ArticleProceedings of the National Academy of Sciences of the United States of America2013
Neutral forces acting on intragenomic variability shape the Escherichia coli regulatory network topology.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Predictive modeling of gene expression and localization of DNA binding site using deep convolutional neural networks.PLoS computational biology · 2026Article
- Differences in evolutionary accessibility determine which equally effective regulatory motif evolves to generate pulses.Genetics · 2021Article
- Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise.Nature communications · 2019Article
- Making sense of transcription networks.Cell · 2015Review
- The influence of promoter architectures and regulatory motifs on gene expression in Escherichia coli.PloS one · 2014Article
- Evolution after whole-genome duplication: a network perspective.G3 (Bethesda, Md.) · 2013Article
- Boosting forward-time population genetic simulators through genotype compression.BMC bioinformatics · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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