大梯度环境中基于路径的动力学模型的宏观极限
Macroscopic limits of pathway-based kinetic models for E.coli chemotaxis in large gradient environments
AI总结：
研究通过构建基于信号通路动力学的宏观模型，解释大梯度环境中大肠杆菌趋化行为的分子机制，验证了模型在不同空间梯度范围内的有效性。
摘要：
理解大肠杆菌趋化行为的分子起源对生物学具有重要意义，通过基于底层信号通路动态的群体水平模型进行研究。我们推导了大肠杆菌趋化行为的宏观模型，其在所有空间梯度范围内均能与基于个体的模型（SPECS）定量匹配，特别是在化学梯度较大时，标准Keller-Segel模型不再适用。这些方程通过形式化和严格的方法作为路径动力学方程的渐进行为极限推导得出。我们还展示了数值结果，显示宏观模型与SPECS之间有良好的一致性。本研究为如何从趋化行为的分子机制确定群体水平的扩散系数和漂移速度提供了答案，适用于浅梯度和大梯度环境。
英文摘要：
It is of great biological interest to understand the molecular origins of chemotactic behavior of E. coli by developing population-level models based on the underlying signaling pathway dynamics. We derive macroscopic models for E.coli chemotaxis that match quantitatively with the agent-based model (SPECS) for all ranges of the spacial gradient, in particular when the chemical gradient is large such that the standard Keller-Segel model is no longer valid. These equations are derived both formally and rigorously as asymptotic limits for pathway-based kinetic equations. We also present numerical results that show good agreement between the macroscopic models and SPECS. Our work provides an answer to the question of how to determine the population-level diffusion coefficient and drift velocity from the molecular mechanisms of chemotaxis, for both shallow gradients and large gradients environments.
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