Skip to content

Gene Networks

In collaboration with Mark Siegal at NYU, we model the evolution of networks of transcriptional regulators in order to study emergent properties such as canalization (also known as robustness), genetic assimilation and evolutionary capacitance.

Robustness that is the product of evolution can have very different properties to robustness that is the product of an engineering process. For example, biological robustness is often distributed across the entire network, rather than being a simple consequence of redundant parts. Complex interacting networks can act as evolutionary capacitors by concealing and revealing variation, and in the process can turn “wasteful mess” into a creative force in evolution.

We are currently constructing a model of ensembles of transcriptional networks, and will use it to simulate development, as well as in silico evolution under mutation and selection. Although simplified, the model is unique in retaining four key features of real networks. First, each transcription factor binding site is either occupied or unoccupied, and binding and unbinding are stochastic processes. This, combined with the low and fluctuating copy number of each mRNA in any given cell, creates significant stochasticity in the system, which we capture explicitly in our model. Second, transcription and translation are not instantaneous processes. Our model includes delays, which can lead to the instability of kinetic systems. Third, our model includes cooperativity in transcription factor binding. Finally, natural selection acts on the outcome of the network as a whole, rather than on its individual components. The model uses parameters taken from real data when possible, particularly from Saccharomyces cerevisiae.

We will use the model to study reversible and irreversible cell differentiation, network topology, and the relationship between genetic robustness, environmental robustness and the stochasticity associated with small numbers of molecules per cell. We will also test the systems-level effects of adding greater realism: this cannot be predicted a priori, since evolution in the absence of a particular feature may find a way to compensate for it. For example, do various forms of post-transcriptional regulation such as chromatin silencing and silencing RNAs add “more of the same” or do they fundamentally change the nature of the kinetics of development and/or evolution? Is cooperativity essential to the system, and if so does adding additional cooperative elements change the dynamics? How sensitive is the system to the precise biology of transcriptional bursting? How is the robustness of a diploid system different from that of a haploid? What are the genetic and environmental bases of rare failures of the system, as a model of complex disease?


  • Brettner, L.M., Masel, J. (2012) Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast, BMC Systems Biology6:128 
  • Masel, J., & Siegal, M. L. (2009). Robustness: mechanisms and consequences. Trends Genet. (PubMed)Go to document (doi)Go to document
  • Masel, J. (2004). Genetic assimilation can occur in the absence of selection for the assimilating phenotype, suggesting a role for the canalization heuristic. J Evol Biol, 17(5), 1106-10. (PubMed)Go to document (doi)Go to document