Exploring signal-driven gene diversity: a novel cascade approach

Abstract

Cells must have the ability to respond to various external signals, but our understanding of this signal response is very limited. Here we analyze a simple cascade model of stochastic gene expression, which assumes that the upstream (input) gene is expressed in a constitutive manner whereas the downstream (output) gene is expressed in a bursty fashion via a two-state model, and that the former regulates the latter via transcription factors (products of the former). By large scale sampling in the space of model parameters, we find that the output distributions can exhibit diverse patterns such as unimodal, bimodal or trimodal modes, depending on the regulation way (positive or negative). These patterns, which are different from those in the case of no regulation, are independent of the choice of parameter values and hence qualitatively invariant. Diverse expression patterns resulting from signal response would be important for cell fate decisions and utilized by cells for a better survival in complex environments.