Abstract

Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor – target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

Highlights

  • Transcription factors (TFs) regulate gene expression by binding to DNA adjacent to target genes

  • Dimerization could serve as a means to generate ultrasensitive responses via molecular titration, which occurs when an active subunit is sequestered into an inactive heterodimer complex by a titrating molecule [7]

  • Dimerization influences the kinetics of the TF-DNA search process [8]

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Summary

Introduction

Transcription factors (TFs) regulate gene expression by binding to DNA adjacent to target genes. Aspects of TF-TF protein-protein interactions and how these change during evolution have been studied [1,2,3], and dimerization is known to be important for various TF families [4]. Known or presumed biological roles of TF dimerization include potential effects at the level of DNA recognition, such as facilitated proximity and enhancement of DNA-binding specificity. Roles at the level of the network output include that dimerization might function to dampen noise due to fluctuations in monomer concentrations [5], or might be important in attaining multistability in certain types of networks [6]. Dimerization influences the kinetics of the TF-DNA search process [8]

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