Statistical Modelling for Cell Reprogramming

Abstract

Cells generally begin their lives as a pluripotent stem cell that gradually differentiates into specialised cell fates over time. However, recent advances in cell reprogramming have successfully converted differentiated cells to other cell types, by overexpressing a combination of transcription factors, fundamentally altering our view of cell identity. This could have large implications for the field of regenerative medicine as cell reprogramming offers the potential to regrow, repair or replace tissues and organs which have been damaged from age or disease. Despite much attention, combinations of transcription factors to drive reprogramming were mostly determined by trial and error, taking up considerable time and resources. To this end, computational methods have been developed to simulate the reprogramming process in silico with the goal of guiding the hypotheses to be experimentally validated. These models have provided a variety of perspectives to the process of cell reprogramming, however they all suffer from limitations in generalisability and scalability. Here, we categorise the existing computational approaches for cell reprogramming and critically evaluate their applicability in a broader context. We propose a novel method which leverages emerging multimodal single cell data. By integrating these different modes of data, we can create a more holistic model of a cell's regulatory system which provides a more accurate and scalable model for reprogramming. We demonstrate the applicability our method by recapitulating known properties of cell differentiation and reprogramming in both simulated and experimental data. We hope that this thesis will contribute to our understanding of the role of gene regulation in cell reprogramming by synthesising the existing computational models. Furthermore, our novel method may be a starting point for future computational models to integrate data from multiple modalities to create more comprehensive models for cell regulation.