Evolution of gene regulatory networks by fluctuating selection and intrinsic constraints

Masaki E. Tsuda, Masakado Kawata

    Research output: Contribution to journalArticle

    14 Citations (Scopus)

    Abstract

    Various characteristics of complex gene regulatory networks (GRNs) have been discovered during the last decade, e.g., redundancy, exponential indegree distributions, scale-free outdegree distributions, mutational robustness, and evolvability. Although progress has been made in this field, it is not well understood whether these characteristics are the direct products of selection or those of other evolutionary forces such as mutational biases and biophysical constraints. To elucidate the causal factors that promoted the evolution of complex GRNs, we examined the effect of fluctuating environmental selection and some intrinsic constraining factors on GRN evolution by using an individual-based model. We found that the evolution of complex GRNs is remarkably promoted by fixation of beneficial gene duplications under unpredictably fluctuating environmental conditions and that some internal factors inherent in organisms, such as mutational bias, gene expression costs, and constraints on expression dynamics, are also important for the evolution of GRNs. The results indicate that various biological properties observed in GRNs could evolve as a result of not only adaptation to unpredictable environmental changes but also non-adaptive processes owing to the properties of the organisms themselves. Our study emphasizes that evolutionary models considering such intrinsic constraining factors should be used as null models to analyze the effect of selection on GRN evolution.

    Original languageEnglish
    Article numbere1000873
    JournalPLoS Computational Biology
    Volume6
    Issue number8
    DOIs
    Publication statusPublished - 2010 Aug

    ASJC Scopus subject areas

    • Ecology, Evolution, Behavior and Systematics
    • Modelling and Simulation
    • Ecology
    • Molecular Biology
    • Genetics
    • Cellular and Molecular Neuroscience
    • Computational Theory and Mathematics

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