TY - JOUR
T1 - Stem Cell Fate Decision Making
T2 - Modeling Approaches
AU - Spector, Alexander A.
AU - Grayson, Warren L.
N1 - Publisher Copyright:
© 2017 American Chemical Society.
PY - 2017/11/13
Y1 - 2017/11/13
N2 - 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.
AB - 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.
KW - cell differentiation
KW - dynamical system
KW - mechanobiology
KW - stochastic methods
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U2 - 10.1021/acsbiomaterials.6b00606
DO - 10.1021/acsbiomaterials.6b00606
M3 - Review article
AN - SCOPUS:85031900763
SN - 2373-9878
VL - 3
SP - 2702
EP - 2711
JO - ACS Biomaterials Science and Engineering
JF - ACS Biomaterials Science and Engineering
IS - 11
ER -