Spector Alexander A, Grayson Warren L
Department of Biomedical Engineering and ‡Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States.
Institute for Nanobiotechnology (INBT) and ∥Department of Material Sciences & Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore 21218, Maryland, United States.
ACS Biomater Sci Eng. 2017 Nov 13;3(11):2702-2711. doi: 10.1021/acsbiomaterials.6b00606. Epub 2017 Feb 1.
Mathematical (computational) modeling approaches can be effective tools in providing insight into cell-fate decisions. In this article, several major approaches to the modeling of embryonic, hematopoietic, adipose-derived, cancer, and neural stem cell differentiation are discussed. First, the population dynamics approach is considered. The models described as bifurcating dynamical systems that result in bistability or periodic oscillations are then discussed. Also, spatiotemporal models of cell differentiation, including continuum and discrete (agent- and rule-based) approaches, are reviewed. Further, the effects of the mechanical factors are discussed, including the convergence of the differentiation and mechanotransducton pathways and computational analysis of the extracellular matrix (surrounding tissue). Finally, the stochastic models that take into account the molecular noise of internal and external origins are reviewed. The effectiveness of the modeling in the creation of the improved differentiation platforms, elucidation of various pathological conditions, and analysis of treatment regiments has been demonstrated.
数学（计算）建模方法可以成为洞察细胞命运决定的有效工具。在本文中，将讨论几种用于胚胎、造血、脂肪来源、癌症和神经干细胞分化建模的主要方法。首先，考虑群体动力学方法。然后讨论被描述为导致双稳态或周期性振荡的分岔动力系统的模型。此外，还综述了细胞分化的时空模型，包括连续和离散（基于主体和规则）方法。进一步讨论了机械因素的影响，包括分化和机械转导途径的趋同以及细胞外基质（周围组织）的计算分析。最后，综述了考虑内源性和外源性分子噪声的随机模型。建模在创建改进的分化平台、阐明各种病理状况以及分析治疗方案方面的有效性已得到证明。
