Stem cell modeling: From gene networks to cell populations.

Stem cell modeling: From gene networks to cell populations.
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DOI:
10.1016/j.coche.2013.01.001
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发表时间:
2013-02-01
影响因子:
6.6
通讯作者:
Tzanakakis ES
Tzanakakis ES
中科院分区:
工程技术2区
文献类型:
--
作者:
Wu J;Rostami MR;Tzanakakis ES

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尽管干细胞/祖细胞领域通过实验研究取得了快速进展,但相关的建模方法并没有以类似的速度发展。各种模型都集中在干细胞生理学的特定方面,包括基因调控网络、基因表达噪声和由外源因素激活的信号级联。然而,干细胞的自我更新和分化是由亚细胞、细胞间和环境水平事件的协调调节驱动的。这些事件也跨越多个时间域,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,干细胞群体的多尺度计算框架的发展是非常可取的。多尺度模型有望帮助设计有效的分化策略和生物过程,以产生治疗有用的干细胞后代。然而,在使这些模型易于处理以及将这些模型与足够的实验数据配对方面的挑战阻碍了它们被干细胞界广泛采用。在这里,我们回顾了干细胞群体和相关障碍的建模方法。
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.
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