Literature DB >> 22680499

Emergence of robustness against noise: A structural phase transition in evolved models of gene regulatory networks.

Tiago P Peixoto1.   

Abstract

We investigate the evolution of Boolean networks subject to a selective pressure which favors robustness against noise, as a model of evolved genetic regulatory systems. By mapping the evolutionary process into a statistical ensemble and minimizing its associated free energy, we find the structural properties which emerge as the selective pressure is increased and identify a phase transition from a random topology to a "segregated-core" structure, where a smaller and more densely connected subset of the nodes is responsible for most of the regulation in the network. This segregated structure is very similar qualitatively to what is found in gene regulatory networks, where only a much smaller subset of genes--those responsible for transcription factors-is responsible for global regulation. We obtain the full phase diagram of the evolutionary process as a function of selective pressure and the average number of inputs per node. We compare the theoretical predictions with Monte Carlo simulations of evolved networks and with empirical data for Saccharomyces cerevisiae and Escherichia coli.

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Year:  2012        PMID: 22680499     DOI: 10.1103/PhysRevE.85.041908

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  6 in total

1.  The influence of assortativity on the robustness and evolvability of gene regulatory networks upon gene birth.

Authors:  Dov A Pechenick; Jason H Moore; Joshua L Payne
Journal:  J Theor Biol       Date:  2013-03-28       Impact factor: 2.691

2.  Bow-tie architecture of gene regulatory networks in species of varying complexity.

Authors:  Gourab Ghosh Roy; Shan He; Nicholas Geard; Karin Verspoor
Journal:  J R Soc Interface       Date:  2021-06-09       Impact factor: 4.118

3.  Stability analysis of a model gene network links aging, stress resistance, and negligible senescence.

Authors:  Valeria Kogan; Ivan Molodtsov; Leonid I Menshikov; Robert J Shmookler Reis; Peter Fedichev
Journal:  Sci Rep       Date:  2015-08-28       Impact factor: 4.379

4.  Limits and trade-offs of topological network robustness.

Authors:  Christopher Priester; Sebastian Schmitt; Tiago P Peixoto
Journal:  PLoS One       Date:  2014-09-24       Impact factor: 3.240

5.  Reverse engineering gene regulatory networks: coupling an optimization algorithm with a parameter identification technique.

Authors:  Yu-Ting Hsiao; Wei-Po Lee
Journal:  BMC Bioinformatics       Date:  2014-12-03       Impact factor: 3.169

6.  Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks.

Authors:  Pablo Villegas; José Ruiz-Franco; Jorge Hidalgo; Miguel A Muñoz
Journal:  Sci Rep       Date:  2016-10-07       Impact factor: 4.379

  6 in total

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