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Abstract

Expression noise is the variability of the amount of gene product among isogenic cells grown
in identical conditions. Expression noise originates from the inherent stochasticity of diffusion and
binding of the molecular players involved in gene transcription and translation. It was shown that
expression noise is an evolvable trait and that central genes in gene networks exhibit less noise. To
study the evolution of expression noise within gene networks, a gene network model that represents
expression noise and is computationally feasible to use in forward-in-time simulations is required.
Here I introduce a new model of gene regulatory networks, which represents expression noise and
is fast enough to be used in evolutionary simulations. I validate the model by replicating previously
known results from experimental data on the expression noise evolution of an individual gene under
selection. Further, I use the unique feature of the model to simulate the noise evolution of a single
gene under selection within a gene regulatory network, in which case not only the selected gene
responds to selection, but also its upstream genes. The response of upstream genes shows that noise
propagation from one gene to downstream genes, a known feature of gene regulatory networks, is
captured with the new model. In conclusion, the gene regulatory network model introduced in this
study captures key features of gene regulatory networks, is fast enough for evolutionary simulations
and is, to my knowledge, the first model of gene networks to include evolvable expression noise.
It can be used to systematically study the evolution of expression noise of genes in the context of
gene networks.