Abstract

Gene regulatory networks show robustness to perturbations. Previous works identified robustness as an emergent property of gene network evolution but the underlying molecular mechanisms are poorly understood. We used a multi-tier modeling approach that integrates molecular sequence and structure information with network architecture and population dynamics. Structural models of transcription factor-DNA complexes are used to estimate relative binding specificities. In this model, mutations in the DNA cause changes on two levels: (a) at the sequence level in individual binding sites (modulating binding specificity), and (b) at the network level (creating and destroying binding sites). We used this model to dissect the underlying mechanisms responsible for the evolution of robustness in gene regulatory networks. Results suggest that in sparse architectures (represented by short promoters), a mixture of local-sequence and network-architecture level changes are exploited. At the local-sequence level, robustness evolves by decreasing the probabilities of both the destruction of existent and generation of new binding sites. Meanwhile, in highly interconnected architectures (represented by long promoters), robustness evolves almost entirely via network level changes, deleting and creating binding sites that modify the network architecture.

Highlights

  • Robustness to genetic and environmental perturbations is ubiquitous in biological systems [1]

  • Previous research has shown these gene regulatory networks to be robust to perturbations at the level of the connections between transcription factors

  • We investigate the mechanisms underlying the evolution of robustness in gene networks using a modeling approach, which considers three levels: binding of individual transcription factors to DNA, dynamics of gene expression levels, and fitness effects at the population level

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Summary

Introduction

Robustness to genetic and environmental perturbations is ubiquitous in biological systems [1]. Previous theoretical studies using gene regulatory network models have shown how robustness evolves under conditions of stabilizing selection [8]. In this class of models, mutations were allowed to alter only the interaction strengths, constraining the model to fixed network architectures. Model refinements that allowed the network architecture itself to evolve have highlighted the importance of network lability even under conditions of stabilizing selection, while making arbitrary and sometimes conflicting assumptions about gain and loss of interactions [13,14]. The network lability seen in these cases coincides with genomic studies where, for example, high rates of gain and loss of cis-regulatory elements are observed [15,16,17] including cases where function is highly conserved [18]

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