Stochastic model analysis for Hes1/MiR-9 brain cell division system

Abstract

Recent studies suggest that cells make stochastic choices with respect to differentiation or division. The effect of molecule concentration on cell division rate is analysed in this work. However, the molecular mechanism underlying such stochasticity is unknown. Here, we computationally model the effects of molecule concentration (acts as noise) on the Hes1/miR-9 oscillator. Consequences of low molecular numbers of interacting species are determined experimentally by the researchers. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.