Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks.

Luca Albergante,Timothy J Newman,J Julian Blow

doi:10.7554/elife.02863

Abstract

The gene regulatory network (GRN) is the central decision-making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.

Highlights

At every level of organisation, biological entities, such as genes, proteins and cells, function as ensembles
We show that published gene regulatory network (GRN), including those of E. coli, M. tuberculosis, P. aeruginosa, S. cerevisiae, mouse and humans are robust in this way, a property we term ‘Buffered Qualitative Stability’ (BQS)
Because there is an inevitable time lag between a transcription factor (TF) binding to the promoter of a gene and the production of the protein product of that gene, this form of instability can occur if GRNs contain feedback loops consisting of three or more TFs

Summary

Introduction

At every level of organisation, biological entities, such as genes, proteins and cells, function as ensembles. Interaction networks are a fundamental feature of biological systems, and a vast amount of analysis exploring the organisation of biological networks has been performed (Milo et al, 2002; Barabasi and Oltvai, 2004; Alon, 2006; Buchanan et al, 2010) This analysis has provided interesting insights into the features of these networks (Barabasi and Oltvai, 2004; Brock et al, 2009; Tyson and Novák, 2010; Ferrell et al, 2011; Liu et al, 2011; Cowan et al, 2012), and has led to new methodologies for characterizing their topologies. BQS is an important step in providing a general mechanistic explanation for the overall structure of GRNs at different scales and in shedding new light on previous observations

Methods

Results

Conclusion