Abstract
The role of stochasticity on gene expression is widely discussed. Both potential advantages and disadvantages have been revealed. In some systems, noise in gene expression has been quantified, in among others the lac operon of Escherichia coli. Whether stochastic gene expression in this system is detrimental or beneficial for the cells is, however, still unclear. We are interested in the effects of stochasticity from an evolutionary point of view. We study this question in the lac operon, taking a computational approach: using a detailed, quantitative, spatial model, we evolve through a mutation–selection process the shape of the promoter function and therewith the effective amount of stochasticity. We find that noise values for lactose, the natural inducer, are much lower than for artificial, nonmetabolizable inducers, because these artificial inducers experience a stronger positive feedback. In the evolved promoter functions, noise due to stochasticity in gene expression, when induced by lactose, only plays a very minor role in short-term physiological adaptation, because other sources of population heterogeneity dominate. Finally, promoter functions evolved in the stochastic model evolve to higher repressed transcription rates than those evolved in a deterministic version of the model. This causes these promoter functions to experience less stochasticity in gene expression. We show that a high repression rate and hence high stochasticity increases the delay in lactose uptake in a variable environment. We conclude that the lac operon evolved such that the impact of stochastic gene expression is minor in its natural environment, but happens to respond with much stronger stochasticity when confronted with artificial inducers. In this particular system, we have shown that stochasticity is detrimental. Moreover, we demonstrate that in silico evolution in a quantitative model, by mutating the parameters of interest, is a promising way to unravel the functional properties of biological systems.
Highlights
Noise in gene expression, i.e., the variation in gene expression in an isogenic population in a homogeneous environment, has drawn much attention in recent years
We studied the evolutionary consequences of stochastic gene expression in one of the best-known systems of genetic regulation, the lac operon of E. coli, which regulates lactose uptake and metabolism
We find that cells evolve their repressed transcription rates to higher values in a stochastic model than in a deterministic model
Summary
I.e., the variation in gene expression in an isogenic population in a homogeneous environment, has drawn much attention in recent years. When two isogenic cells vary in gene expression, this can be due to variation in factors determining gene expression in these cells, such as transcription factors, the concentration of RNA polymerase, the cell cycle, etc., which is called extrinsic noise. All extrinsic noise is absent, gene expression between these cells would still be different, because gene expression is inherently stochastic, due to the low numbers of molecules involved. It has been clearly shown that gene expression can be stochastic [1,2,3]. The implications of stochastic gene expression are, much less clear. There is very little known about the consequences of stochasticity on particular systems
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