Control of stochastic gene expression by a nonlinear biological oscillator.
Understanding the dynamical processes by which cells translate external stimuli into an adequate response is a fundamental problem in biology that can be addressed through the methods of physics and nonlinear dynamics. Transcription activators play a key role in this system: they are proteins that become active under certain external stimuli and trigger the expression of genes encoding for the proteins required by the cells to provide an adequate response. Their activity is commonly controlled by different negative feedbacks that regulate the duration and strength of their activations. This can give rise to pulses in the activity of the transcription activator and these systems can be referred to as “genetic oscillators”: a paradigmatic example is NF-kappaB, that can be modelled as a nonlinear stochastic oscillator. We recently showed that a simplified model of this genetic circuit can be analyzed combining ideas from dynamical systems theory and stochastic processes. Here we use those tools to show that it can also reproduce the dynamical patterns of gene expression that this genetic oscillator can produce. Furthermore, we use our approach to characterize the dynamics of our simple model in cancer cells, where the tight regulation of this circuit is lost by the effect of mutations, and show how the dynamical patterns of gene expression are disrupted. These insights can have implications in cancer biology.