathway seems mechanistically distinct from the evolution of regulatory DNA segments driven by the species-specific expansion of transposable elements. Genome-wide proximity placement analysis of HSRS revealed that a small fraction of topologically associating domains (TADs) contain more than half of HSRS from four distinct families. TADs that are enriched for HSRS and termed rapidly evolving in humans TADs (revTADs) comprise 0.8-10.3% of 3,127 TADs in the hESC genome. RevTADs manifest distinct correlation patterns between placements of human accelerated regions, human-specific transcription factor-binding sites, and recombination rates. There is a significant enrichment within revTAD boundaries of hESC-enhancers, primate-specific CTCF-binding sites, human-specific RNAPII-binding sites, hCONDELs, and H3K4me3 peaks with human-specific enrichment at TSS in prefrontal cortex neurons (P sapiens is driven by the evolution of human-specific genomic regulatory networks via at least two mechanistically distinct pathways of creation of

  17. Tissue-specific expression and regulatory networks of pig microRNAome.

    Directory of Open Access Journals (Sweden)

    Paolo Martini

    Full Text Available BACKGROUND: Despite the economic and medical importance of the pig, knowledge about its genome organization, gene expression regulation, and molecular mechanisms involved in physiological processes is far from that achieved for mouse and rat, the two most used model organisms in biomedical research. MicroRNAs (miRNAs are a wide class of molecules that exert a recognized role in gene expression modulation, but only 280 miRNAs in pig have been characterized to date. RESULTS: We applied a novel computational approach to predict species-specific and conserved miRNAs in the pig genome, which were then subjected to experimental validation. We experimentally identified candidate miRNAs sequences grouped in high-confidence (424 and medium-confidence (353 miRNAs according to RNA-seq results. A group of miRNAs was also validated by PCR experiments. We established the subtle variability in expression of isomiRs and miRNA-miRNA star couples supporting a biological function for these molecules. Finally, miRNA and mRNA expression profiles produced from the same sample of 20 different tissue of the animal were combined, using a correlation threshold to filter miRNA-target predictions, to identify tissue-specific regulatory networks. CONCLUSIONS: Our data represent a significant progress in the current understanding of miRNAome in pig. The identification of miRNAs, their target mRNAs, and the construction of regulatory circuits will provide new insights into the complex biological networks in several tissues of this important animal model.

  18. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

    Science.gov (United States)

    Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

    2004-12-01

    As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

  19. Interactive visualization of gene regulatory networks with associated gene expression time series data

    NARCIS (Netherlands)

    Westenberg, M.A.; Hijum, van S.A.F.T.; Lulko, A.T.; Kuipers, O.P.; Roerdink, J.B.T.M.; Linsen, L.; Hagen, H.; Hamann, B.

    2008-01-01

    We present GENeVis, an application to visualize gene expression time series data in a gene regulatory network context. This is a network of regulator proteins that regulate the expression of their respective target genes. The networks are represented as graphs, in which the nodes represent genes,

  20. Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast.

    Directory of Open Access Journals (Sweden)

    Chun Ye

    2009-03-01

    Full Text Available Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.

  1. Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

    Directory of Open Access Journals (Sweden)

    Guo Zheng

    2006-01-01

    Full Text Available Abstract Background It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. Results In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network to address the underlying regulations of genes that can span any unit(s of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. Conclusion We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex

  2. Plant RNA Regulatory Network and RNA Granules in Virus Infection

    Directory of Open Access Journals (Sweden)

    Kristiina Mäkinen

    2017-12-01

    Full Text Available Regulation of post-transcriptional gene expression on mRNA level in eukaryotic cells includes translocation, translation, translational repression, storage, mRNA decay, RNA silencing, and nonsense-mediated decay. These processes are associated with various RNA-binding proteins and cytoplasmic ribonucleoprotein complexes many of which are conserved across eukaryotes. Microscopically visible aggregations formed by ribonucleoprotein complexes are termed RNA granules. Stress granules where the translationally inactive mRNAs are stored and processing bodies where mRNA decay may occur present the most studied RNA granule types. Diverse RNP-granules are increasingly being assigned important roles in viral infections. Although the majority of the molecular level studies on the role of RNA granules in viral translation and replication have been conducted in mammalian systems, some studies link also plant virus infection to RNA granules. An increasing body of evidence indicates that plant viruses require components of stress granules and processing bodies for their replication and translation, but how extensively the cellular mRNA regulatory network is utilized by plant viruses has remained largely enigmatic. Antiviral RNA silencing, which is an important regulator of viral RNA stability and expression in plants, is commonly counteracted by viral suppressors of RNA silencing. Some of the RNA silencing suppressors localize to cellular RNA granules and have been proposed to carry out their suppression functions there. Moreover, plant nucleotide-binding leucine-rich repeat protein-mediated virus resistance has been linked to enhanced processing body formation and translational repression of viral RNA. Many interesting questions relate to how the pathways of antiviral RNA silencing leading to viral RNA degradation and/or repression of translation, suppression of RNA silencing and viral RNA translation converge in plants and how different RNA granules and

  3. Plant RNA Regulatory Network and RNA Granules in Virus Infection.

    Science.gov (United States)

    Mäkinen, Kristiina; Lõhmus, Andres; Pollari, Maija

    2017-01-01

    Regulation of post-transcriptional gene expression on mRNA level in eukaryotic cells includes translocation, translation, translational repression, storage, mRNA decay, RNA silencing, and nonsense-mediated decay. These processes are associated with various RNA-binding proteins and cytoplasmic ribonucleoprotein complexes many of which are conserved across eukaryotes. Microscopically visible aggregations formed by ribonucleoprotein complexes are termed RNA granules. Stress granules where the translationally inactive mRNAs are stored and processing bodies where mRNA decay may occur present the most studied RNA granule types. Diverse RNP-granules are increasingly being assigned important roles in viral infections. Although the majority of the molecular level studies on the role of RNA granules in viral translation and replication have been conducted in mammalian systems, some studies link also plant virus infection to RNA granules. An increasing body of evidence indicates that plant viruses require components of stress granules and processing bodies for their replication and translation, but how extensively the cellular mRNA regulatory network is utilized by plant viruses has remained largely enigmatic. Antiviral RNA silencing, which is an important regulator of viral RNA stability and expression in plants, is commonly counteracted by viral suppressors of RNA silencing. Some of the RNA silencing suppressors localize to cellular RNA granules and have been proposed to carry out their suppression functions there. Moreover, plant nucleotide-binding leucine-rich repeat protein-mediated virus resistance has been linked to enhanced processing body formation and translational repression of viral RNA. Many interesting questions relate to how the pathways of antiviral RNA silencing leading to viral RNA degradation and/or repression of translation, suppression of RNA silencing and viral RNA translation converge in plants and how different RNA granules and their individual

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  6. Inference of gene regulatory networks from time series by Tsallis entropy

    Directory of Open Access Journals (Sweden)

    de Oliveira Evaldo A

    2011-05-01

    Full Text Available Abstract Background The inference of gene regulatory networks (GRNs from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information, a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5

  7. Low doses of ionizing radiation: Biological effects and regulatory control. Contributed papers

    International Nuclear Information System (INIS)

    1997-11-01

    The International Atomic Energy Agency and the World Health Organization, in cooperation with the United Nations Scientific Committee on the Effects of Atomic Radiation, organized an international conference on Low Doses of Ionizing Radiation: Biological Effects and Regulatory Control, held in seville, Spain, from 17 to 21 November 1997. This technical document contains concise papers submitted to the conference

  8. Low doses of ionizing radiation: Biological effects and regulatory control. Contributed papers

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-11-01

    The International Atomic Energy Agency and the World Health Organization, in cooperation with the United Nations Scientific Committee on the Effects of Atomic Radiation, organized an international conference on Low Doses of Ionizing Radiation: Biological Effects and Regulatory Control, held in seville, Spain, from 17 to 21 November 1997. This technical document contains concise papers submitted to the conference. Refs, figs, tabs.

  9. A comparative study of covariance selection models for the inference of gene regulatory networks.

    Science.gov (United States)

    Stifanelli, Patrizia F; Creanza, Teresa M; Anglani, Roberto; Liuzzi, Vania C; Mukherjee, Sayan; Schena, Francesco P; Ancona, Nicola

    2013-10-01

    The inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology. In this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) 'ℓ(2C)' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that ℓ(2C) outperformed the other two methods having the most predictive partial correlation estimates and the highest values of sensitivity to infer conditional dependencies between genes even when a few number of observations was available. The application of this method for inferring gene networks of the isoprenoid biosynthesis pathways in Arabidopsis thaliana allowed to enlighten a negative partial correlation coefficient between the two hubs in the two isoprenoid pathways and, more importantly, provided an evidence of cross-talk between genes in the plastidial and the cytosolic pathways. When applied to gene expression data relative to a signature of HRAS oncogene in human cell cultures, the method revealed 9 genes (p-value<0.0005) directly interacting with HRAS, sharing the same Ras-responsive binding site for the transcription factor RREB1. This result suggests that the transcriptional activation of these genes is mediated by a common transcription factor downstream of Ras signaling. Software implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip. Copyright © 2013 The Authors. Published by

  10. Systems Biology of Metabolic Networks: Uncovering Regulatory and stoichiometric Principles

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb

    2007-01-01

    central rolle i funktionen af alle levende celler, er der også stor interesse for metoder til analyse af metabolske netværk i naturvidenskabelige studier samt i forbindelse med kortlægning af mekanismerne bag stofskifte relaterede sygdomme. I dette studium er der udviklet forskellige nye bioinformatiske...

  11. Construction and analysis of circular RNA molecular regulatory networks in liver cancer.

    Science.gov (United States)

    Ren, Shuangchun; Xin, Zhuoyuan; Xu, Yinyan; Xu, Jianting; Wang, Guoqing

    2017-01-01

    Liver cancer is the sixth most prevalent cancer, and the third most frequent cause of cancer-related deaths. Circular RNAs (circRNAs), a kind of special endogenous ncRNAs, have been coming back to the forefront of cancer genomics research. In this study, we used a systems biology approach to construct and analyze the circRNA molecular regulatory networks in the context of liver cancer. We detected a total of 127 differentially expressed circRNAs and 3,235 differentially expressed mRNAs. We selected the top-5 upregulated circRNAs to construct a circRNA-miRNA-mRNA network. We enriched the pathways and gene ontology items and determined their participation in cancer-related pathways such as p53 signaling pathway and pathways involved in angiogenesis and cell cycle. Quantitative real-time PCR was performed to verify the top-five circRNAs. ROC analysis showed circZFR, circFUT8, circIPO11 could significantly distinguish the cancer samples, with an AUC of 0.7069, 0.7575, and 0.7103, respectively. Our results suggest the circRNA-miRNA-mRNA network may help us further understand the molecular mechanisms of tumor progression in liver cancer, and reveal novel biomarkers and therapeutic targets.

  12. A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

    Energy Technology Data Exchange (ETDEWEB)

    Santra, Tapesh, E-mail: tapesh.santra@ucd.ie [Systems Biology Ireland, University College Dublin, Dublin (Ireland)

    2014-05-20

    Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

  13. A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

    International Nuclear Information System (INIS)

    Santra, Tapesh

    2014-01-01

    Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

  14. Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

    Science.gov (United States)

    Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

    2015-01-01

    Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

  15. RNA SURVEILLANCE– AN EMERGING ROLE FOR RNA REGULATORY NETWORKS IN AGING

    OpenAIRE

    Montano, Monty; Long, Kimberly

    2010-01-01

    In this review, we describe recent advances in the field of RNA regulatory biology and relate these advances to aging science. We introduce a new term, RNA surveillance, an RNA regulatory process that is conserved in metazoans, and describe how RNA surveillance represents molecular cross-talk between two emerging RNA regulatory systems – RNA interference and RNA editing. We discuss how RNA surveillance mechanisms influence mRNA and microRNA expression and activity during lifespan. Additionall...

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

    Science.gov (United States)

    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.

  17. Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli

    DEFF Research Database (Denmark)

    Seo, Sang Woo; Kim, Donghyuk; Latif, Haythem

    2014-01-01

    The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response...

  18. Uncovering transcription factor and microRNA risk regulatory pathways associated with osteoarthritis by network analysis.

    Science.gov (United States)

    Song, Zhenhua; Zhang, Chi; He, Lingxiao; Sui, Yanfang; Lin, Xiafei; Pan, Jingjing

    2018-05-01

    Osteoarthritis (OA) is the most common form of joint disease. The development of inflammation have been considered to play a key role during the progression of OA. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, deciphering these risk regulatory pathways is critical for elucidating the mechanisms underlying OA. We constructed an OA-specific regulatory network by integrating comprehensive curated transcription and post-transcriptional resource involving transcription factor (TF) and microRNA (miRNA). To deepen our understanding of underlying molecular mechanisms of OA, we developed an integrated systems approach to identify OA-specific risk regulatory pathways. In this study, we identified 89 significantly differentially expressed genes between normal and inflamed areas of OA patients. We found the OA-specific regulatory network was a standard scale-free network with small-world properties. It significant enriched many immune response-related functions including leukocyte differentiation, myeloid differentiation and T cell activation. Finally, 141 risk regulatory pathways were identified based on OA-specific regulatory network, which contains some known regulator of OA. The risk regulatory pathways may provide clues for the etiology of OA and be a potential resource for the discovery of novel OA-associated disease genes. Copyright © 2018 Elsevier Inc. All rights reserved.

  19. Regional and International Networking to Support the Energy Regulatory Commission of Thailand

    Energy Technology Data Exchange (ETDEWEB)

    Lavansiri, Direk; Bull, Trevor

    2010-09-15

    The Energy Regulatory Commission of Thailand is a new regulatory agency. The structure of the energy sector; the tradition of administration; and, the lack of access to experienced personnel in Thailand all pose particular challenges. The Commission is meeting these challenges through regional and international networking to assist in developing policies and procedures that allow it to meet international benchmarks.

  20. A framework to find the logic backbone of a biological network.

    Science.gov (United States)

    Maheshwari, Parul; Albert, Réka

    2017-12-06

    Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network. In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures. The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.

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

    Science.gov (United States)

    Cojocaru, Radu Ionut

    lattice at zero temperature and then we apply this formalism to the K-SAT problem defined in Chapter 1. The phase transition which physicists study often corresponds to a change in the computational complexity of the corresponding computer science problem. Chapter 3 presents phase transitions which are specific to the problems discussed in Chapter 1 and also known results for the K-SAT problem. We discuss the replica method and experimental evidences of replica symmetry breaking. The physics approach to hard problems is based on replica methods which are difficult to understand. In Chapter 4 we develop novel methods for studying hard problems using methods similar to the message passing techniques that were discussed in Chapter 2. Although we concentrated on the symmetric case, cavity methods show promise for generalizing our methods to the un-symmetric case. As has been highlighted by John Hopfield, several key features of biological systems are not shared by physical systems. Although living entities follow the laws of physics and chemistry, the fact that organisms adapt and reproduce introduces an essential ingredient that is missing in the physical sciences. In order to extract information from networks many algorithm have been developed. In Chapter 5 we apply polynomial algorithms like minimum spanning tree in order to study and construct gene regulatory networks from experimental data. As future work we propose the use of algorithms like min-cut/max-flow and Dijkstra for understanding key properties of these networks.

  2. Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    He Weiming

    2010-07-01

    Full Text Available Abstract Background Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions. Results Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways. Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods. Conclusions Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

  3. Networks in biological systems: An investigation of the Gene Ontology as an evolving network

    International Nuclear Information System (INIS)

    Coronnello, C; Tumminello, M; Micciche, S; Mantegna, R.N.

    2009-01-01

    Many biological systems can be described as networks where different elements interact, in order to perform biological processes. We introduce a network associated with the Gene Ontology. Specifically, we construct a correlation-based network where the vertices are the terms of the Gene Ontology and the link between each two terms is weighted on the basis of the number of genes that they have in common. We analyze a filtered network obtained from the correlation-based network and we characterize its evolution over different releases of the Gene Ontology.

  4. Synthetic tetracycline-inducible regulatory networks: computer-aided design of dynamic phenotypes

    Directory of Open Access Journals (Sweden)

    Kaznessis Yiannis N

    2007-01-01

    Full Text Available Abstract Background Tightly regulated gene networks, precisely controlling the expression of protein molecules, have received considerable interest by the biomedical community due to their promising applications. Among the most well studied inducible transcription systems are the tetracycline regulatory expression systems based on the tetracycline resistance operon of Escherichia coli, Tet-Off (tTA and Tet-On (rtTA. Despite their initial success and improved designs, limitations still persist, such as low inducer sensitivity. Instead of looking at these networks statically, and simply changing or mutating the promoter and operator regions with trial and error, a systematic investigation of the dynamic behavior of the network can result in rational design of regulatory gene expression systems. Sophisticated algorithms can accurately capture the dynamical behavior of gene networks. With computer aided design, we aim to improve the synthesis of regulatory networks and propose new designs that enable tighter control of expression. Results In this paper we engineer novel networks by recombining existing genes or part of genes. We synthesize four novel regulatory networks based on the Tet-Off and Tet-On systems. We model all the known individual biomolecular interactions involved in transcription, translation, regulation and induction. With multiple time-scale stochastic-discrete and stochastic-continuous models we accurately capture the transient and steady state dynamics of these networks. Important biomolecular interactions are identified and the strength of the interactions engineered to satisfy design criteria. A set of clear design rules is developed and appropriate mutants of regulatory proteins and operator sites are proposed. Conclusion The complexity of biomolecular interactions is accurately captured through computer simulations. Computer simulations allow us to look into the molecular level, portray the dynamic behavior of gene regulatory

  5. Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease.

    Science.gov (United States)

    Torres, Matthew P; Dewhurst, Henry; Sundararaman, Niveda

    2016-11-01

    Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable

  6. Potential energy landscape and robustness of a gene regulatory network: toggle switch.

    Directory of Open Access Journals (Sweden)

    Keun-Young Kim

    2007-03-01

    Full Text Available Finding a multidimensional potential landscape is the key for addressing important global issues, such as the robustness of cellular networks. We have uncovered the underlying potential energy landscape of a simple gene regulatory network: a toggle switch. This was realized by explicitly constructing the steady state probability of the gene switch in the protein concentration space in the presence of the intrinsic statistical fluctuations due to the small number of proteins in the cell. We explored the global phase space for the system. We found that the protein synthesis rate and the unbinding rate of proteins to the gene were small relative to the protein degradation rate; the gene switch is monostable with only one stable basin of attraction. When both the protein synthesis rate and the unbinding rate of proteins to the gene are large compared with the protein degradation rate, two global basins of attraction emerge for a toggle switch. These basins correspond to the biologically stable functional states. The potential energy barrier between the two basins determines the time scale of conversion from one to the other. We found as the protein synthesis rate and protein unbinding rate to the gene relative to the protein degradation rate became larger, the potential energy barrier became larger. This also corresponded to systems with less noise or the fluctuations on the protein numbers. It leads to the robustness of the biological basins of the gene switches. The technique used here is general and can be applied to explore the potential energy landscape of the gene networks.

  7. Gene regulatory networks in lactation: identification of global principles using bioinformatics

    Directory of Open Access Journals (Sweden)

    Pollard Katherine S

    2007-11-01

    Full Text Available Abstract Background The molecular events underlying mammary development during pregnancy, lactation, and involution are incompletely understood. Results Mammary gland microarray data, cellular localization data, protein-protein interactions, and literature-mined genes were integrated and analyzed using statistics, principal component analysis, gene ontology analysis, pathway analysis, and network analysis to identify global biological principles that govern molecular events during pregnancy, lactation, and involution. Conclusion Several key principles were derived: (1 nearly a third of the transcriptome fluctuates to build, run, and disassemble the lactation apparatus; (2 genes encoding the secretory machinery are transcribed prior to lactation; (3 the diversity of the endogenous portion of the milk proteome is derived from fewer than 100 transcripts; (4 while some genes are differentially transcribed near the onset of lactation, the lactation switch is primarily post-transcriptionally mediated; (5 the secretion of materials during lactation occurs not by up-regulation of novel genomic functions, but by widespread transcriptional suppression of functions such as protein degradation and cell-environment communication; (6 the involution switch is primarily transcriptionally mediated; and (7 during early involution, the transcriptional state is partially reverted to the pre-lactation state. A new hypothesis for secretory diminution is suggested – milk production gradually declines because the secretory machinery is not transcriptionally replenished. A comprehensive network of protein interactions during lactation is assembled and new regulatory gene targets are identified. Less than one fifth of the transcriptionally regulated nodes in this lactation network have been previously explored in the context of lactation. Implications for future research in mammary and cancer biology are discussed.

  8. Visual analysis of transcriptome data in the context of anatomical structures and biological networks

    Directory of Open Access Journals (Sweden)

    Astrid eJunker

    2012-11-01

    Full Text Available 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.

  9. [Sporulation or competence development? A genetic regulatory network model of cell-fate determination in Bacillus subtilis].

    Science.gov (United States)

    Lu, Zhenghui; Zhou, Yuling; Zhang, Xiaozhou; Zhang, Guimin

    2015-11-01

    Bacillus subtilis is a generally recognized as safe (GRAS) strain that has been widely used in industries including fodder, food, and biological control. In addition, B. subtilis expression system also plays a significant role in the production of industrial enzymes. However, its application is limited by its low sporulation frequency and transformation efficiency. Immense studies have been done on interpreting the molecular mechanisms of sporulation and competence development, whereas only few of them were focused on improving sporulation frequency and transformation efficiency of B. subtilis by genetic modification. The main challenge is that sporulation and competence development, as the two major developmental events in the stationary phase of B. subtilis, are regulated by the complicated intracellular genetic regulatory systems. In addition, mutual regulatory mechanisms also exist in these two developmental events. With the development of genetic and metabolic engineering, constructing genetic regulatory networks is currently one of the most attractive research fields, together with the genetic information of cell growth, metabolism, and development, to guide the industrial application. In this review, the mechanisms of sporulation and competence development of B. subtilis, their interactions, and the genetic regulation of cell growth were interpreted. In addition, the roles of these regulatory networks in guiding basic and applied research of B. subtilis and its related species were discussed.

  10. Using consensus bayesian network to model the reactive oxygen species regulatory pathway.

    Directory of Open Access Journals (Sweden)

    Liangdong Hu

    Full Text Available Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.

  11. Using biological networks to improve our understanding of infectious diseases

    Directory of Open Access Journals (Sweden)

    Nicola J. Mulder

    2014-08-01

    Full Text Available Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.

  12. Analysis and logical modeling of biological signaling transduction networks

    Science.gov (United States)

    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.

  13. Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations.

    Science.gov (United States)

    Wu, Shuang; Liu, Zhi-Ping; Qiu, Xing; Wu, Hulin

    2014-01-01

    The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.

  14. A comparative analysis on computational methods for fitting an ERGM to biological network data

    Directory of Open Access Journals (Sweden)

    Sudipta Saha

    2015-03-01

    Full Text Available Exponential random graph models (ERGM based on graph theory are useful in studying global biological network structure using its local properties. However, computational methods for fitting such models are sensitive to the type, structure and the number of the local features of a network under study. In this paper, we compared computational methods for fitting an ERGM with local features of different types and structures. Two commonly used methods, such as the Markov Chain Monte Carlo Maximum Likelihood Estimation and the Maximum Pseudo Likelihood Estimation are considered for estimating the coefficients of network attributes. We compared the estimates of observed network to our random simulated network using both methods under ERGM. The motivation was to ascertain the extent to which an observed network would deviate from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of Escherichia coli (E. coli.

  15. Non-Hermitian localization in biological networks.

    Science.gov (United States)

    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.

  16. Emergence of communication in socio-biological networks

    CERN Document Server

    Berea, Anamaria

    2018-01-01

    This book integrates current advances in biology, economics of information and linguistics research through applications using agent-based modeling and social network analysis to develop scenarios of communication and language emergence in the social aspects of biological communications. The book presents a model of communication emergence that can be applied both to human and non-human living organism networks. The model is based on economic concepts and individual behavior fundamental for the study of trust and reputation networks in social science, particularly in economics; it is also based on the theory of the emergence of norms and historical path dependence that has been influential in institutional economics. Also included are mathematical models and code for agent-based models to explore various scenarios of language evolution, as well as a computer application that explores language and communication in biological versus social organisms, and the emergence of various meanings and grammars in human ...

  17. Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds

    Directory of Open Access Journals (Sweden)

    Neelam Redekar

    2017-11-01

    Full Text Available A dominant loss of function mutation in myo-inositol phosphate synthase (MIPS gene and recessive loss of function mutations in two multidrug resistant protein type-ABC transporter genes not only reduce the seed phytic acid levels in soybean, but also affect the pathways associated with seed development, ultimately resulting in low emergence. To understand the regulatory mechanisms and identify key genes that intervene in the seed development process in low phytic acid crops, we performed computational inference of gene regulatory networks in low and normal phytic acid soybeans using a time course transcriptomic data and multiple network inference algorithms. We identified a set of putative candidate transcription factors and their regulatory interactions with genes that have functions in myo-inositol biosynthesis, auxin-ABA signaling, and seed dormancy. We evaluated the performance of our unsupervised network inference method by comparing the predicted regulatory network with published regulatory interactions in Arabidopsis. Some contrasting regulatory interactions were observed in low phytic acid mutants compared to non-mutant lines. These findings provide important hypotheses on expression regulation of myo-inositol metabolism and phytohormone signaling in developing low phytic acid soybeans. The computational pipeline used for unsupervised network learning in this study is provided as open source software and is freely available at https://lilabatvt.github.io/LPANetwork/.

  18. Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease*

    Science.gov (United States)

    Dewhurst, Henry; Sundararaman, Niveda

    2016-01-01

    Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable

  19. Toward Petascale Biologically Plausible Neural Networks

    Science.gov (United States)

    Long, Lyle

    This talk will describe an approach to achieving petascale neural networks. Artificial intelligence has been oversold for many decades. Computers in the beginning could only do about 16,000 operations per second. Computer processing power, however, has been doubling every two years thanks to Moore's law, and growing even faster due to massively parallel architectures. Finally, 60 years after the first AI conference we have computers on the order of the performance of the human brain (1016 operations per second). The main issues now are algorithms, software, and learning. We have excellent models of neurons, such as the Hodgkin-Huxley model, but we do not know how the human neurons are wired together. With careful attention to efficient parallel computing, event-driven programming, table lookups, and memory minimization massive scale simulations can be performed. The code that will be described was written in C + + and uses the Message Passing Interface (MPI). It uses the full Hodgkin-Huxley neuron model, not a simplified model. It also allows arbitrary network structures (deep, recurrent, convolutional, all-to-all, etc.). The code is scalable, and has, so far, been tested on up to 2,048 processor cores using 107 neurons and 109 synapses.

  20. A phenotypic profile of the Candida albicans regulatory network.

    Directory of Open Access Journals (Sweden)

    Oliver R Homann

    2009-12-01

    Full Text Available Candida albicans is a normal resident of the gastrointestinal tract and also the most prevalent fungal pathogen of humans. It last shared a common ancestor with the model yeast Saccharomyces cerevisiae over 300 million years ago. We describe a collection of 143 genetically matched strains of C. albicans, each of which has been deleted for a specific transcriptional regulator. This collection represents a large fraction of the non-essential transcription circuitry. A phenotypic profile for each mutant was developed using a screen of 55 growth conditions. The results identify the biological roles of many individual transcriptional regulators; for many, this work represents the first description of their functions. For example, a quarter of the strains showed altered colony formation, a phenotype reflecting transitions among yeast, pseudohyphal, and hyphal cell forms. These transitions, which have been closely linked to pathogenesis, have been extensively studied, yet our work nearly doubles the number of transcriptional regulators known to influence them. As a second example, nearly a quarter of the knockout strains affected sensitivity to commonly used antifungal drugs; although a few transcriptional regulators have previously been implicated in susceptibility to these drugs, our work indicates many additional mechanisms of sensitivity and resistance. Finally, our results inform how transcriptional networks evolve. Comparison with the existing S. cerevisiae data (supplemented by additional S. cerevisiae experiments reported here allows the first systematic analysis of phenotypic conservation by orthologous transcriptional regulators over a large evolutionary distance. We find that, despite the many specific wiring changes documented between these species, the general phenotypes of orthologous transcriptional regulator knockouts are largely conserved. These observations support the idea that many wiring changes affect the detailed architecture of

  1. A phenotypic profile of the Candida albicans regulatory network.

    Science.gov (United States)

    Homann, Oliver R; Dea, Jeanselle; Noble, Suzanne M; Johnson, Alexander D

    2009-12-01

    Candida albicans is a normal resident of the gastrointestinal tract and also the most prevalent fungal pathogen of humans. It last shared a common ancestor with the model yeast Saccharomyces cerevisiae over 300 million years ago. We describe a collection of 143 genetically matched strains of C. albicans, each of which has been deleted for a specific transcriptional regulator. This collection represents a large fraction of the non-essential transcription circuitry. A phenotypic profile for each mutant was developed using a screen of 55 growth conditions. The results identify the biological roles of many individual transcriptional regulators; for many, this work represents the first description of their functions. For example, a quarter of the strains showed altered colony formation, a phenotype reflecting transitions among yeast, pseudohyphal, and hyphal cell forms. These transitions, which have been closely linked to pathogenesis, have been extensively studied, yet our work nearly doubles the number of transcriptional regulators known to influence them. As a second example, nearly a quarter of the knockout strains affected sensitivity to commonly used antifungal drugs; although a few transcriptional regulators have previously been implicated in susceptibility to these drugs, our work indicates many additional mechanisms of sensitivity and resistance. Finally, our results inform how transcriptional networks evolve. Comparison with the existing S. cerevisiae data (supplemented by additional S. cerevisiae experiments reported here) allows the first systematic analysis of phenotypic conservation by orthologous transcriptional regulators over a large evolutionary distance. We find that, despite the many specific wiring changes documented between these species, the general phenotypes of orthologous transcriptional regulator knockouts are largely conserved. These observations support the idea that many wiring changes affect the detailed architecture of the circuit, but

  2. Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.

    Science.gov (United States)

    Jia, Bin; Wang, Xiaodong

    2013-12-17

    : The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF3), and the fifth-degree cubature Kalman filter (CKF5) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.

  3. Neural network models: from biology to many - body phenomenology

    International Nuclear Information System (INIS)

    Clark, J.W.

    1993-01-01

    Theoretical work in neural networks has a strange feel for most physicists. In some cases the aspect of design becomes paramount. More comfortable ground at least for many body theorists may be found in realistic biological simulation, although the complexity of most problems is so awesome that incisive results will be hard won. It has also shown the impressive capabilities of artificial networks in pattern recognition and classification may be exploited to solve management problems in experimental physics and for discovery of radically new theoretical description of physical systems. This advance represents an important step towards the ultimate goal of neuro biological paradigm. (A.B.)

  4. Network and Database Security: Regulatory Compliance, Network, and Database Security - A Unified Process and Goal

    Directory of Open Access Journals (Sweden)

    Errol A. Blake

    2007-12-01

    Full Text Available Database security has evolved; data security professionals have developed numerous techniques and approaches to assure data confidentiality, integrity, and availability. This paper will show that the Traditional Database Security, which has focused primarily on creating user accounts and managing user privileges to database objects are not enough to protect data confidentiality, integrity, and availability. This paper is a compilation of different journals, articles and classroom discussions will focus on unifying the process of securing data or information whether it is in use, in storage or being transmitted. Promoting a change in Database Curriculum Development trends may also play a role in helping secure databases. This paper will take the approach that if one make a conscientious effort to unifying the Database Security process, which includes Database Management System (DBMS selection process, following regulatory compliances, analyzing and learning from the mistakes of others, Implementing Networking Security Technologies, and Securing the Database, may prevent database breach.

  5. GraphAlignment: Bayesian pairwise alignment of biological networks

    Czech Academy of Sciences Publication Activity Database

    Kolář, Michal; Meier, J.; Mustonen, V.; Lässig, M.; Berg, J.

    2012-01-01

    Roč. 6, November 21 (2012) ISSN 1752-0509 Grant - others:Deutsche Forschungsgemeinschaft(DE) SFB 680; Deutsche Forschungsgemeinschaft(DE) SFB-TR12; Deutsche Forschungsgemeinschaft(DE) BE 2478/2-1 Institutional research plan: CEZ:AV0Z50520514 Keywords : Graph alignment * Biological networks * Parameter estimation * Bioconductor Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.982, year: 2012

  6. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

    Directory of Open Access Journals (Sweden)

    Sapna Kumari

    Full Text Available 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.

  7. Anticipated Ethics and Regulatory Challenges in PCORnet: The National Patient-Centered Clinical Research Network.

    Science.gov (United States)

    Ali, Joseph; Califf, Robert; Sugarman, Jeremy

    2016-01-01

    PCORnet, the National Patient-Centered Clinical Research Network, seeks to establish a robust national health data network for patient-centered comparative effectiveness research. This article reports the results of a PCORnet survey designed to identify the ethics and regulatory challenges anticipated in network implementation. A 12-item online survey was developed by leadership of the PCORnet Ethics and Regulatory Task Force; responses were collected from the 29 PCORnet networks. The most pressing ethics issues identified related to informed consent, patient engagement, privacy and confidentiality, and data sharing. High priority regulatory issues included IRB coordination, privacy and confidentiality, informed consent, and data sharing. Over 150 IRBs and five different approaches to managing multisite IRB review were identified within PCORnet. Further empirical and scholarly work, as well as practical and policy guidance, is essential if important initiatives that rely on comparative effectiveness research are to move forward.

  8. Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Ji Wei

    2010-10-01

    Full Text Available Abstract Background Microarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data. Results In this study, we propose a new discretization method "bikmeans", and compare its performance with four other widely-used discretization methods using different datasets, modeling algorithms and number of intervals. Sensitivities, specificities and total accuracies were calculated and statistical analysis was carried out. Bikmeans method always gave high total accuracies. Conclusions Our results indicate that proper discretization methods can consistently improve gene regulatory network inference independent of network modeling algorithms and datasets. Our new method, bikmeans, resulted in significant better total accuracies than other methods.

  9. Towards the understanding of network information processing in biology

    Science.gov (United States)

    Singh, Vijay

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

  10. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Xiangyun Xiao

    Full Text Available The reconstruction of gene regulatory networks (GRNs from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM, experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

  11. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

    Science.gov (United States)

    Xiao, Xiangyun; Zhang, Wei; Zou, Xiufen

    2015-01-01

    The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

  12. Regulatory requirements for groundwater monitoring networks at hazardous waste sites

    International Nuclear Information System (INIS)

    Keller, J.F.

    1989-10-01

    In the absence of an explicit national mandate to protect groundwater quality, operators of active and inactive hazardous waste sites must use a number of statutes and regulations as guidance for detecting, correcting, and preventing groundwater contamination. The objective of this paper is to provide a framework of the technical and regulatory considerations that are important to the development of groundwater monitoring programs at hazardous waste sites. The technical site-specific needs and regulatory considerations, including existing groundwater standards and classifications, will be presented. 14 refs., 2 tabs

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

    Science.gov (United States)

    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

  14. Uncovering Biological Network Function via Graphlet Degree Signatures

    Directory of Open Access Journals (Sweden)

    Nataša Pržulj

    2008-01-01

    Full Text Available Motivation: Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker’s yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.Results: We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein’s local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.Availability: Data is available upon request.

  15. Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks.

    Science.gov (United States)

    Fogelmark, Karl; Peterson, Carsten; Troein, Carl

    2016-01-01

    Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks.

  16. Yeast systems biology to unravel the network of life

    DEFF Research Database (Denmark)

    Mustacchi, Roberta; Hohmann, S; Nielsen, Jens

    2006-01-01

    Systems biology focuses on obtaining a quantitative description of complete biological systems, even complete cellular function. In this way, it will be possible to perform computer-guided design of novel drugs, advanced therapies for treatment of complex diseases, and to perform in silico design....... Furthermore, it serves as an industrial workhorse for production of a wide range of chemicals and pharmaceuticals. Systems biology involves the combination of novel experimental techniques from different disciplines as well as functional genomics, bioinformatics and mathematical modelling, and hence no single...... laboratory has access to all the necessary competences. For this reason the Yeast Systems Biology Network (YSBN) has been established. YSBN will coordinate research efforts, in yeast systems biology and, through the recently obtained EU funding for a Coordination Action, it will be possible to set...

  17. A genomic approach to identify regulatory nodes in the transcriptional network of systemic acquired resistance in plants.

    Directory of Open Access Journals (Sweden)

    Dong Wang

    2006-11-01

    Full Text Available Many biological processes are controlled by intricate networks of transcriptional regulators. With the development of microarray technology, transcriptional changes can be examined at the whole-genome level. However, such analysis often lacks information on the hierarchical relationship between components of a given system. Systemic acquired resistance (SAR is an inducible plant defense response involving a cascade of transcriptional events induced by salicylic acid through the transcription cofactor NPR1. To identify additional regulatory nodes in the SAR network, we performed microarray analysis on Arabidopsis plants expressing the NPR1-GR (glucocorticoid receptor fusion protein. Since nuclear translocation of NPR1-GR requires dexamethasone, we were able to control NPR1-dependent transcription and identify direct transcriptional targets of NPR1. We show that NPR1 directly upregulates the expression of eight WRKY transcription factor genes. This large family of 74 transcription factors has been implicated in various defense responses, but no specific WRKY factor has been placed in the SAR network. Identification of NPR1-regulated WRKY factors allowed us to perform in-depth genetic analysis on a small number of WRKY factors and test well-defined phenotypes of single and double mutants associated with NPR1. Among these WRKY factors we found both positive and negative regulators of SAR. This genomics-directed approach unambiguously positioned five WRKY factors in the complex transcriptional regulatory network of SAR. Our work not only discovered new transcription regulatory components in the signaling network of SAR but also demonstrated that functional studies of large gene families have to take into consideration sequence similarity as well as the expression patterns of the candidates.

  18. Bayesian error analysis model for reconstructing transcriptional regulatory networks

    OpenAIRE

    Sun, Ning; Carroll, Raymond J.; Zhao, Hongyu

    2006-01-01

    Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical–chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse typ...

  19. Reachability Analysis in Probabilistic Biological Networks.

    Science.gov (United States)

    Gabr, Haitham; Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2015-01-01

    Extra-cellular molecules trigger a response inside the cell by initiating a signal at special membrane receptors (i.e., sources), which is then transmitted to reporters (i.e., targets) through various chains of interactions among proteins. Understanding whether such a signal can reach from membrane receptors to reporters is essential in studying the cell response to extra-cellular events. This problem is drastically complicated due to the unreliability of the interaction data. In this paper, we develop a novel method, called PReach (Probabilistic Reachability), that precisely computes the probability that a signal can reach from a given collection of receptors to a given collection of reporters when the underlying signaling network is uncertain. This is a very difficult computational problem with no known polynomial-time solution. PReach represents each uncertain interaction as a bi-variate polynomial. It transforms the reachability problem to a polynomial multiplication problem. We introduce novel polynomial collapsing operators that associate polynomial terms with possible paths between sources and targets as well as the cuts that separate sources from targets. These operators significantly shrink the number of polynomial terms and thus the running time. PReach has much better time complexity than the recent solutions for this problem. Our experimental results on real data sets demonstrate that this improvement leads to orders of magnitude of reduction in the running time over the most recent methods. Availability: All the data sets used, the software implemented and the alignments found in this paper are available at http://bioinformatics.cise.ufl.edu/PReach/.

  20. Challenges for modeling global gene regulatory networks during development: insights from Drosophila.

    Science.gov (United States)

    Wilczynski, Bartek; Furlong, Eileen E M

    2010-04-15

    Development is regulated by dynamic patterns of gene expression, which are orchestrated through the action of complex gene regulatory networks (GRNs). Substantial progress has been made in modeling transcriptional regulation in recent years, including qualitative "coarse-grain" models operating at the gene level to very "fine-grain" quantitative models operating at the biophysical "transcription factor-DNA level". Recent advances in genome-wide studies have revealed an enormous increase in the size and complexity or GRNs. Even relatively simple developmental processes can involve hundreds of regulatory molecules, with extensive interconnectivity and cooperative regulation. This leads to an explosion in the number of regulatory functions, effectively impeding Boolean-based qualitative modeling approaches. At the same time, the lack of information on the biophysical properties for the majority of transcription factors within a global network restricts quantitative approaches. In this review, we explore the current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks. We suggest to integrate coarse- and find-grain approaches to model gene regulatory networks in cis. We focus on two very well-studied examples from Drosophila, which likely represent typical developmental regulatory modules across metazoans. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  1. Cooperative adaptive responses in gene regulatory networks with many degrees of freedom.

    Science.gov (United States)

    Inoue, Masayo; Kaneko, Kunihiko

    2013-04-01

    Cells generally adapt to environmental changes by first exhibiting an immediate response and then gradually returning to their original state to achieve homeostasis. Although simple network motifs consisting of a few genes have been shown to exhibit such adaptive dynamics, they do not reflect the complexity of real cells, where the expression of a large number of genes activates or represses other genes, permitting adaptive behaviors. Here, we investigated the responses of gene regulatory networks containing many genes that have undergone numerical evolution to achieve high fitness due to the adaptive response of only a single target gene; this single target gene responds to changes in external inputs and later returns to basal levels. Despite setting a single target, most genes showed adaptive responses after evolution. Such adaptive dynamics were not due to common motifs within a few genes; even without such motifs, almost all genes showed adaptation, albeit sometimes partial adaptation, in the sense that expression levels did not always return to original levels. The genes split into two groups: genes in the first group exhibited an initial increase in expression and then returned to basal levels, while genes in the second group exhibited the opposite changes in expression. From this model, genes in the first group received positive input from other genes within the first group, but negative input from genes in the second group, and vice versa. Thus, the adaptation dynamics of genes from both groups were consolidated. This cooperative adaptive behavior was commonly observed if the number of genes involved was larger than the order of ten. These results have implications in the collective responses of gene expression networks in microarray measurements of yeast Saccharomyces cerevisiae and the significance to the biological homeostasis of systems with many components.

  2. An electronic regulatory document management system for a clinical trial network.

    Science.gov (United States)

    Zhao, Wenle; Durkalski, Valerie; Pauls, Keith; Dillon, Catherine; Kim, Jaemyung; Kolk, Deneil; Silbergleit, Robert; Stevenson, Valerie; Palesch, Yuko

    2010-01-01

    A computerized regulatory document management system has been developed as a module in a comprehensive Clinical Trial Management System (CTMS) designed for an NIH-funded clinical trial network in order to more efficiently manage and track regulatory compliance. Within the network, several institutions and investigators are involved in multiple trials, and each trial has regulatory document requirements. Some of these documents are trial specific while others apply across multiple trials. The latter causes a possible redundancy in document collection and management. To address these and other related challenges, a central regulatory document management system was designed. This manuscript shares the design of the system as well as examples of it use in current studies. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  3. Vision from next generation sequencing: multi-dimensional genome-wide analysis for producing gene regulatory networks underlying retinal development, aging and disease.

    Science.gov (United States)

    Yang, Hyun-Jin; Ratnapriya, Rinki; Cogliati, Tiziana; Kim, Jung-Woong; Swaroop, Anand

    2015-05-01

    Genomics and genetics have invaded all aspects of biology and medicine, opening uncharted territory for scientific exploration. The definition of "gene" itself has become ambiguous, and the central dogma is continuously being revised and expanded. Computational biology and computational medicine are no longer intellectual domains of the chosen few. Next generation sequencing (NGS) technology, together with novel methods of pattern recognition and network analyses, has revolutionized the way we think about fundamental biological mechanisms and cellular pathways. In this review, we discuss NGS-based genome-wide approaches that can provide deeper insights into retinal development, aging and disease pathogenesis. We first focus on gene regulatory networks (GRNs) that govern the differentiation of retinal photoreceptors and modulate adaptive response during aging. Then, we discuss NGS technology in the context of retinal disease and develop a vision for therapies based on network biology. We should emphasize that basic strategies for network construction and analyses can be transported to any tissue or cell type. We believe that specific and uniform guidelines are required for generation of genome, transcriptome and epigenome data to facilitate comparative analysis and integration of multi-dimensional data sets, and for constructing networks underlying complex biological processes. As cellular homeostasis and organismal survival are dependent on gene-gene and gene-environment interactions, we believe that network-based biology will provide the foundation for deciphering disease mechanisms and discovering novel drug targets for retinal neurodegenerative diseases. Published by Elsevier Ltd.

  4. [Application of network biology on study of traditional Chinese medicine].

    Science.gov (United States)

    Tian, Sai-Sai; Yang, Jian; Zhao, Jing; Zhang, Wei-Dong

    2018-01-01

    With the completion of the human genome project, people have gradually recognized that the functions of the biological system are fulfilled through network-type interaction between genes, proteins and small molecules, while complex diseases are caused by the imbalance of biological processes due to a number of gene expression disorders. These have contributed to the rise of the concept of the "multi-target" drug discovery. Treatment and diagnosis of traditional Chinese medicine are based on holism and syndrome differentiation. At the molecular level, traditional Chinese medicine is characterized by multi-component and multi-target prescriptions, which is expected to provide a reference for the development of multi-target drugs. This paper reviews the application of network biology in traditional Chinese medicine in six aspects, in expectation to provide a reference to the modernized study of traditional Chinese medicine. Copyright© by the Chinese Pharmaceutical Association.

  5. Quantifying the connectivity of scale-free and biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Shiner, J.S. E-mail: shiner@alumni.duke.edu; Davison, Matt E-mail: mdavison@uwo.ca

    2004-07-01

    Scale-free and biological networks follow a power law distribution p{sub k}{proportional_to}k{sup -{alpha}} for the probability that a node is connected to k other nodes; the corresponding ranges for {alpha} (biological: 1<{alpha}<2; scale-free: 2<{alpha}{<=}3) yield a diverging variance for the connectivity k and lack of predictability for the average connectivity. Predictability can be achieved with the Renyi, Tsallis and Landsberg-Vedral extended entropies and corresponding 'disorders' for correctly chosen values of the entropy index q. Escort distributions p{sub k}{proportional_to}k{sup -{alpha}}{sup q} with q>3/{alpha} also yield a nondiverging variance and predictability. It is argued that the Tsallis entropies may be the appropriate quantities for the study of scale-free and biological networks.

  6. Topology of transcriptional regulatory networks: testing and improving

    NARCIS (Netherlands)

    Hasdemir, D.; Smits, G.J.; Westerhuis, J.A.; Smilde, A.K.

    2012-01-01

    With the increasing amount and complexity of data generated in biological experiments it is becoming necessary to enhance the performance and applicability of existing statistical data analysis methods. This enhancement is needed for the hidden biological information to be better resolved and better

  7. Fast grid layout algorithm for biological networks with sweep calculation.

    Science.gov (United States)

    Kojima, Kaname; Nagasaki, Masao; Miyano, Satoru

    2008-06-15

    Properly drawn biological networks are of great help in the comprehension of their characteristics. The quality of the layouts for retrieved biological networks is critical for pathway databases. However, since it is unrealistic to manually draw biological networks for every retrieval, automatic drawing algorithms are essential. Grid layout algorithms handle various biological properties such as aligning vertices having the same attributes and complicated positional constraints according to their subcellular localizations; thus, they succeed in providing biologically comprehensible layouts. However, existing grid layout algorithms are not suitable for real-time drawing, which is one of requisites for applications to pathway databases, due to their high-computational cost. In addition, they do not consider edge directions and their resulting layouts lack traceability for biochemical reactions and gene regulations, which are the most important features in biological networks. We devise a new calculation method termed sweep calculation and reduce the time complexity of the current grid layout algorithms through its encoding and decoding processes. We conduct practical experiments by using 95 pathway models of various sizes from TRANSPATH and show that our new grid layout algorithm is much faster than existing grid layout algorithms. For the cost function, we introduce a new component that penalizes undesirable edge directions to avoid the lack of traceability in pathways due to the differences in direction between in-edges and out-edges of each vertex. Java implementations of our layout algorithms are available in Cell Illustrator. masao@ims.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online.

  8. Causal structure of oscillations in gene regulatory networks: Boolean analysis of ordinary differential equation attractors.

    Science.gov (United States)

    Sun, Mengyang; Cheng, Xianrui; Socolar, Joshua E S

    2013-06-01

    A common approach to the modeling of gene regulatory networks is to represent activating or repressing interactions using ordinary differential equations for target gene concentrations that include Hill function dependences on regulator gene concentrations. An alternative formulation represents the same interactions using Boolean logic with time delays associated with each network link. We consider the attractors that emerge from the two types of models in the case of a simple but nontrivial network: a figure-8 network with one positive and one negative feedback loop. We show that the different modeling approaches give rise to the same qualitative set of attractors with the exception of a possible fixed point in the ordinary differential equation model in which concentrations sit at intermediate values. The properties of the attractors are most easily understood from the Boolean perspective, suggesting that time-delay Boolean modeling is a useful tool for understanding the logic of regulatory networks.

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

    Directory of Open Access Journals (Sweden)

    Jose A. Santiago

    2017-05-01

    Full Text Available 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.

  10. S100A4 and its role in metastasis – computational integration of data on biological networks.

    Science.gov (United States)

    Buetti-Dinh, Antoine; Pivkin, Igor V; Friedman, Ran

    2015-08-01

    Characterising signal transduction networks is fundamental to our understanding of biology. However, redundancy and different types of feedback mechanisms make it difficult to understand how variations of the network components contribute to a biological process. In silico modelling of signalling interactions therefore becomes increasingly useful for the development of successful therapeutic approaches. Unfortunately, quantitative information cannot be obtained for all of the proteins or complexes that comprise the network, which limits the usability of computational models. We developed a flexible computational framework for the analysis of biological signalling networks. We demonstrate our approach by studying the mechanism of metastasis promotion by the S100A4 protein, and suggest therapeutic strategies. The advantage of the proposed method is that only limited information (interaction type between species) is required to set up a steady-state network model. This permits a straightforward integration of experimental information where the lack of details are compensated by efficient sampling of the parameter space. We investigated regulatory properties of the S100A4 network and the role of different key components. The results show that S100A4 enhances the activity of matrix metalloproteinases (MMPs), causing higher cell dissociation. Moreover, it leads to an increased stability of the pathological state. Thus, avoiding metastasis in S100A4-expressing tumours requires multiple target inhibition. Moreover, the analysis could explain the previous failure of MMP inhibitors in clinical trials. Finally, our method is applicable to a wide range of biological questions that can be represented as directional networks.

  11. Identifying noncoding risk variants using disease-relevant gene regulatory networks.

    Science.gov (United States)

    Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

    2018-02-16

    Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

  12. Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej; Pers, Tune Hannes; Pinho Soares, Simao Pedro

    2010-01-01

    mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets...... with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment...... factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic...

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

    Science.gov (United States)

    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

  14. The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks

    Directory of Open Access Journals (Sweden)

    Wingender Edgar

    2008-05-01

    Full Text Available Abstract Background Currently, there is a gap between purely theoretical studies of the topology of large bioregulatory networks and the practical traditions and interests of experimentalists. While the theoretical approaches emphasize the global characterization of regulatory systems, the practical approaches focus on the role of distinct molecules and genes in regulation. To bridge the gap between these opposite approaches, one needs to combine 'general' with 'particular' properties and translate abstract topological features of large systems into testable functional characteristics of individual components. Here, we propose a new topological parameter – the pairwise disconnectivity index of a network's element – that is capable of such bridging. Results The pairwise disconnectivity index quantifies how crucial an individual element is for sustaining the communication ability between connected pairs of vertices in a network that is displayed as a directed graph. Such an element might be a vertex (i.e., molecules, genes, an edge (i.e., reactions, interactions, as well as a group of vertices and/or edges. The index can be viewed as a measure of topological redundancy of regulatory paths which connect different parts of a given network and as a measure of sensitivity (robustness of this network to the presence (absence of each individual element. Accordingly, we introduce the notion of a path-degree of a vertex in terms of its corresponding incoming, outgoing and mediated paths, respectively. The pairwise disconnectivity index has been applied to the analysis of several regulatory networks from various organisms. The importance of an individual vertex or edge for the coherence of the network is determined by the particular position of the given element in the whole network. Conclusion Our approach enables to evaluate the effect of removing each element (i.e., vertex, edge, or their combinations from a network. The greatest potential value of

  15. Layered signaling regulatory networks analysis of gene expression involved in malignant tumorigenesis of non-resolving ulcerative colitis via integration of cross-study microarray profiles.

    Science.gov (United States)

    Fan, Shengjun; Pan, Zhenyu; Geng, Qiang; Li, Xin; Wang, Yefan; An, Yu; Xu, Yan; Tie, Lu; Pan, Yan; Li, Xuejun

    2013-01-01

    Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups. In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks. Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one. The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis

  16. Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?

    Directory of Open Access Journals (Sweden)

    Saurabh Vashishtha

    Full Text Available There is a growing appreciation for the network biology that regulates the coordinated expression of molecular and cellular markers however questions persist regarding the identifiability of these networks. Here we explore some of the issues relevant to recovering directed regulatory networks from time course data collected under experimental constraints typical of in vivo studies. NetSim simulations of sparsely connected biological networks were used to evaluate two simple feature selection techniques used in the construction of linear Ordinary Differential Equation (ODE models, namely truncation of terms versus latent vector projection. Performance was compared with ODE-based Time Series Network Identification (TSNI integral, and the information-theoretic Time-Delay ARACNE (TD-ARACNE. Projection-based techniques and TSNI integral outperformed truncation-based selection and TD-ARACNE on aggregate networks with edge densities of 10-30%, i.e. transcription factor, protein-protein cliques and immune signaling networks. All were more robust to noise than truncation-based feature selection. Performance was comparable on the in silico 10-node DREAM 3 network, a 5-node Yeast synthetic network designed for In vivo Reverse-engineering and Modeling Assessment (IRMA and a 9-node human HeLa cell cycle network of similar size and edge density. Performance was more sensitive to the number of time courses than to sample frequency and extrapolated better to larger networks by grouping experiments. In all cases performance declined rapidly in larger networks with lower edge density. Limited recovery and high false positive rates obtained overall bring into question our ability to generate informative time course data rather than the design of any particular reverse engineering algorithm.

  17. Architecture and dynamics of overlapped RNA regulatory networks.

    Science.gov (United States)

    Lapointe, Christopher P; Preston, Melanie A; Wilinski, Daniel; Saunders, Harriet A J; Campbell, Zachary T; Wickens, Marvin

    2017-11-01

    A single protein can bind and regulate many mRNAs. Multiple proteins with similar specificities often bind and control overlapping sets of mRNAs. Yet little is known about the architecture or dynamics of overlapped networks. We focused on three proteins with similar structures and related RNA-binding specificities-Puf3p, Puf4p, and Puf5p of S. cerevisiae Using RNA Tagging, we identified a "super-network" comprised of four subnetworks: Puf3p, Puf4p, and Puf5p subnetworks, and one controlled by both Puf4p and Puf5p. The architecture of individual subnetworks, and thus the super-network, is determined by competition among particular PUF proteins to bind mRNAs, their affinities for binding elements, and the abundances of the proteins. The super-network responds dramatically: The remaining network can either expand or contract. These strikingly opposite outcomes are determined by an interplay between the relative abundance of the RNAs and proteins, and their affinities for one another. The diverse interplay between overlapping RNA-protein networks provides versatile opportunities for regulation and evolution. © 2017 Lapointe et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  18. Fractal gene regulatory networks for robust locomotion control of modular robots

    DEFF Research Database (Denmark)

    Zahadat, Payam; Christensen, David Johan; Schultz, Ulrik Pagh

    2010-01-01

    Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed and the ......Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed...

  19. Analysis of a Plant Transcriptional Regulatory Network Using Transient Expression Systems.

    Science.gov (United States)

    Díaz-Triviño, Sara; Long, Yuchen; Scheres, Ben; Blilou, Ikram

    2017-01-01

    In plant biology, transient expression systems have become valuable approaches used routinely to rapidly study protein expression, subcellular localization, protein-protein interactions, and transcriptional activity prior to in vivo studies. When studying transcriptional regulation, luciferase reporter assays offer a sensitive readout for assaying promoter behavior in response to different regulators or environmental contexts and to confirm and assess the functional relevance of predicted binding sites in target promoters. This chapter aims to provide detailed methods for using luciferase reporter system as a rapid, efficient, and versatile assay to analyze transcriptional regulation of target genes by transcriptional regulators. We describe a series of optimized transient expression systems consisting of Arabidopsis thaliana protoplasts, infiltrated Nicotiana benthamiana leaves, and human HeLa cells to study the transcriptional regulations of two well-characterized transcriptional regulators SCARECROW (SCR) and SHORT-ROOT (SHR) on one of their targets, CYCLIN D6 (CYCD6).Here, we illustrate similarities and differences in outcomes when using different systems. The plant-based systems revealed that the SCR-SHR complex enhances CYCD6 transcription, while analysis in HeLa cells showed that the complex is not sufficient to strongly induce CYCD6 transcription, suggesting that additional, plant-specific regulators are required for full activation. These results highlight the importance of the system and suggest that including heterologous systems, such as HeLa cells, can provide a more comprehensive analysis of a complex gene regulatory network.

  20. Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

    Science.gov (United States)

    García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

    2018-01-01

    Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent

  1. Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

    Science.gov (United States)

    García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

    2018-01-01

    Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent

  2. Synthetic Biology and the U.S. Biotechnology Regulatory System: Challenges and Options

    Energy Technology Data Exchange (ETDEWEB)

    Carter, Sarah R. [J. Craig Venter Inst., Rockville, MD (United States); Rodemeyer, Michael [Univ. of Virginia, Charlottesville, VA (United States); Garfinkel, Michele S. [EMBO, Heidelberg (Germany); Friedman, Robert M. [J. Craig Venter Inst., Rockville, MD (United States)

    2014-05-01

    Synthetic Biology and the U.S. Biotechnology Regulatory System: Challenges and Options Sarah R. Carter, Ph.D., J. Craig Venter Institute; Michael Rodemeyer, J.D., University of Virginia; Michele S. Garfinkel, Ph.D., EMBO; Robert M. Friedman, Ph.D., J. Craig Venter Institute In recent years, a range of genetic engineering techniques referred to as “synthetic biology” has significantly expanded the tool kit available to scientists and engineers, providing them with far greater capabilities to engineer organisms than previous techniques allowed. The field of synthetic biology includes the relatively new ability to synthesize long pieces of DNA from chemicals, as well as improved methods for genetic manipulation and design of genetic pathways to achieve more precise control of biological systems. These advances will help usher in a new generation of genetically engineered microbes, plants, and animals. The JCVI Policy Center team, along with researchers at the University of Virginia and EMBO, examined how well the current U.S. regulatory system for genetically engineered products will handle the near-term introduction of organisms engineered using synthetic biology. In particular, the focus was on those organisms intended to be used or grown directly in the environment, outside of a contained facility. The study concludes that the U.S. regulatory agencies have adequate legal authority to address most, but not all, potential environmental, health and safety concerns posed by these organisms. Such near-term products are likely to represent incremental changes rather than a marked departure from previous genetically engineered organisms. However, the study also identified two key challenges for the regulatory system, which are detailed in the report. First, USDA’s authority over genetically engineered plants depends on the use of an older engineering technique that is no longer necessary for many applications. The shift to synthetic biology and other newer genetic

  3. Integrated Network Analysis and Effective Tools in Plant Systems Biology

    Directory of Open Access Journals (Sweden)

    Atsushi eFukushima

    2014-11-01

    Full Text Available 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.

  4. The impact of network biology in pharmacology and toxicology

    DEFF Research Database (Denmark)

    Panagiotou, Gianni; Taboureau, Olivier

    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...... 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...

  5. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

  6. Information-Theoretic Inference of Large Transcriptional Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Meyer Patrick

    2007-01-01

    Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

  7. Information-Theoretic Inference of Large Transcriptional Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Patrick E. Meyer

    2007-06-01

    Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

  8. Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees

    Directory of Open Access Journals (Sweden)

    Chen Xiaoyu

    2007-12-01

    Full Text Available Abstract Background In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression. Results We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure. Conclusion Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

  9. Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data.

    Directory of Open Access Journals (Sweden)

    Taosheng Xu

    Full Text Available Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.In this paper, we propose a method, weighted similarity network fusion (WSNF, to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs, transcription factors (TFs and messenger RNAs (mRNAs and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA and glioblastoma multiforme (GBM datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some mi

  10. A relative variation-based method to unraveling gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Yali Wang

    Full Text Available Gene regulatory network (GRN reconstruction is essential in understanding the functioning and pathology of a biological system. Extensive models and algorithms have been developed to unravel a GRN. The DREAM project aims to clarify both advantages and disadvantages of these methods from an application viewpoint. An interesting yet surprising observation is that compared with complicated methods like those based on nonlinear differential equations, etc., methods based on a simple statistics, such as the so-called Z-score, usually perform better. A fundamental problem with the Z-score, however, is that direct and indirect regulations can not be easily distinguished. To overcome this drawback, a relative expression level variation (RELV based GRN inference algorithm is suggested in this paper, which consists of three major steps. Firstly, on the basis of wild type and single gene knockout/knockdown experimental data, the magnitude of RELV of a gene is estimated. Secondly, probability for the existence of a direct regulation from a perturbed gene to a measured gene is estimated, which is further utilized to estimate whether a gene can be regulated by other genes. Finally, the normalized RELVs are modified to make genes with an estimated zero in-degree have smaller RELVs in magnitude than the other genes, which is used afterwards in queuing possibilities of the existence of direct regulations among genes and therefore leads to an estimate on the GRN topology. This method can in principle avoid the so-called cascade errors under certain situations. Computational results with the Size 100 sub-challenges of DREAM3 and DREAM4 show that, compared with the Z-score based method, prediction performances can be substantially improved, especially the AUPR specification. Moreover, it can even outperform the best team of both DREAM3 and DREAM4. Furthermore, the high precision of the obtained most reliable predictions shows that the suggested algorithm may be

  11. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    understanding of molecular processes which are fundamental to tumorigenesis. In Article 1, we propose a novel framework for how cancer mutations can be studied by taking into account their effect at the protein network level. In Article 2, we demonstrate how global, quantitative data on phosphorylation dynamics...... can be generated using MS, and how this can be modeled using a computational framework for deciphering kinase-substrate dynamics. This framework is described in depth in Article 3, and covers the design of KinomeXplorer, which allows the prediction of kinases responsible for modulating observed...... phosphorylation dynamics in a given biological sample. In Chapter III, we move into Integrative Network Biology, where, by combining two fundamental technologies (MS & NGS), we can obtain more in-depth insights into the links between cellular phenotype and genotype. Article 4 describes the proof...

  12. Generation of dTALEs and Libraries of Synthetic TALE-Activated Promoters for Engineering of Gene Regulatory Networks in Plants.

    Science.gov (United States)

    Schreiber, Tom; Tissier, Alain

    2017-01-01

    Transcription factors with programmable DNA-binding specificity constitute valuable tools for the design of orthogonal gene regulatory networks for synthetic biology. Transcription activator-like effectors (TALEs), as natural transcription regulators, were used to design, build, and test libraries of synthetic TALE-activated promoters (STAPs) that show a broad range of expression levels in plants. In this chapter, we present protocols for the construction of artificial TALEs and corresponding STAPs.

  13. rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks.

    Science.gov (United States)

    Guo, Liyuan; Wang, Jing

    2018-01-04

    Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element-target gene pairs (E-G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  14. Analysis of complex networks from biology to linguistics

    CERN Document Server

    Dehmer, Matthias

    2009-01-01

    Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.

  15. Strengthening Regulatory Cooperation in Africa: Lessons Learned from the African Regulatory Network (FNRBA)

    International Nuclear Information System (INIS)

    Severa, R.

    2016-01-01

    Africa is a continent endowed in mineral resources. Among others, there are vast deposits of gold and uranium in African countries. The by-products of these minerals are in some cases radioactive and therefore exposures must be monitored. Additionally, Africa uses a lot of radioactive sources in different industries and in the health sector. Regulation of these mining activities and facilities handling these radioactive sources becomes extremely important for the protection of people, property and the environment against harmful effects of ionizing radiation. Due to the vast size of the African continent, with inequitable resources, regional cooperation becomes key to capacity building and knowledge and information sharing. In order to achieve this, African Member States resolved to form a Forum for Nuclear Regulatory Bodies in Africa (FNRBA) in 2009. The paper will present the FNRBA, its activities, achievements and challenges in the quest to bring about effective regulation of nuclear and radiation safety in the continent. (author)

  16. Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

    Directory of Open Access Journals (Sweden)

    Gao Shouguo

    2011-08-01

    Full Text Available Abstract Background Bayesian Network (BN is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable. Results We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information. Conclusion our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.

  17. Epidemiologic studies of occupational pesticide exposure and cancer: regulatory risk assessments and biologic plausibility.

    Science.gov (United States)

    Acquavella, John; Doe, John; Tomenson, John; Chester, Graham; Cowell, John; Bloemen, Louis

    2003-01-01

    Epidemiologic studies frequently show associations between self-reported use of specific pesticides and human cancers. These findings have engendered debate largely on methodologic grounds. However, biologic plausibility is a more fundamental issue that has received only superficial attention. The purpose of this commentary is to review briefly the toxicology and exposure data that are developed as part of the pesticide regulatory process and to discuss the applicability of this data to epidemiologic research. The authors also provide a generic example of how worker pesticide exposures might be estimated and compared to relevant toxicologic dose levels. This example provides guidance for better characterization of exposure and for consideration of biologic plausibility in epidemiologic studies of pesticides.

  18. Regulatory networks in pollen development under cold stress

    Directory of Open Access Journals (Sweden)

    Kamal Dev Sharma

    2016-03-01

    Full Text Available Cold stress modifies anthers’ metabolic pathways to induce pollen sterility. Cold-tolerant plants, unlike the susceptible ones, produce high proportion of viable pollen. Anthers in susceptible plants, when exposed to cold stress, increase abscisic acid (ABA metabolism and reduce ABA catabolism. Increased ABA negatively regulates expression of tapetum cell wall bound invertase and monosaccharide transport genes resulting in distorted carbohydrate pool in anther. Cold-stress also reduces endogenous levels of the bioactive gibberellins (GAs, GA4 and GA7, in susceptible anthers by repression of the GA biosynthesis genes. Here we discuss recent findings on mechanisms of cold susceptibility in anthers which determine pollen sterility. We also discuss differences in regulatory pathways between cold-stressed anthers of susceptible and tolerant plants that decide pollen sterility or viability.

  19. Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities

    DEFF Research Database (Denmark)

    Fang, Xin; Sastry, Anand; Mih, Nathan

    2017-01-01

    Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN-probably the best characterized TRN-several questions remain. Here, we address three questions: (i) How complete is our knowledge of the E. coli TRN; (ii) how well can we predi...

  20. Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

    NARCIS (Netherlands)

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Background: Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori

  1. Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle

    Science.gov (United States)

    Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e., hypothal...

  2. Global Stability of Complex-Valued Genetic Regulatory Networks with Delays on Time Scales

    Directory of Open Access Journals (Sweden)

    Wang Yajing

    2016-01-01

    Full Text Available In this paper, the global exponential stability of complex-valued genetic regulatory networks with delays is investigated. Besides presenting conditions guaranteeing the existence of a unique equilibrium pattern, its global exponential stability is discussed. Some numerical examples for different time scales.

  3. Impact of heuristics in clustering large biological networks.

    Science.gov (United States)

    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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Detecting and evaluating communities in complex human and biological networks

    Science.gov (United States)

    Morrison, Greg; Mahadevan, L.

    2012-02-01

    We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

  5. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    Science.gov (United States)

    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

  6. National Nuclear Regulatory Portal (NNRP) – A Useful Regulatory Knowledge Network

    International Nuclear Information System (INIS)

    Georgieva, Albena

    2014-01-01

    Conclusions: → The main advantage of developing and operation of NNRP is that the most relevant information in the field, obtained from various granted data sources, will be internationally accessible from one place; → NNRP can be used as a platform for more effective international cooperation between MS or for national information and cooperation activities and information exchange; → NNRP is an inclusive concept that brings together, links and complements all existing networks and initiatives

  7. Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

    Science.gov (United States)

    Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

    2013-03-28

    Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. Copyright © 2013 Elsevier Inc. All rights reserved.

  8. Network regulation and regulatory institutional reform: Revisiting the case of Australia

    International Nuclear Information System (INIS)

    Nepal, Rabindra; Menezes, Flavio; Jamasb, Tooraj

    2014-01-01

    It is well-understood that the success of liberalizing the electricity supply industry depends crucially on the quality and design of the regulatory and institutional framework. This paper analyses the regulatory arrangements that underpin the work of the Australian Energy Regulator (AER). These arrangements are contrasted with the regulatory structure of electricity provision in Norway. A key difference between the reform processes in the two countries relates to the lack of privatization in Norway and the co-existence of private and publicly owned generators and distributors in Australia. This comparative analysis allows us to make several recommendations to improve regulatory arrangements in Australia. These include greater independence for the AER, better coordination among regulatory institutions, greater use of benchmarking analysis, greater customer involvement, and improving market transparency and privatization of government-owned corporations. However, the success of privatization will hinge upon the effectiveness of the regulatory environment. - Highlights: • Rising electricity prices and network costs is of great concern in Australia. • Flaws in the existing regulatory environment and economic efficiency exist. • The AER should be provided with adequate resources (financial and staff experts) and discretion. • Robust benchmarking techniques should be adopted in the incentive regulation framework for cost efficiency. • Privatization of the state-owned assets also remains an option

  9. Plasticity of gene-regulatory networks controlling sex determination: of masters, slaves, usual suspects, newcomers, and usurpators.

    Science.gov (United States)

    Herpin, Amaury; Schartl, Manfred

    2015-10-01

    Sexual dimorphism is one of the most pervasive and diverse features of animal morphology, physiology, and behavior. Despite the generality of the phenomenon itself, the mechanisms controlling how sex is determined differ considerably among various organismic groups, have evolved repeatedly and independently, and the underlying molecular pathways can change quickly during evolution. Even within closely related groups of organisms for which the development of gonads on the morphological, histological, and cell biological level is undistinguishable, the molecular control and the regulation of the factors involved in sex determination and gonad differentiation can be substantially different. The biological meaning of the high molecular plasticity of an otherwise common developmental program is unknown. While comparative studies suggest that the downstream effectors of sex-determining pathways tend to be more stable than the triggering mechanisms at the top, it is still unclear how conserved the downstream networks are and how all components work together. After many years of stasis, when the molecular basis of sex determination was amenable only in the few classical model organisms (fly, worm, mouse), recently, sex-determining genes from several animal species have been identified and new studies have elucidated some novel regulatory interactions and biological functions of the downstream network, particularly in vertebrates. These data have considerably changed our classical perception of a simple linear developmental cascade that makes the decision for the embryo to develop as male or female, and how it evolves. © 2015 The Authors.

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

    Science.gov (United States)

    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.

  11. Analysis of metastasis associated signal regulatory network in colorectal cancer.

    Science.gov (United States)

    Qi, Lu; Ding, Yanqing

    2018-06-18

    Metastasis is a key factor that affects the survival and prognosis of colorectal cancer patients. To elucidate molecular mechanism associated with the metastasis of colorectal cancer, genes related to the metastasis time of colorectal cancer were screened. Then, a network was constructed with this genes. Data was obtained from colorectal cancer expression profile. Molecular mechanism elucidated the time of tumor metastasis and the expression of genes related to colorectal cancer. We found that metastasis-promoting and metastasis-inhibiting networks included protein hubs of high connectivity. These protein hubs were components of organelles. Some ribosomal proteins promoted the metastasis of colorectal cancer. In some components of organelles, such as proteasomes, mitochondrial ribosome, ATP synthase, and splicing factors, the metastasis of colorectal cancer was inhibited by some sections of these organelles. After performing survival analysis of proteins in organelles, joint survival curve of proteins was constructed in ribosomal network. This joint survival curve showed metastasis was promoted in patients with colorectal cancer (P = 0.0022939). Joint survival curve of proteins was plotted against proteasomes (P = 7 e-07), mitochondrial ribosome (P = 0.0001157), ATP synthase (P = 0.0001936), and splicing factors (P = 1.35e-05). These curves indicate that metastasis of colorectal cancer can be inhibited. After analyzing proteins that bind with organelle components, we also found that some proteins were associated with the time of colorectal cancer metastasis. Hence, different cellular components play different roles in the metastasis of colorectal cancer. Copyright © 2018 Elsevier Inc. All rights reserved.

  12. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data

    NARCIS (Netherlands)

    Kümmel, Anne; Panke, Sven; Heinemann, Matthias

    2006-01-01

    As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic

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

    Science.gov (United States)

    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.

  14. Phenotypic stability and plasticity in GMP-derived cells as determined by their underlying regulatory network.

    Science.gov (United States)

    Ramírez, Carlos; Mendoza, Luis

    2018-04-01

    Blood cell formation has been recognized as a suitable system to study celular differentiation mainly because of its experimental accessibility, and because it shows characteristics such as hierarchical and gradual bifurcated patterns of commitment, which are present in several developmental processes. Although hematopoiesis has been extensively studied and there is a wealth of molecular and cellular data about it, it is not clear how the underlying molecular regulatory networks define or restrict cellular differentiation processes. Here, we infer the molecular regulatory network that controls the differentiation of a blood cell subpopulation derived from the granulocyte-monocyte precursor (GMP), comprising monocytes, neutrophils, eosinophils, basophils and mast cells. We integrate published qualitative experimental data into a model to describe temporal expression patterns observed in GMP-derived cells. The model is implemented as a Boolean network, and its dynamical behavior is studied. Steady states of the network can be clearly identified with the expression profiles of monocytes, mast cells, neutrophils, basophils, and eosinophils, under wild-type and mutant backgrounds. All scripts are publicly available at https://github.com/caramirezal/RegulatoryNetworkGMPModel. lmendoza@biomedicas.unam.mx. Supplementary data are available at Bioinformatics online.

  15. Fixed Points in Discrete Models for Regulatory Genetic Networks

    Directory of Open Access Journals (Sweden)

    Orozco Edusmildo

    2007-01-01

    Full Text Available It is desirable to have efficient mathematical methods to extract information about regulatory iterations between genes from repeated measurements of gene transcript concentrations. One piece of information is of interest when the dynamics reaches a steady state. In this paper we develop tools that enable the detection of steady states that are modeled by fixed points in discrete finite dynamical systems. We discuss two algebraic models, a univariate model and a multivariate model. We show that these two models are equivalent and that one can be converted to the other by means of a discrete Fourier transform. We give a new, more general definition of a linear finite dynamical system and we give a necessary and sufficient condition for such a system to be a fixed point system, that is, all cycles are of length one. We show how this result for generalized linear systems can be used to determine when certain nonlinear systems (monomial dynamical systems over finite fields are fixed point systems. We also show how it is possible to determine in polynomial time when an ordinary linear system (defined over a finite field is a fixed point system. We conclude with a necessary condition for a univariate finite dynamical system to be a fixed point system.

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

    Science.gov (United States)

    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

  17. Generation of intervention strategy for a genetic regulatory network represented by a family of Markov Chains.

    Science.gov (United States)

    Berlow, Noah; Pal, Ranadip

    2011-01-01

    Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a genetic regulatory network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.

  18. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    Science.gov (United States)

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

  19. Use artificial neural network to align biological ontologies.

    Science.gov (United States)

    Huang, Jingshan; Dang, Jiangbo; Huhns, Michael N; Zheng, W Jim

    2008-09-16

    Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics. In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others. The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.

  20. Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network

    Directory of Open Access Journals (Sweden)

    Delyon Bernard

    2010-11-01

    Full Text Available Abstract Background A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. Results We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation. Conclusions The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific.

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

    Directory of Open Access Journals (Sweden)

    Martin Florian

    2012-05-01

    Full Text Available Abstract 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

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

    Science.gov (United States)

    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

  3. International STakeholder NETwork (ISTNET): creating a developmental neurotoxicity (DNT) testing road map for regulatory purposes.

    Science.gov (United States)

    Bal-Price, Anna; Crofton, Kevin M; Leist, Marcel; Allen, Sandra; Arand, Michael; Buetler, Timo; Delrue, Nathalie; FitzGerald, Rex E; Hartung, Thomas; Heinonen, Tuula; Hogberg, Helena; Bennekou, Susanne Hougaard; Lichtensteiger, Walter; Oggier, Daniela; Paparella, Martin; Axelstad, Marta; Piersma, Aldert; Rached, Eva; Schilter, Benoît; Schmuck, Gabriele; Stoppini, Luc; Tongiorgi, Enrico; Tiramani, Manuela; Monnet-Tschudi, Florianne; Wilks, Martin F; Ylikomi, Timo; Fritsche, Ellen

    2015-02-01

    A major problem in developmental neurotoxicity (DNT) risk assessment is the lack of toxicological hazard information for most compounds. Therefore, new approaches are being considered to provide adequate experimental data that allow regulatory decisions. This process requires a matching of regulatory needs on the one hand and the opportunities provided by new test systems and methods on the other hand. Alignment of academically and industrially driven assay development with regulatory needs in the field of DNT is a core mission of the International STakeholder NETwork (ISTNET) in DNT testing. The first meeting of ISTNET was held in Zurich on 23-24 January 2014 in order to explore the concept of adverse outcome pathway (AOP) to practical DNT testing. AOPs were considered promising tools to promote test systems development according to regulatory needs. Moreover, the AOP concept was identified as an important guiding principle to assemble predictive integrated testing strategies (ITSs) for DNT. The recommendations on a road map towards AOP-based DNT testing is considered a stepwise approach, operating initially with incomplete AOPs for compound grouping, and focussing on key events of neurodevelopment. Next steps to be considered in follow-up activities are the use of case studies to further apply the AOP concept in regulatory DNT testing, making use of AOP intersections (common key events) for economic development of screening assays, and addressing the transition from qualitative descriptions to quantitative network modelling.

  4. SMAD regulatory networks construct a balanced immune system.

    Science.gov (United States)

    Malhotra, Nidhi; Kang, Joonsoo

    2013-05-01

    A balanced immune response requires combating infectious assaults while striving to maintain quiescence towards the self. One of the central players in this process is the pleiotropic cytokine transforming growth factor-β (TGF-β), whose deficiency results in spontaneous systemic autoimmunity in mice. The dominant function of TGF-β is to regulate the peripheral immune homeostasis, particularly in the microbe-rich and antigen-rich environment of the gut. To maintain intestinal integrity, the epithelial cells, myeloid cells and lymphocytes that inhabit the gut secrete TGF-β, which acts in both paracrine and autocrine fashions to activate its signal transducers, the SMAD transcription factors. The SMAD pathway regulates the production of IgA by B cells, maintains the protective mucosal barrier and promotes the balanced differentiation of CD4(+) T cells into inflammatory T helper type 17 cells and suppressive FOXP3(+) T regulatory cells. While encounters with pathogenic microbes activate SMAD proteins to evoke a protective inflammatory immune response, SMAD activation and synergism with immunoregulatory factors such as the vitamin A metabolite retinoic acid enforce immunosuppression toward commensal microbes and innocuous food antigens. Such complementary context-dependent functions of TGF-β are achieved by the co-operation of SMAD proteins with distinct dominant transcription activators and accessory chromatin modifiers. This review highlights recent advances in unravelling the molecular basis for the multi-faceted functions of TGF-β in the gut that are dictacted by fluid orchestrations of SMADs and their myriad partners. © 2013 Blackwell Publishing Ltd.

  5. The transcriptional and gene regulatory network of Lactococcus lactis MG1363 during growth in milk.

    Directory of Open Access Journals (Sweden)

    Anne de Jong

    Full Text Available In the present study we examine the changes in the expression of genes of Lactococcus lactis subspecies cremoris MG1363 during growth in milk. To reveal which specific classes of genes (pathways, operons, regulons, COGs are important, we performed a transcriptome time series experiment. Global analysis of gene expression over time showed that L. lactis adapted quickly to the environmental changes. Using upstream sequences of genes with correlated gene expression profiles, we uncovered a substantial number of putative DNA binding motifs that may be relevant for L. lactis fermentative growth in milk. All available novel and literature-derived data were integrated into network reconstruction building blocks, which were used to reconstruct and visualize the L. lactis gene regulatory network. This network enables easy mining in the chrono-transcriptomics data. A freely available website at http://milkts.molgenrug.nl gives full access to all transcriptome data, to the reconstructed network and to the individual network building blocks.

  6. The impact of gene expression variation on the robustness and evolvability of a developmental gene regulatory network.

    Directory of Open Access Journals (Sweden)

    David A Garfield

    2013-10-01

    Full Text Available Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear, allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.

  7. Color encoding in biologically-inspired convolutional neural networks.

    Science.gov (United States)

    Rafegas, Ivet; Vanrell, Maria

    2018-05-11

    Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. Learning gene regulatory networks from gene expression data using weighted consensus

    KAUST Repository

    Fujii, Chisato; Kuwahara, Hiroyuki; Yu, Ge; Guo, Lili; Gao, Xin

    2016-01-01

    An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

  9. Exploring the bZIP transcription factor regulatory network in Neurospora crassa.

    Science.gov (United States)

    Tian, Chaoguang; Li, Jingyi; Glass, N Louise

    2011-03-01

    Transcription factors (TFs) are key nodes of regulatory networks in eukaryotic organisms, including filamentous fungi such as Neurospora crassa. The 178 predicted DNA-binding TFs in N. crassa are distributed primarily among six gene families, which represent an ancient expansion in filamentous ascomycete genomes; 98 TF genes show detectable expression levels during vegetative growth of N. crassa, including 35 that show a significant difference in expression level between hyphae at the periphery versus hyphae in the interior of a colony. Regulatory networks within a species genome include paralogous TFs and their respective target genes (TF regulon). To investigate TF network evolution in N. crassa, we focused on the basic leucine zipper (bZIP) TF family, which contains nine members. We performed baseline transcriptional profiling during vegetative growth of the wild-type and seven isogenic, viable bZIP deletion mutants. We further characterized the regulatory network of one member of the bZIP family, NCU03905. NCU03905 encodes an Ap1-like protein (NcAp-1), which is involved in resistance to multiple stress responses, including oxidative and heavy metal stress. Relocalization of NcAp-1 from the cytoplasm to the nucleus was associated with exposure to stress. A comparison of the NcAp-1 regulon with Ap1-like regulons in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Aspergillus fumigatus showed both conservation and divergence. These data indicate how N. crassa responds to stress and provide information on pathway evolution.

  10. Learning gene regulatory networks from gene expression data using weighted consensus

    KAUST Repository

    Fujii, Chisato

    2016-08-25

    An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

  11. Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes.

    Directory of Open Access Journals (Sweden)

    Fei Xiao

    Full Text Available Combining path consistency (PC algorithms with conditional mutual information (CMI are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference, to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.

  12. Qualitative reasoning for biological network inference from systematic perturbation experiments.

    Science.gov (United States)

    Badaloni, Silvana; Di Camillo, Barbara; Sambo, Francesco

    2012-01-01

    The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.

  13. Molecular codes in biological and chemical reaction networks.

    Directory of Open Access Journals (Sweden)

    Dennis Görlich

    Full Text Available 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.

  14. Using synthetic biology to distinguish and overcome regulatory and functional barriers related to nitrogen fixation.

    Directory of Open Access Journals (Sweden)

    Xia Wang

    Full Text Available Biological nitrogen fixation is a complex process requiring multiple genes working in concert. To date, the Klebsiella pneumoniae nif gene cluster, divided into seven operons, is one of the most studied systems. Its nitrogen fixation capacity is subject to complex cascade regulation and physiological limitations. In this report, the entire K. pneumoniae nif gene cluster was reassembled as operon-based BioBrick parts in Escherichia coli. It provided ~100% activity of native K. pneumoniae system. Based on the expression levels of these BioBrick parts, a T7 RNA polymerase-LacI expression system was used to replace the σ(54-dependent promoters located upstream of nif operons. Expression patterns of nif operons were critical for the maximum activity of the recombinant system. By mimicking these expression levels with variable-strength T7-dependent promoters, ~42% of the nitrogenase activity of the σ(54-dependent nif system was achieved in E. coli. When the newly constructed T7-dependent nif system was challenged with different genetic and physiological conditions, it bypassed the original complex regulatory circuits, with minor physiological limitations. Therefore, we have successfully replaced the nif regulatory elements with a simple expression system that may provide the first step for further research of introducing nif genes into eukaryotic organelles, which has considerable potentials in agro-biotechnology.

  15. Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis.

    Science.gov (United States)

    Lu, Tao

    2016-01-01

    The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.

  16. Jimena: efficient computing and system state identification for genetic regulatory networks.

    Science.gov (United States)

    Karl, Stefan; Dandekar, Thomas

    2013-10-11

    Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.

  17. The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

    DEFF Research Database (Denmark)

    Liu, Guodong; Marras, Antonio; Nielsen, Jens

    2014-01-01

    regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network (TRN) for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model......Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional....... While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate...

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

    Science.gov (United States)

    De Domenico, Manlio

    2018-03-01

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

  19. Interchangeability of biosimilar and biological reference product: updated regulatory positions and pre- and post-marketing evidence.

    Science.gov (United States)

    Trifirò, Gianluca; Marcianò, Ilaria; Ingrasciotta, Ylenia

    2018-03-01

    Since 2006, biosimilars have been available in several countries worldwide, thus allowing for potential savings in pharmaceutical expenditure. However, there have been numerous debates about the interchangeability of biosimilars and reference products based on concerns of immunogenicity by switching between biological products, which may cause lack of effect and toxicity. Areas covered: The authors provide the reader with an overview of the different positions of regulatory authorities on the interchangeability and automatic substitution of biosimilars and reference products. Presently, the FDA allows automatic substitution without prescriber intervention if the biosimilar is interchangeable with reference products, while the European Medicines Agency delegate to each single EU member state. Expert opinion: Different approaches in defining interchangeability and automatic substitution call for harmonization to increase confidence of healthcare professionals and patients about the clinical impact of switching. Networks of electronic healthcare records and administrative databases, potentially linkable to clinical charts and registries may rapidly assess frequency and benefit-risk profile of different switching patterns in routine care at different levels, thus integrating and strengthening pre-marketing evidence.

  20. Construction and analysis of the transcription factor-microRNA co-regulatory network response to Mycobacterium tuberculosis: a view from the blood.

    Science.gov (United States)

    Lin, Yan; Duan, Zipeng; Xu, Feng; Zhang, Jiayuan; Shulgina, Marina V; Li, Fan

    2017-01-01

    Mycobacterium tuberculosis ( Mtb ) infection has been regional outbreak, recently. The traditional focus on the patterns of "reductionism" which was associated with single molecular changes has been unable to meet the demand of early diagnosis and clinical application when current tuberculosis infection happened. In this study, we employed a systems biology approach to collect large microarray data sets including mRNAs and microRNAs (miRNAs) to identify the differentially expressed mRNAs and miRNAs in the whole blood of TB patients. The aim was to identify key genes associated with the immune response in the pathogenic process of tuberculosis by analyzing the co-regulatory network that was consisted of transcription factors and miRNAs as well as their target genes. The network along with their co-regulatory genes was analyzed utilizing Transcriptional Regulatory Element Database (TRED) and Database for Annotation, Visualization and Integrated Discovery (DAVID). We got 21 (19 up-regulated and 2 down-regulated) differentially expressed genes that were co-regulated by transcription factors and miRNAs. KEGG pathway enrichment analysis showed that the 21 differentially expressed genes were predominantly involved in Tuberculosis signaling pathway, which may play a major role in tuberculosis biological process. Quantitative real-time PCR was performed to verify the over expression of co-regulatory genes ( FCGR1A and CEBPB ). The genetic expression was correlated with clinicopathological characteristics in TB patients and inferences drawn. Our results suggest the TF-miRNA gene co-regulatory network may help us further understand the molecular mechanism of immune response to tuberculosis and provide us a new angle of future biomarker and therapeutic targets.

  1. NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

    Science.gov (United States)

    Bellot, Pau; Olsen, Catharina; Salembier, Philippe; Oliveras-Vergés, Albert; Meyer, Patrick E

    2015-09-29

    In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

  2. Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

    OpenAIRE

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Abstract Background Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori assumptions about the interactions, which all simulate the observed patterns. It is important to analyze the properties of the circuits. Findings We have analyzed the simulated gene expression ...

  3. GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Kam D. Dahlquist

    2016-09-01

    Full Text Available GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs. A gene regulatory network (GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF text file, or a GraphML XML file. When a user uploads an input file specifying an unweighted network, GRNsight automatically lays out the graph using black lines and pointed arrowheads. For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (positive for activation or negative for repression and magnitude of the weight parameter. GRNsight is written in JavaScript, with diagrams facilitated by D3.js, a data visualization library. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized to support the specific needs of GRNs. Nodes are rectangular and support gene labels of up to 12 characters. The edges are arcs, which become straight lines when the nodes are close together. Self-regulatory edges are indicated by a loop. When a user mouses over an edge, the numerical value of the weight parameter is displayed. Visualizations can

  4. Regulatory Improvements for Effective Integration of Distributed Generation into Electricity Distribution Networks

    International Nuclear Information System (INIS)

    Scheepers, M.J.J.; Jansen, J.C.; De Joode, J.; Bauknecht, D.; Gomez, T.; Pudjianto, D.; Strbac, G.; Ropenus, S.

    2007-11-01

    The growth of distributed electricity supply of renewable energy sources (RES-E) and combined heat and power (CHP) - so called distributed generation (DG) - can cause technical problems for electricity distribution networks. These integration problems can be overcome by reinforcing the network. Many European Member States apply network regulation that does not account for the impact of DG growth on the network costs. Passing on network integration costs to the DG-operator who is responsible for these extra costs may result in discrimination between different DG plants and between DG and large power generation. Therefore, in many regulatory systems distribution system operators (DSOs) are not being compensated for the DG integration costs. The DG-GRID project analysed technical and economical barriers for integration of distributed generation into electricity distribution networks. The project looked into the impact of a high DG deployment on the electricity distribution system costs and the impact on the financial position of the DSO. Several ways for improving network regulation in order to compensate DSOs for the increasing DG penetration were identified and tested. The DG-GRID project looked also into stimulating network innovations through economic regulation. The project was co-financed by the European Commission and carried out by nine European universities and research institutes. This report summarises the project results and is based on a number of DG-GRID reports that describe the conducted analyses and their results

  5. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

    DEFF Research Database (Denmark)

    Soberano de Oliveira, Ana Paula; Patil, Kiran Raosaheb; Nielsen, Jens

    2008-01-01

    is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology......Background: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem...... with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription...

  6. Quantum Processes and Dynamic Networks in Physical and Biological Systems.

    Science.gov (United States)

    Dudziak, Martin Joseph

    Quantum theory since its earliest formulations in the Copenhagen Interpretation has been difficult to integrate with general relativity and with classical Newtonian physics. There has been traditionally a regard for quantum phenomena as being a limiting case for a natural order that is fundamentally classical except for microscopic extrema where quantum mechanics must be applied, more as a mathematical reconciliation rather than as a description and explanation. Macroscopic sciences including the study of biological neural networks, cellular energy transports and the broad field of non-linear and chaotic systems point to a quantum dimension extending across all scales of measurement and encompassing all of Nature as a fundamentally quantum universe. Theory and observation lead to a number of hypotheses all of which point to dynamic, evolving networks of fundamental or elementary processes as the underlying logico-physical structure (manifestation) in Nature and a strongly quantized dimension to macroscalar processes such as are found in biological, ecological and social systems. The fundamental thesis advanced and presented herein is that quantum phenomena may be the direct consequence of a universe built not from objects and substance but from interacting, interdependent processes collectively operating as sets and networks, giving rise to systems that on microcosmic or macroscopic scales function wholistically and organically, exhibiting non-locality and other non -classical phenomena. The argument is made that such effects as non-locality are not aberrations or departures from the norm but ordinary consequences of the process-network dynamics of Nature. Quantum processes are taken to be the fundamental action-events within Nature; rather than being the exception quantum theory is the rule. The argument is also presented that the study of quantum physics could benefit from the study of selective higher-scale complex systems, such as neural processes in the brain

  7. DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks

    Science.gov (United States)

    Gerstein, Mark

    2016-01-01

    Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem’s gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally–e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the “state” and “control” in the model refer to its own (internal) and another subsystem’s (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model’s parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with

  8. DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Daifeng Wang

    2016-10-01

    Full Text Available Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs, cellular growth factors and microRNAs. A subsystem's gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally-e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the "state" and "control" in the model refer to its own (internal and another subsystem's (external gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model's parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs, seeing the degree to which these can be accounted for by orthologous (internal versus species-specific (external TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with

  9. Notes on a PDE system for biological network formation

    KAUST Repository

    Haskovec, Jan

    2016-01-22

    We present new analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transport networks. The model describes the pressure field using a Darcy’s type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. The analytical part extends the results of Haskovec et al. (2015) regarding the existence of weak and mild solutions to the whole range of meaningful relaxation exponents. Moreover, we prove finite time extinction or break-down of solutions in the spatially one-dimensional setting for certain ranges of the relaxation exponent. We also construct stationary solutions for the case of vanishing diffusion and critical value of the relaxation exponent, using a variational formulation and a penalty method. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on mixed finite elements and study the qualitative properties of network structures for various parameter values. Furthermore, we indicate numerically that some analytical results proved for the spatially one-dimensional setting are likely to be valid also in several space dimensions.

  10. A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection.

    Science.gov (United States)

    Carey, Michelle; Ramírez, Juan Camilo; Wu, Shuang; Wu, Hulin

    2018-07-01

    A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

  11. Transcriptional regulatory network triggered by oxidative signals configures the early response mechanisms of japonica rice to chilling stress

    KAUST Repository

    Yun, Kil-Young; Park, Myoung Ryoul; Mohanty, Bijayalaxmi; Herath, Venura; Xu, Fuyu; Mauleon, Ramil; Wijaya, Edward; Bajic, Vladimir B.; Bruskiewich, Richard; de los Reyes, Benildo G

    2010-01-01

    -plant level analyses established a holistic view of chilling stress response mechanism of japonica rice. Early response regulatory network triggered by oxidative signals is critical for prolonged survival under sub-optimal temperature. Integration of stress

  12. A Process Perspective on Regulation: A Grounded Theory Study into Regulatory Practice in Newly Liberalized Network-Based Markets

    NARCIS (Netherlands)

    Ubacht, J.

    The transition from a former monopolistic towards a more competitive market in
    newly liberalized network-based markets raises regulatory issues. National Regulatory Authorities (NRA) face the challenge to deal with these issues in order to guide the transition process. Although this transition

  13. OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks.

    Science.gov (United States)

    Lim, Néhémy; Senbabaoglu, Yasin; Michailidis, George; d'Alché-Buc, Florence

    2013-06-01

    Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. A number of approaches using either perturbation (knock-out) or wild-type time-series data have appeared in the literature addressing this problem, with the latter using linear temporal models. Nonlinear dynamical models are particularly appropriate for this inference task, given the generation mechanism of the time-series data. In this study, we introduce a novel nonlinear autoregressive model based on operator-valued kernels that simultaneously learns the model parameters, as well as the network structure. A flexible boosting algorithm (OKVAR-Boost) that shares features from L2-boosting and randomization-based algorithms is developed to perform the tasks of parameter learning and network inference for the proposed model. Specifically, at each boosting iteration, a regularized Operator-valued Kernel-based Vector AutoRegressive model (OKVAR) is trained on a random subnetwork. The final model consists of an ensemble of such models. The empirical estimation of the ensemble model's Jacobian matrix provides an estimation of the network structure. The performance of the proposed algorithm is first evaluated on a number of benchmark datasets from the DREAM3 challenge and then on real datasets related to the In vivo Reverse-Engineering and Modeling Assessment (IRMA) and T-cell networks. The high-quality results obtained strongly indicate that it outperforms existing approaches. The OKVAR-Boost Matlab code is available as the archive: http://amis-group.fr/sourcecode-okvar-boost/OKVARBoost-v1.0.zip. Supplementary data are available at Bioinformatics online.

  14. A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology.

    Science.gov (United States)

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-07-12

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.

  15. Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Mathilde de Taffin

    Full Text Available Collier, the single Drosophila COE (Collier/EBF/Olf-1 transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles.

  16. Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks

    Science.gov (United States)

    Dubois, Laurence; Bataillé, Laetitia; Painset, Anaïs; Le Gras, Stéphanie; Jost, Bernard; Crozatier, Michèle; Vincent, Alain

    2015-01-01

    Collier, the single Drosophila COE (Collier/EBF/Olf-1) transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col) targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya) is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles. PMID:26204530

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

    Science.gov (United States)

    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/.

  18. A method to identify important dynamical states in Boolean models of regulatory networks: application to regulation of stomata closure by ABA in A. thaliana.

    Science.gov (United States)

    Bugs, Cristhian A; Librelotto, Giovani R; Mombach, José C M

    2011-12-22

    We introduce a method to analyze the states of regulatory Boolean models that identifies important network states and their biological influence on the global network dynamics. It consists in (1) finding the states of the network that are most frequently visited and (2) the identification of variable and frozen nodes of the network. The method, along with a simulation that includes random features, is applied to the study of stomata closure by abscisic acid (ABA) in A. thaliana proposed by Albert and coworkers. We find that for the case of study, that the dynamics of wild and mutant networks have just two states that are highly visited in their space of states and about a third of all nodes of the wild network are variable while the rest remain frozen in True or False states. This high number of frozen elements explains the low cardinality of the space of states of the wild network. Similar results are observed in the mutant networks. The application of the method allowed us to explain how wild and mutants behave dynamically in the SS and determined an essential feature of the activation of the closure node (representing stomata closure), i.e. its synchronization with the AnionEm node (representing anion efflux at the plasma membrane). The dynamics of this synchronization explains the efficiency reached by the wild and each of the mutant networks. For the biological problem analyzed, our method allows determining how wild and mutant networks differ 'phenotypically'. It shows that the different efficiencies of stomata closure reached among the simulated wild and mutant networks follow from a dynamical behavior of two nodes that are always synchronized. Additionally, we predict that the involvement of the anion efflux at the plasma membrane is crucial for the plant response to ABA. The algorithm used in the simulations is available upon request.

  19. Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

    Science.gov (United States)

    Gong, Wuming; Koyano-Nakagawa, Naoko; Li, Tongbin; Garry, Daniel J

    2015-03-07

    Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion. We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes. The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p network inference model identified a region with an elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP

  20. The Complexity of Posttranscriptional Small RNA Regulatory Networks Revealed by In Silico Analysis of Gossypium arboreum L. Leaf, Flower and Boll Small Regulatory RNAs.

    Directory of Open Access Journals (Sweden)

    Hongtao Hu

    of a second round of both cis- and trans-cleavage of additional siRNAs, leading to the formation of complex sRNA regulatory networks mediating posttranscriptional gene silencing. Results from this study extended our knowledge on G. arboreum sRNAs and their biological importance, which would facilitate future studies on regulatory mechanism of tissue development in cotton and other plant species.

  1. The Complexity of Posttranscriptional Small RNA Regulatory Networks Revealed by In Silico Analysis of Gossypium arboreum L. Leaf, Flower and Boll Small Regulatory RNAs.

    Science.gov (United States)

    Hu, Hongtao; Rashotte, Aaron M; Singh, Narendra K; Weaver, David B; Goertzen, Leslie R; Singh, Shree R; Locy, Robert D

    2015-01-01

    round of both cis- and trans-cleavage of additional siRNAs, leading to the formation of complex sRNA regulatory networks mediating posttranscriptional gene silencing. Results from this study extended our knowledge on G. arboreum sRNAs and their biological importance, which would facilitate future studies on regulatory mechanism of tissue development in cotton and other plant species.

  2. The origins and evolutionary history of human non-coding RNA regulatory networks.

    Science.gov (United States)

    Sherafatian, Masih; Mowla, Seyed Javad

    2017-04-01

    The evolutionary history and origin of the regulatory function of animal non-coding RNAs are not well understood. Lack of conservation of long non-coding RNAs and small sizes of microRNAs has been major obstacles in their phylogenetic analysis. In this study, we tried to shed more light on the evolution of ncRNA regulatory networks by changing our phylogenetic strategy to focus on the evolutionary pattern of their protein coding targets. We used available target databases of miRNAs and lncRNAs to find their protein coding targets in human. We were able to recognize evolutionary hallmarks of ncRNA targets by phylostratigraphic analysis. We found the conventional 3'-UTR and lesser known 5'-UTR targets of miRNAs to be enriched at three consecutive phylostrata. Firstly, in eukaryata phylostratum corresponding to the emergence of miRNAs, our study revealed that miRNA targets function primarily in cell cycle processes. Moreover, the same overrepresentation of the targets observed in the next two consecutive phylostrata, opisthokonta and eumetazoa, corresponded to the expansion periods of miRNAs in animals evolution. Coding sequence targets of miRNAs showed a delayed rise at opisthokonta phylostratum, compared to the 3' and 5' UTR targets of miRNAs. LncRNA regulatory network was the latest to evolve at eumetazoa.

  3. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  4. Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.

    Science.gov (United States)

    Xenitidis, P; Seimenis, I; Kakolyris, S; Adamopoulos, A

    2017-08-07

    High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time domain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. Copyright © 2017 Elsevier

  5. An Organismal Model for Gene Regulatory Networks in the Gut-Associated Immune Response

    Directory of Open Access Journals (Sweden)

    Katherine M. Buckley

    2017-10-01

    Full Text Available The gut epithelium is an ancient site of complex communication between the animal immune system and the microbial world. While elements of self-non-self receptors and effector mechanisms differ greatly among animal phyla, some aspects of recognition, regulation, and response are broadly conserved. A gene regulatory network (GRN approach provides a means to investigate the nature of this conservation and divergence even as more peripheral functional details remain incompletely understood. The sea urchin embryo is an unparalleled experimental model for detangling the GRNs that govern embryonic development. By applying this theoretical framework to the free swimming, feeding larval stage of the purple sea urchin, it is possible to delineate the conserved regulatory circuitry that regulates the gut-associated immune response. This model provides a morphologically simple system in which to efficiently unravel regulatory connections that are phylogenetically relevant to immunity in vertebrates. Here, we review the organism-wide cellular and transcriptional immune response of the sea urchin larva. A large set of transcription factors and signal systems, including epithelial expression of interleukin 17 (IL17, are important mediators in the activation of the early gut-associated response. Many of these have homologs that are active in vertebrate immunity, while others are ancient in animals but absent in vertebrates or specific to echinoderms. This larval model provides a means to experimentally characterize immune function encoded in the sea urchin genome and the regulatory interconnections that control immune response and resolution across the tissues of the organism.

  6. Biologically based neural network for mobile robot navigation

    Science.gov (United States)

    Torres Muniz, Raul E.

    1999-01-01

    The new tendency in mobile robots is to crete non-Cartesian system based on reactions to their environment. This emerging technology is known as Evolutionary Robotics, which is combined with the Biorobotic field. This new approach brings cost-effective solutions, flexibility, robustness, and dynamism into the design of mobile robots. It also provides fast reactions to the sensory inputs, and new interpretation of the environment or surroundings of the mobile robot. The Subsumption Architecture (SA) and the action selection dynamics developed by Brooks and Maes, respectively, have successfully obtained autonomous mobile robots initiating this new trend of the Evolutionary Robotics. Their design keeps the mobile robot control simple. This work present a biologically inspired modification of these schemes. The hippocampal-CA3-based neural network developed by Williams Levy is used to implement the SA, while the action selection dynamics emerge from iterations of the levels of competence implemented with the HCA3. This replacement by the HCA3 results in a closer biological model than the SA, combining the Behavior-based intelligence theory with neuroscience. The design is kept simple, and it is implemented in the Khepera Miniature Mobile Robot. The used control scheme obtains an autonomous mobile robot that can be used to execute a mail delivery system and surveillance task inside a building floor.

  7. Multilayer network modeling creates opportunities for novel network statistics. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

    Science.gov (United States)

    Muldoon, Sarah Feldt

    2018-03-01

    As described in the review by Gosak et al., the field of network science has had enormous success in providing new insights into the structure and function of biological systems [1]. In the complex networks framework, system elements are network nodes, and connections between nodes represent some form of interaction between system elements [2]. The flexibility to define network nodes and edges to represent different aspects of biological systems has been employed to model numerous diverse systems at multiple scales.

  8. Boolean Dynamic Modeling Approaches to Study Plant Gene Regulatory Networks: Integration, Validation, and Prediction.

    Science.gov (United States)

    Velderraín, José Dávila; Martínez-García, Juan Carlos; Álvarez-Buylla, Elena R

    2017-01-01

    Mathematical models based on dynamical systems theory are well-suited tools for the integration of available molecular experimental data into coherent frameworks in order to propose hypotheses about the cooperative regulatory mechanisms driving developmental processes. Computational analysis of the proposed models using well-established methods enables testing the hypotheses by contrasting predictions with observations. Within such framework, Boolean gene regulatory network dynamical models have been extensively used in modeling plant development. Boolean models are simple and intuitively appealing, ideal tools for collaborative efforts between theorists and experimentalists. In this chapter we present protocols used in our group for the study of diverse plant developmental processes. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature.

  9. Summary of the first meeting of ASEAN Network of Regulatory Bodies on Atomic Energy (ASEANTOM)

    International Nuclear Information System (INIS)

    Siriratana Biramontri, Pantip Ampornrat

    2013-01-01

    The 1st Meeting of ASEAN Network of Regulatory Bodies on Atomic Energy (ASEANTOM) was organized in Phuket, Thailand on 3 - 4 September, 2013. The meeting was held on annually basis following the Meeting to Finalize the Term of Reference (TOR) in Bangkok, Thailand on 29 August, 2012. The objective of the meeting is to review and finalize TOR, and to set up the action plan of ASEANTOM. The action plan is an expected outcome of the meeting. The Meeting consisted of 41 participants from IAEA and ASEAN Member States (AMS), namely, Cambodia, Laos, Singapore, Indonesia, Malaysia, Myanmar, Philippines, Vietnam and Thailand. Only Brunei Darussalam could not attend the Meeting. Participant's organizations were regulatory body or relevant authorities, and Ministry of Foreign Affairs.

  10. Increasing galactose consumption by Saccharomyces cerevisiae through metabolic engineering of the GAL gene regulatory network

    DEFF Research Database (Denmark)

    Østergaard, Simon; Olsson, Lisbeth; Johnston, M.

    2000-01-01

    Increasing the flux through central carbon metabolism is difficult because of rigidity in regulatory structures, at both the genetic and the enzymatic levels. Here we describe metabolic engineering of a regulatory network to obtain a balanced increase in the activity of all the enzymes in the pat...... media. The improved galactose consumption of the gal mutants did not favor biomass formation, but rather caused excessive respiro-fermentative metabolism, with the ethanol production rate increasing linearly with glycolytic flux....... by eliminating three known negative regulators of the GAL system: Gale, Gal80, and Mig1. This led to a 41% increase in flux through the galactose utilization pathway compared with the wild-type strain. This is of significant interest within the field of biotechnology since galactose is present in many industrial...

  11. The cell envelope stress response of Bacillus subtilis: from static signaling devices to dynamic regulatory network.

    Science.gov (United States)

    Radeck, Jara; Fritz, Georg; Mascher, Thorsten

    2017-02-01

    The cell envelope stress response (CESR) encompasses all regulatory events that enable a cell to protect the integrity of its envelope, an essential structure of any bacterial cell. The underlying signaling network is particularly well understood in the Gram-positive model organism Bacillus subtilis. It consists of a number of two-component systems (2CS) and extracytoplasmic function σ factors that together regulate the production of both specific resistance determinants and general mechanisms to protect the envelope against antimicrobial peptides targeting the biogenesis of the cell wall. Here, we summarize the current picture of the B. subtilis CESR network, from the initial identification of the corresponding signaling devices to unraveling their interdependence and the underlying regulatory hierarchy within the network. In the course of detailed mechanistic studies, a number of novel signaling features could be described for the 2CSs involved in mediating CESR. This includes a novel class of so-called intramembrane-sensing histidine kinases (IM-HKs), which-instead of acting as stress sensors themselves-are activated via interprotein signal transfer. Some of these IM-HKs are involved in sensing the flux of antibiotic resistance transporters, a unique mechanism of responding to extracellular antibiotic challenge.

  12. Neural model of gene regulatory network: a survey on supportive meta-heuristics.

    Science.gov (United States)

    Biswas, Surama; Acharyya, Sriyankar

    2016-06-01

    Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

  13. Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

    Directory of Open Access Journals (Sweden)

    Xiaobo Guo

    Full Text Available Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs. It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC curve and the precision-recall (PR curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

  14. Diversity and plasticity of Th cell types predicted from regulatory network modelling.

    Directory of Open Access Journals (Sweden)

    Aurélien Naldi

    Full Text Available Alternative cell differentiation pathways are believed to arise from the concerted action of signalling pathways and transcriptional regulatory networks. However, the prediction of mammalian cell differentiation from the knowledge of the presence of specific signals and transcriptional factors is still a daunting challenge. In this respect, the vertebrate hematopoietic system, with its many branching differentiation pathways and cell types, is a compelling case study. In this paper, we propose an integrated, comprehensive model of the regulatory network and signalling pathways controlling Th cell differentiation. As most available data are qualitative, we rely on a logical formalism to perform extensive dynamical analyses. To cope with the size and complexity of the resulting network, we use an original model reduction approach together with a stable state identification algorithm. To assess the effects of heterogeneous environments on Th cell differentiation, we have performed a systematic series of simulations considering various prototypic environments. Consequently, we have identified stable states corresponding to canonical Th1, Th2, Th17 and Treg subtypes, but these were found to coexist with other transient hybrid cell types that co-express combinations of Th1, Th2, Treg and Th17 markers in an environment-dependent fashion. In the process, our logical analysis highlights the nature of these cell types and their relationships with canonical Th subtypes. Finally, our logical model can be used to explore novel differentiation pathways in silico.

  15. An approach to evaluate the topological significance of motifs and other patterns in regulatory networks

    Directory of Open Access Journals (Sweden)

    Wingender Edgar

    2009-05-01

    that enables to evaluate the topological significance of various connected patterns in a regulatory network. Applying this method onto transcriptional networks of three largely distinct organisms we could prove that it is highly suitable to identify most important pattern instances, but that neither motifs nor any pattern in general appear to play a particularly important role per se. From the results obtained so far, we conclude that the pairwise disconnectivity index will most likely prove useful as well in identifying other (higher-order pattern instances in transcriptional and other networks.

  16. Transcriptome analysis reveals regulatory networks underlying differential susceptibility to Botrytis cinerea in response to nitrogen availability in Solanum lycopersicum.

    Directory of Open Access Journals (Sweden)

    Andrea eVega

    2015-11-01

    Full Text Available Nitrogen (N is one of the main limiting nutrients for plant growth and crop yield. It is well documented that changes in nitrate availability, the main N source found in agricultural soils, influences a myriad of developmental programs and processes including the plant defense response. Indeed, many agronomical reports indicate that the plant N nutritional status influences their ability to respond effectively when challenged by different pathogens. However, the molecular mechanisms involved in N-modulation of plant susceptibility to pathogens are poorly characterized. In this work, we show that Solanum lycopersicum defense response to the necrotrophic fungus Botrytis cinerea is affected by plant N availability, with higher susceptibility in nitrate-limiting conditions. Global gene expression responses of tomato against B. cinerea under contrasting nitrate conditions reveals that plant primary metabolism is affected by the fungal infection regardless of N regimes. This result suggests that differential susceptibility to pathogen attack under contrasting N conditions is not only explained by a metabolic alteration. We used a systems biology approach to identify the transcriptional regulatory network implicated in plant response to the fungus infection under contrasting nitrate conditions. Interestingly, hub genes in this network are known key transcription factors involved in ethylene and jasmonic acid signaling. This result positions these hormones as key integrators of nitrate and defense against B. cinerea in tomato plants. Our results provide insights into potential crosstalk mechanisms between necrotrophic defense response and N status in plants.

  17. The Evolution of Gene Regulatory Networks that Define Arthropod Body Plans.

    Science.gov (United States)

    Auman, Tzach; Chipman, Ariel D

    2017-09-01

    Our understanding of the genetics of arthropod body plan development originally stems from work on Drosophila melanogaster from the late 1970s and onward. In Drosophila, there is a relatively detailed model for the network of gene interactions that proceeds in a sequential-hierarchical fashion to define the main features of the body plan. Over the years, we have a growing understanding of the networks involved in defining the body plan in an increasing number of arthropod species. It is now becoming possible to tease out the conserved aspects of these networks and to try to reconstruct their evolution. In this contribution, we focus on several key nodes of these networks, starting from early patterning in which the main axes are determined and the broad morphological domains of the embryo are defined, and on to later stage wherein the growth zone network is active in sequential addition of posterior segments. The pattern of conservation of networks is very patchy, with some key aspects being highly conserved in all arthropods and others being very labile. Many aspects of early axis patterning are highly conserved, as are some aspects of sequential segment generation. In contrast, regional patterning varies among different taxa, and some networks, such as the terminal patterning network, are only found in a limited range of taxa. The growth zone segmentation network is ancient and is probably plesiomorphic to all arthropods. In some insects, it has undergone significant modification to give rise to a more hardwired network that generates individual segments separately. In other insects and in most arthropods, the sequential segmentation network has undergone a significant amount of systems drift, wherein many of the genes have changed. However, it maintains a conserved underlying logic and function. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please

  18. E3Net: a system for exploring E3-mediated regulatory networks of cellular functions.

    Science.gov (United States)

    Han, Youngwoong; Lee, Hodong; Park, Jong C; Yi, Gwan-Su

    2012-04-01

    Ubiquitin-protein ligase (E3) is a key enzyme targeting specific substrates in diverse cellular processes for ubiquitination and degradation. The existing findings of substrate specificity of E3 are, however, scattered over a number of resources, making it difficult to study them together with an integrative view. Here we present E3Net, a web-based system that provides a comprehensive collection of available E3-substrate specificities and a systematic framework for the analysis of E3-mediated regulatory networks of diverse cellular functions. Currently, E3Net contains 2201 E3s and 4896 substrates in 427 organisms and 1671 E3-substrate specific relations between 493 E3s and 1277 substrates in 42 organisms, extracted mainly from MEDLINE abstracts and UniProt comments with an automatic text mining method and additional manual inspection and partly from high throughput experiment data and public ubiquitination databases. The significant functions and pathways of the extracted E3-specific substrate groups were identified from a functional enrichment analysis with 12 functional category resources for molecular functions, protein families, protein complexes, pathways, cellular processes, cellular localization, and diseases. E3Net includes interactive analysis and navigation tools that make it possible to build an integrative view of E3-substrate networks and their correlated functions with graphical illustrations and summarized descriptions. As a result, E3Net provides a comprehensive resource of E3s, substrates, and their functional implications summarized from the regulatory network structures of E3-specific substrate groups and their correlated functions. This resource will facilitate further in-depth investigation of ubiquitination-dependent regulatory mechanisms. E3Net is freely available online at http://pnet.kaist.ac.kr/e3net.

  19. Comparison of Control Approaches in Genetic Regulatory Networks by Using Stochastic Master Equation Models, Probabilistic Boolean Network Models and Differential Equation Models and Estimated Error Analyzes

    Science.gov (United States)

    Caglar, Mehmet Umut; Pal, Ranadip

    2011-03-01

    Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared.

  20. Evolution of Transcriptional Regulatory Networks in Pseudomonas aeruginosa During Long Time Growth in Human Hosts

    DEFF Research Database (Denmark)

    Andresen, Eva Kammer

    extent these observations relate to natural microbial populations. The focus of this thesis has been to study how regulatory networks evolve in natural systems. By using a particular infectious disease scenario (human associated persistent airway infections caused by the bacterium Pseudomonas aeruginosa...... in global regulator genes facilitate the generation of novel phenotypes which again facilitate the shift in life-style of the bacterium from an environmental opportunistic pathogen to a human airway specific pathogen. These findings are not only applicable to P. aeruginosa specific studies, but suggest that...

  1. CoryneRegNet: an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks.

    Science.gov (United States)

    Baumbach, Jan; Brinkrolf, Karina; Czaja, Lisa F; Rahmann, Sven; Tauch, Andreas

    2006-02-14

    The application of DNA microarray technology in post-genomic analysis of bacterial genome sequences has allowed the generation of huge amounts of data related to regulatory networks. This data along with literature-derived knowledge on regulation of gene expression has opened the way for genome-wide reconstruction of transcriptional regulatory networks. These large-scale reconstructions can be converted into in silico models of bacterial cells that allow a systematic analysis of network behavior in response to changing environmental conditions. CoryneRegNet was designed to facilitate the gen