Computational Methods to Identify Cell-Fate Determinants, Identity Transcription Factors, and Niche-Induced Signaling Pathways for Stem Cell Research.

Abstract

The large-scale development of high-throughput sequencing technologies has not only allowed the generation of reliable omics data related to various regulatory layers but also the development of novel computational models in the field of stem cell research. These computational approaches have enabled the disentangling of a complex interplay between these interrelated layers of regulation by interpreting large quantities of biomedical data in a systematic way. In the context of stem cell research, network modeling of complex gene-gene interactions has been successfully used for understanding the mechanisms underlying stem cell differentiation and cellular conversion. Notably, it has proven helpful for predicting cell-fate determinants and signaling molecules controlling such processes. This chapter will provide an overview of various computational approaches that rely on single-cell and/or bulk RNA sequencing data for elucidating the molecular underpinnings of cell subpopulation identities, lineage specification, and the process of cell-fate decisions. Furthermore, we discuss how these computational methods provide the right framework for computational modeling of biological systems in order to address long-standing challenges in the stem cell field by guiding experimental efforts in stem cell research and regenerative medicine.