Modeling Cellular Differentiation and Reprogramming with Gene Regulatory Networks
Gene expression regulation is a fundamental cellular process that enables robust functioning of cells. How different genes interact among themselves to coordinate and maintain the overall gene expression profile observed in a cell is a key question in cel
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Introduction Cellular differentiation is a key biological process through which a less specialized and immature cell evolves in to a specialized cell, reaching its fully mature form and function [1, 2]. Stem cells are crucial for developmental processes as they are pluripotent and have the ability to differentiate and transform into any cell type of the
Andra´s Hartmann and Srikanth Ravichandran contributed equally to this work. Patrick Cahan (ed.), Computational Stem Cell Biology: Methods and Protocols, Methods in Molecular Biology, vol. 1975, https://doi.org/10.1007/978-1-4939-9224-9_2, © Springer Science+Business Media, LLC, part of Springer Nature 2019
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body. Importantly, this ability of stem cells elicit tremendous interest in cellular reprogramming, which is a process where a more differentiated cell type is transformed into a progenitor or pluripotent stem cell by targeted manipulation of specific transcription factors (TFs) [1]. Due to the clinical potential of reprogrammed stem cells, it is important to understand how stem cell fate decisions are determined. The potential cell fates of stem cells include self-renewal, differentiation and lineage specification, programmed cell death, and quiescence [2]. Further, these choices of cell fate identities also apply to the immediate progeny of stem cells, known as progenitor or transit-amplifying cells. A major challenge in the field of stem cell biology is to identify the key molecular factors that determine the different cell fate options confronting stem and progenitor cells. Understanding the mechanisms involved in controlling the stem cell fate has important applications for biomedical research in general, ranging from elucidating the causes of cancer to the use of stem cells in regenerative medicine. 1.1 Gene Regulatory Networks
Over the years, several approaches have been developed that enable the experimental or computational identification of interactions between genes and TFs within a given cellular context [3–6]. However, it is not straightforward to interpret and analyze these datasets directly due to the sheer volume of the data and its complexity. In this regard, computational network modeling is a powerful approach to overcome the inherent complexity of biological processes that arise due to several interacting genes and the nonlinear nature of their interactions [7, 8]. For instance, mammalian genome consists of approximately 30,000 genes, which will result in staggering 230000 potential combinations of gene expression values when we consider only “ON” and “OFF” states for each gene [2]. However, in reality, gene expression is continuous; this makes the potential gene expression space even larger. It is difficult to comprehend how such a large repertoire of potential gene expression states can be constrained so that only several hundreds of different cell types, which together comprise an organism, are stably maintained. To achieve this, gene expression and gene-gene interactions must exhibit robustness with r
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