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.

中文翻译：

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干细胞建模：从基因网络到细胞群。

尽管通过实验研究在干/祖细胞领域取得了快速进展，但相关建模方法并没有以类似的速度发展。各种模型专注于干细胞生理学的特定方面，包括基因调控网络、基因表达噪声和外源性因子激活的信号级联。然而，干细胞的自我更新和分化是由亚细胞、细胞间和环境水平的事件协调调节驱动的。这些事件还跨越多个时间域，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开发多尺度计算框架。预计多尺度模型将有助于设计有效的分化策略和生物过程，以产生具有治疗作用的干细胞后代。然而，使这些模型易于处理并将其与足够的实验数据配对的挑战阻碍了它们被干细胞社区广泛采用。在这里，我们回顾了针对干细胞群和相关障碍报道的建模方法。