Uncovering a Hidden Distributed Architecture Behind Scale-free Transcriptional Regulatory Networks

Balaji, S.; Iyer, Lakshminarayan M.; Aravind, L.; Babu, M. Madan

doi:10.1016/j.jmb.2006.04.026

Cited by 61 publications

(80 citation statements)

References 45 publications

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“…3A; see Materials and Methods). As used in previous studies (14,15), this step only forms the most significant partnership associations.…”

Section: Resultsmentioning

confidence: 99%

“…Various data sources were used for different species: the largest collection of regulatory data obtained from published microarray data for M. tuberculosis (9), regulonDB version 6.2 for E. coli (26), results of genetic and biochemical experiments as used in previous studies for yeast (2,14,15,(27)(28)(29)(30)(31), and Transcriptional Regulatory Element Database database for rat, mouse, and human (as of June 2008) (32). The protein modification network was obtained from the Human Protein Reference Database database (33).…”

Section: Methodsmentioning

confidence: 99%

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Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

Bhardwaj

Yan

Gerstein

2010

Proc. Natl. Acad. Sci. U.S.A.

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Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. These hierarchies, in turn, can be placed between the extremes of autocracies, with welldefined levels and clear chains of command, and democracies, without such defined levels and with more co-regulatory partnerships between regulators. In general, the presence of partnerships decreases the variation in information flow amongst nodes within a level, more evenly distributing stress. Here we study various regulatory networks (transcriptional, modification, and phosphorylation) for five diverse species, Escherichia coli to human. We specify three levels of regulators-top, middle, and bottom-which collectively govern the non-regulator targets lying in the lowest fourth level. We define quantities for nodes, levels, and entire networks that measure their degree of collaboration and autocratic vs. democratic character. We show individual regulators have a range of partnership tendencies: Some regulate their targets in combination with other regulators in local instantiations of democratic structure, whereas others regulate mostly in isolation, in more autocratic fashion. Overall, we show that in all networks studied the middle level has the highest collaborative propensity and coregulatory partnerships occur most frequently amongst midlevel regulators, an observation that has parallels in corporate settings where middle managers must interact most to ensure organizational effectiveness. There is, however, one notable difference between networks in different species: The amount of collaborative regulation and democratic character increases markedly with overall genomic complexity.coregulatory partnerships | hierarchy | middle managers | autocracy | democracy

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“…3A; see Materials and Methods). As used in previous studies (14,15), this step only forms the most significant partnership associations.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

Bhardwaj

Yan

Gerstein

2010

Proc. Natl. Acad. Sci. U.S.A.

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“…The complexity of CRNs, coupled with observed "unexpected" trends in their properties, such as scale-freeness (6), high degree of clustering (7), and overrepresented subgraphs (3,(8)(9)(10), has led to several hypotheses of adaptive origins and explanations of CRNs and their properties. Central to most of these studies was the use of simplistic graph-theoretic models, such as randomly rewiring the connectivity of a biological network, to serve as a null model for CRN connectivity maps and their properties (11).…”

mentioning

confidence: 99%

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

Ruths

Nakhleh

2013

Proc. Natl. Acad. Sci. U.S.A.

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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 motifswithin 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. Reconstructing a CRN from experimental data, elucidating its dynamic and topological properties, and understanding how these properties emerge during development and evolution are major endeavors in experimental and computational biology (1-5).The complexity of CRNs, coupled with observed "unexpected" trends in their properties, such as scale-freeness (6), high degree of clustering (7), and overrepresented subgraphs (3,(8)(9)(10), has led to several hypotheses of adaptive origins and explanations of CRNs and their properties. Central to most of these studies was the use of simplistic graph-theoretic models, such as randomly rewiring the connectivity of a biological network, to serve as a null model for CRN connectivity maps and their properties (11). However, it has been shown that when subjecting CRNs to the various neutral evolutionary forces and tracing their trajectory in time, many of these topological patterns may simply arise spontaneously due to the forces of mutation, recombination, gene duplication, and genetic drift (10, 12). These studies call into question arguments that were made in favor of adaptive explanations for the emergence and conservation of CRN properties (8,(13)(14)(15) and identify important parameters that may significantly affect the evolution of CRNs from a neutral perspective. Specifically (12), they highlighted the role that promoter length, binding-site size, and population size may play in forming certain topological patterns known as motifs. Nonetheless, a lingering question remains: Which specific parts of a CRN arise due to nonadaptive forces and, moreover, can we quantify these patterns to...

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Section: Discussionmentioning

confidence: 99%

“…6 Specifically that the removal of a large number of hubs are not lethal to an organism. It has been shown that in yeast, the removal of 28 out of 33 highly connected hubs did not lead to the death of the given yeast cells 6 with little correlation between the connectivity of a node and its importance to viability. Additionally, simulations which explore the evolution of metabolic networks have resulted in networks that contain the existence of hubs but do not exhibit a clear power law 32 in their network connectivity suggesting there are non scale-free elements in the overall network.…”

Section: Discussionmentioning

confidence: 99%

Context Specific Transcription Factor Prediction

et al. 2007

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Abstract-One of the goals of systems biology is the identification of regulatory mechanisms that govern an organism's response to external stimuli. Transcription factors have been hypothesized as a major contributor to an organism's response to various outside stimuli, and a great deal of work has been done to predict the set of transcription factors which regulate a given gene. Most of the current methods seek to identify possible binding sites from genomic sequence. Initial attempts at predicting transcription factors from genomic sequences suffered from the problem of false positives. Making the problem more difficult, it has also been shown that while predicted binding sites might be false positives, they can be shown to bind to their corresponding sequences in vitro. One method for rectifying this is through the use of phylogenetic analysis in which only regions which show high evolutionary conservation are analyzed. However such an approach may be too stringent because of the level of degeneracy shown in transcription factor binding site position weight matrices. Due to the degeneracy, there may be only a few bases that need to be conserved across species. Therefore, while a sequence may not show a high level of evolutionary conservation, these sequences may still show high affinity for the same transcription factor. In predicting transcription factor binding we explore the notion that ''Coexpression implies co-regulation'' [Allocco et al. BMC Bioinformatics 5:18, 2004]. With multiple genes requiring similar transcription factors binding sites, there exists a basis for eliminating false positives. This method allows for the selection of transcription factors binding sites that are active under a given experimental paradigm, thereby allowing us to indirectly incorporate the effects of chromosome and recognition site presentation upon transcription factor binding prediction. Rather than having to rationalize that a few transcription factors binding sites are over-represented in a cluster of genes, one can show that a few transcription factors are active in the cluster of genes that have been grouped together. Although the method focuses on predicting experiment-specific transcription factor binding sites, it is possible that if such a methodology were used in an iterative process where different experiments were analyzed, one could obtain a comprehensive set of transcription factors binding sites which regulate the various dynamic responses shown by biological systems under a variety of conditions hence building a more comprehensive model of transcriptional regulation.

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Uncovering a Hidden Distributed Architecture Behind Scale-free Transcriptional Regulatory Networks

Cited by 61 publications

References 45 publications

Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

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

Context Specific Transcription Factor Prediction

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