Stability depends on positive autoregulation in Boolean gene regulatory networks

PLoS Comput Biol. 2014 Nov 6;10(11):e1003916. doi: 10.1371/journal.pcbi.1003916. eCollection 2014 Nov.


Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs). The most basic motif, autoregulation, has been associated with bistability (when positive) and with homeostasis and robustness to noise (when negative), but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Computational Biology
  • Diptera
  • Gene Regulatory Networks*
  • Homeostasis / genetics*
  • Mammals
  • Models, Biological*
  • Phenotype
  • Yeasts

Grant support

The Ph.D. Program in Computational Biology is sponsored by Fundação Calouste Gulbenkian, Siemens SA, and Fundação para a Ciência e Tecnologia (fellowship SFRH/BD/33531/2008). The research was also supported in part by The Stanford Center for Computational, Evolutionary and Human Genomics, and by NIH grant no. GM28016. VG gratefully acknowledges the funding of the Swiss National Science Foundation, grant number P1EZP3_148648. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.