Understanding stem cells, their property of maintaining stemness by self renewal, and differentiation into lineages and maturation is a complex biological process. The factors that control, regulate, and direct a stem cell toward its lineage are profoundly complex. They are also important in engineering cells and designing replacement therapies. Cell differentiation involves complex signal transduction pathways, cross talks, and gene regulation networks (GRNs) or genetic switches.

In this chapter we discuss stem cell fate choice and factors that play a role in determining the fate of cells. In recent years, the development of experimental and computational approaches has enabled analysis of the huge data generated by omics approaches and from studies in next generation sequencing (NGS) and has furthered our understanding of cell fate determination. Mathematical models that synthesize molecular knowledge into mathematical formalism and enable simulation of important behaviors are being designed to understand cell fate determination.

We also discuss studies reporting the applications of statistical analyses and computational methods including Bayesian networks and principal components analysis (PCA)/partial least squares (PLS) regression, providing insight into the critical aspects and components of gene regulatory networks (GRNs) and signaling pathways involved. Recently machine learning algorithm techniques such as deep learning based on “policy” and “reward” system have been developed to predict cell fate. Most of these learning systems are based on “reinforce learning algorithm” [1].