Identifying Cell Reprogramming Roadblocks by Modelling Induced Pluripotency with Boolean Networks and Dynamical Systems

Abstract: The generation of induced pluripotent stem (iPS) cells from differentiated cells is a process of great scientific interest due to the medical potential of such cells. This process (called 'reprogramming' of cells) is however notoriously inefficient and still not very well understood. Therefore studies that can help us understand the interactions and mechanisms governing the reprogramming process are of great importance. In part one of this project, we develop a Boolean network that is able to emulate the full transition from mouse embryonic fibroblast (MEF) cells to iPS cells and show that it can be used to predict real knockdown behavior with an overall accuracy of 83%. We also establish a new way to accurately measure in silico reprogramming efficiency of Boolean models. Part two of this project involves the construction of a minimal dynamical systems model for gene regulatory networks that is able to simulate the reprogramming process. We show that this model accurately replicates certain key features in the cell reprogramming process and predicts that factors exhibiting specific expression dynamics act as roadblocks for cell reprogramming. Overall, the results of the two parts of this project provide valuable insights on the cell reprogramming process and help identify factors acting as roadblocks.