Wu Jincheng, Rostami Mahboubeh Rahmati, Tzanakakis Emmanuel S
Department of Chemical and Biological Engineering, State University of New York at Buffalo, Buffalo, NY 14260.
Curr Opin Chem Eng. 2013 Feb 1;2(1):17-25. doi: 10.1016/j.coche.2013.01.001.
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.
尽管通过实验研究，干细胞/祖细胞领域取得了快速进展，但相关的建模方法却没有以类似的速度发展。各种模型都聚焦于干细胞生理学的特定方面，包括基因调控网络、基因表达噪声以及由外源性因素激活的信号级联反应。然而，干细胞的自我更新和分化是由亚细胞、细胞间和环境水平上事件的协同调控驱动的。这些事件还跨越了多个时间域，从控制基因表达的快速分子反应到较慢的细胞周期和分裂。因此，非常需要为干细胞群体开发多尺度计算框架。多尺度模型有望有助于设计高效的分化策略和生物过程，以产生具有治疗用途的干细胞后代。然而，使这些模型易于处理并将其与足够的实验数据配对所面临的挑战，阻碍了它们在干细胞领域的广泛应用。在这里，我们综述了针对干细胞群体报道的建模方法及相关障碍。
