Evolution, Modularity, and Dynamics of Gene Regulatory Networks

Amoolya Singh, University of California, Berkeley

Cells grow, divide, differentiate, and respond to changing environments by means of an intricate regulatory program. The genetic circuitry carrying out this program is staggeringly complex yet capable of remarkable evolutionary modification for different physiological contexts and ecological niches. Thus there is a tradeoff between the seemingly incompatible objectives of complexity and evolvability. To both evolve efficiently and have robust function, the network must allow just enough variability on which selection can act while preserving its intricacy.


Here, I examine this interplay between networks, phenotype and evolution via a combination of concept, theory and experiment. Using genomic, phylogenetic, and gene expression data along with 30 years of literature from experimental molecular biology and genetics, I trace the evolution of three candidate stress response networks across several hundred diverse microbial lineages. At the conceptual level, I demonstrate that genes in these networks group into surprisingly well-defined evolutionary modules with distinctive rates of evolution and conserved patterns of gene expression. In many cases, the evolutionary module is also a module of defined dynamic control in the network, and differences in module from organism to organism seem to reflect niche adaptation.


With this finding, I attempt to refine the notion of pathway homology from simple gene homology to homology of interactions between pathway components. This leads to the development of a novel probabilistic model for estimating the phylogeny of a pathway. In parallel, I experimentally investigate whether conserved phylogenetic distribution also implies conserved network dynamics, and vice versa.


Taken together, these approaches begin the attempt towards a system-level understanding of how evolution is linked to the design and dynamics of biological regulatory networks, and how these networks function across timescales from microseconds to eons.

Abstract Author(s): Amoolya H. Singh