Stochastic Modeling of Stem Cell Induction Protocols
Generation of pluripotent stem cells starting from adult human cells using induction processes is a technology that has the potential to revolutionize regenerative medicine. However, the production of these so called iPS cells is still quite inefficient and may be dominated by stochastic effects. In this work we build mass action models of the core circuitry controlling stem cell induction and maintenance. The model includes not only the network of transcription factors NANOG, OCT4, SOX2, but also important epigenetic regulatory features of DNA methylation and histone modifications. We are able to show that the network topology reported in the literature is consistent with the observed experimental behavior of bistability and inducibility. Based on simulations of stem cell generation protocols we show that cooperative and independent reaction mechanisms have experimentally identifiable differences in the dynamics of reprogramming, and we analyze such differences and their biological basis. It had been argued that stochastic and elite models of stem cell generation represent distinct fundamental mechanisms. Work presented here illustrates the possibility that rather they represent differences in the amount of information we have about the distribution of cellular states before and during reprogramming protocols. We show that unpredictability decreases as the cell moves through the necessary induction stages, and that identifiable groups of cells with elite-like behavior can come about by stochastic process. We also show how different mechanisms and kinetic properties impact the prospects of improving the efficiency of iPS cell generation protocols.