Modelling noise in the design of synthetic gene networks
Random variability in gene expression (noise) might severely impact on cellular behaviours. At odds with other engineering applications, where noise is usually associated with a deterioration of the functional properties, when dealing with biological systems, random variability might actually improve fitness, for instance increasing the survival rate of bacterial populations, or boosting sensitivity by stochastic focusing. Part of the random variability affecting gene expression is due to the intrinsic stochasticity of biochemical events in the presence of low copy numbers. This intrinsic contribution to noise is routinely analysed by numerical simulations based on the Chemical Master Equation and the Gillespie’s algorithm. However, the quantitative analysis of noise in gene regulatory networks by stochastic simulations is still hampered by a lack of accurate estimates of model parameters, and by the high computational cost associated with an extensive sampling of the accessible configurational space. Numerical techniques to improve parameter fitting and to accelerate sampling, thus reducing the computational cost of stochastic simulations, will be discussed. As an experimental application of stochastic simulations in synthetic bioloby, a comparison between transcriptional and post-transcriptional control mechanisms will be presented. By combining transcriptional and post-transcriptional control strategies, it is possible to tune the level of noise affecting gene expression, with possible applications to the design of synthetic circuits that are more suited to operate in a noisy environment.