Computational Biology of Embryonic Stem Cells

Computational Biology of microRNA-Pluripotency Gene Networks in Embryonic Stem Cells

Author(s): Preethi H. Gunaratne and Jayantha B. Tennakoon

Pp: 161-179 (19)

DOI: 10.2174/978160805025311201010161

* (Excluding Mailing and Handling)


Spectacular advances in technology and computational biology spawned in large part by the Human Genome Project have been instrumental in transforming the field of embryonic stem cell biology. From the findings reported in the last decade it is clear that properties unique to embryonic stem cells (ESCs) are regulated not by individual genes but by complex gene networks that include both genes and ~22 nt noncoding microRNAs that act to integrate multiple genes across diverse signaling pathways to regulate self-renewal and differentiation. In this chapter we will discuss the evolution of our understanding of regulatory networks underlying stem cell self-renewal and pluripotency made possible through highthroughput genomic studies. In the last decade molecular technologies that revealed key transcription and epigenetic factors in ESCs have given way to highthroughput microarray and Next Generation Sequencing technologies. These largescale genomics datasets analyzed through the latest bioinformatic and computational methods have been instrumental in transforming the field of embryonic stem cells. We will trace the history of ES cells to briefly discuss key genes and microRNAs that have been established to regulate self-renewal and pluripotency in mouse and human prior to the genomics revolution. We will then discuss the latest technologies and computational algorithms that have been instrumental in revealing genome-wide changes associated with self-renewal and differentiation at the genetic and epigenetic levels to yield the current systems-level understanding of embryonic stem cells.

Keywords: miRNA, stem cells, pluripotency, epigenome, reprogramming, microarrays, gene networks, Next generation sequencing (NGS), Chromatin immunoprecipitation (ChIP), target prediction, iPSCs, bioinformatics, differentiation, polycomb group proteins (PcGs), methylation, transcription factors.

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