Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties

PLoS Comput Biol. 2018 Apr 30;14(4):e1006098. doi: 10.1371/journal.pcbi.1006098. eCollection 2018 Apr.

Abstract

Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These exponents are independent of the fraction of the GRN experimentally sampled, enabling prediction of properties of the complete GRN for an organism. We further demonstrate that the exponents describe inequalities in transcription factor (TF)-target gene recognition across GRNs. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. Consequently, architectural GRN organization drives not only phenotypic plasticity within a species, but is also likely implicated in species-specific phenotype.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Arabidopsis / genetics
  • Caenorhabditis elegans / genetics
  • Computational Biology
  • Computer Simulation
  • Drosophila melanogaster / genetics
  • Gene Regulatory Networks*
  • Models, Genetic*
  • Phenotype
  • Saccharomyces cerevisiae / genetics
  • Species Specificity
  • Transcription Factors / genetics
  • Transcription Factors / metabolism

Substances

  • Transcription Factors

Grants and funding

Funding for this project was provided in part by grant IOS-1733633 from the National Science Foundation (https://www.nsf.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.