Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.
Methods Mol Biol. 2022;2488:145-157. doi: 10.1007/978-1-0716-2277-3_11.
The epithelial-mesenchymal transition (EMT) is a key developmental program that is often activated during the cancer invasion, metastasis, and drug resistance. However, it remains a critical question to elucidate the mechanisms of EMT. For example, how to quantify the global stability and stochastic transition dynamics of EMT under fluctuations is yet to be clarified. Here, we describe a framework and detailed steps for stochastic dynamics analysis of EMT. Starting from the underlying EMT gene regulatory network, we quantify the energy landscape of the EMT computationally. Multiple steady-state attractors are identified on the landscape surface, characterizing different cell phenotypes. The kinetic transition paths based on large deviation theory delineate the transition processes between different attractors quantitatively. The EMT or the reverse process, the mesenchymal-epithelial transition (MET), can be achieved by either a direct transition or a step-wise transition that goes through an intermediate state, depending on different extracellular environments. The landscape and transition paths presented in this chapter provide a new physical and quantitative picture to understand the underlying mechanisms of the EMT process. The approach for landscape and path analysis can be extended to other biological networks.
上皮-间充质转化 (EMT) 是一个关键的发育程序，通常在癌症侵袭、转移和耐药性过程中被激活。然而，阐明 EMT 的机制仍然是一个关键问题。例如，如何在波动下量化 EMT 的全局稳定性和随机跃迁动力学，这一点仍有待阐明。在这里，我们描述了 EMT 随机动力学分析的框架和详细步骤。从潜在的 EMT 基因调控网络开始，我们从计算上量化 EMT 的能量景观。在景观表面上确定了多个稳定状态吸引子，这些吸引子表征了不同的细胞表型。基于大偏差理论的动力学跃迁路径定量地描绘了不同吸引子之间的跃迁过程。EMT 或相反的过程，即间充质-上皮转化 (MET)，可以通过直接跃迁或逐步跃迁来实现，这取决于不同的细胞外环境，逐步跃迁会经过一个中间状态。本章中提出的景观和跃迁路径为理解 EMT 过程的潜在机制提供了一个新的物理和定量图景。景观和路径分析的方法可以扩展到其他生物网络。
