Moment-Based Estimation of State-Switching Rates in Cell Populations

Abstract

In isogenic cell populations, cells can switch back and forth between different gene expression states. These expression states can be biologically relevant. For example, a certain expression state may cause a tumor cell to be resistant to treatment, while another state may leave it vulnerable to treatment. However, estimating the rates of state-switching can be difficult, because experimentally measuring a cells transcriptome often involves destroying the cell, so it can only be measured once. In this paper, we propose a computational method to estimate the rate of switching between expression states, given data from a Luria-Delbruck style fluctuation test that is experimentally simple and feasible. We then benchmark this method using simulated data to test its efficacy, with varying assumptions made about cell cycle timing distribution in the simulations.