论文标题
重建细胞表型过渡的数据驱动的管理方程：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
论文作者
论文摘要
具有相同基因组的细胞可以存在于不同的表型中。在经过特定的刺激和微环境时，可以改变不同的表型。一些例子包括开发过程中的细胞分化，用于诱导多能干细胞的重编程以及转分化，癌症转移和纤维化发育。细胞表型转化的调节和动力学是生物学中的一个基本问题，并且在动态系统的形式主义中进行了悠久的研究。机理驱动的建模研究的主要挑战是获得足够数量的定量信息来约束模型参数。定量方法的进步，尤其是高吞吐量单细胞技术，已经加速了从定量单单元数据中重建细胞系统的管理动力学方程的新方向，而不是主要的统计方法。在这里，我回顾了使用活细胞数据和固定细胞数据的一些近期研究，并提供了我对未来发展的看法。
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.
