细胞表型转变的数据驱动控制方程重构：数据科学与系统生物学的整合
Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology
摘要：
具有相同基因组的细胞可以以不同的表型存在。并且当受到特定的刺激和微环境时可以在不同的表型之间变化。一些实例包括发育期间的细胞分化、诱导多能干细胞的重编程和转分化、癌症转移和纤维化发展。细胞表型转换的调控和动力学是生物学中的一个基本问题，并且在动力系统的形式主义中有着悠久的研究历史。机制驱动建模研究的一个主要挑战是获取足够数量的定量信息来约束模型参数。定量方法，特别是高通量单细胞技术的进步，加速了一个新的方向的出现，从定量单细胞数据重建细胞系统的动力学方程，超越了占主导地位的统计方法。在这里，我回顾了一些最近的研究使用活细胞和固定细胞的数据，并提供我对未来发展的看法。
英文摘要：
Cells with the same genome can exist in different phenotypes. and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis development. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
