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量化人类干细胞网络分化和重编程的细胞命运决策:景观和生物学途径。

Quantifying cell fate decisions for differentiation and reprogramming of a human stem cell network: landscape and biological paths.

机构信息

Department of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, New York, United States of America.

出版信息

PLoS Comput Biol. 2013;9(8):e1003165. doi: 10.1371/journal.pcbi.1003165. Epub 2013 Aug 1.

Abstract

Cellular reprogramming has been recently intensively studied experimentally. We developed a global potential landscape and kinetic path framework to explore a human stem cell developmental network composed of 52 genes. We uncovered the underlying landscape for the stem cell network with two basins of attractions representing stem and differentiated cell states, quantified and exhibited the high dimensional biological paths for the differentiation and reprogramming process, connecting the stem cell state and differentiated cell state. Both the landscape and non-equilibrium curl flux determine the dynamics of cell differentiation jointly. Flux leads the kinetic paths to be deviated from the steepest descent gradient path, and the corresponding differentiation and reprogramming paths are irreversible. Quantification of paths allows us to find out how the differentiation and reprogramming occur and which important states they go through. We show the developmental process proceeds as moving from the stem cell basin of attraction to the differentiation basin of attraction. The landscape topography characterized by the barrier heights and transition rates quantitatively determine the global stability and kinetic speed of cell fate decision process for development. Through the global sensitivity analysis, we provided some specific predictions for the effects of key genes and regulation connections on the cellular differentiation or reprogramming process. Key links from sensitivity analysis and biological paths can be used to guide the differentiation designs or reprogramming tactics.

摘要

细胞重编程最近在实验上得到了深入研究。我们开发了一个全局势能景观和动力学路径框架,以探索由 52 个基因组成的人类干细胞发育网络。我们揭示了干细胞网络的基础景观,有两个吸引盆地代表干细胞和分化细胞状态,量化并展示了分化和重编程过程的高维生物路径,连接了干细胞状态和分化细胞状态。景观和非平衡卷曲通量共同决定细胞分化的动力学。通量导致动力学路径偏离最陡下降梯度路径,并且对应的分化和重编程路径是不可逆的。路径的量化使我们能够找出分化和重编程是如何发生的,以及它们经历了哪些重要状态。我们表明,发育过程是从干细胞吸引盆地向分化吸引盆地进行的。由势垒高度和跃迁速率表征的景观地形定量地决定了细胞命运决策过程的全局稳定性和动力学速度。通过全局敏感性分析,我们对关键基因和调控连接对细胞分化或重编程过程的影响提供了一些具体的预测。敏感性分析和生物路径的关键环节可用于指导分化设计或重编程策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a213/3731225/15399b364576/pcbi.1003165.g001.jpg

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