title: Stem cell modeling: From gene networks to cell populations.
abstract: Despite rapid advances in the field of stem/progenitor cells through experimental studies, relevant modeling approaches have not progressed with a similar pace. Various models have focused on particular aspects of stem cell physiology including gene regulatory networks, gene expression noise and signaling cascades activated by exogenous factors. However, the self-renewal and differentiation of stem cells is driven by the coordinated regulation of events at the subcellular, intercellular and milieu levels. Such events also span multiple time domains from the fast molecular reactions governing gene expression to the slower cell cycle and division. Thus, the development of multiscale computational frameworks for stem cell populations is highly desirable. Multiscale models are expected to aid the design of efficient differentiation strategies and bioprocesses for the generation of therapeutically useful stem cell progeny. Yet, challenges in making these models tractable and pairing those to sufficient experimental data prevent their wide adoption by the stem cell community. Here, we review modeling approaches reported for stem cell populations and associated hurdles.
authors.affiliation: Department of Chemical and Biological Engineering, State University of New York at Buffalo, Buffalo, NY 14260.
authors.forename: Jincheng
authors.initials: J
authors.lastname: Wu
authors.forename: Mahboubeh Rahmati
authors.initials: MR
authors.lastname: Rostami
authors.forename: Emmanuel S
authors.initials: ES
authors.lastname: Tzanakakis
keywords: Stem cells; deterministic model; differentiation; multiscale model; population balance model; self-renewal; stochastic model
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