A stochastic model of homeostasis: the roles of noise and nuclear positioning in deciding cell fate

Abstract

We study a population based cellular model starting from a single stem cell which stochastically divides to give rise to either daughter stem cells or differentiated daughter cells. There are three main components in the model: nucleus position, gene-regulatory network, and stochastic segregation of transcription factors in daughter cells. We study the proportion of self-renewal and differentiated cell lines as a function of the nucleus position, which in turn decides the plane of cleavage. Both nuclear position and noise play an important role in determining the stem cell genealogies and these results can be compared with a Markov model that ignores nucleus position. We have observed long and short genealogies from model simulation which compares well with experimental results from neuroblast and B-cell division. Symmetric cellular divisions were observed when the nucleus is apical, while asymmetric division occurs when the nucleus is towards the base. The number of clones decreases as a function of time in this model, although the average clone size increases.