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首页> 外文期刊>Journal of Mathematical Biology >Macroscopic equations for bacterial chemotaxis: integration of detailed biochemistry of cell signaling
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Macroscopic equations for bacterial chemotaxis: integration of detailed biochemistry of cell signaling

机译:细菌趋化性的宏观方程式:细胞信号转导的详细生物化学的整合

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Chemotaxis of single cells has been extensively studied and a great deal on intracellular signaling and cell movement is known. However, systematic methods to embed such information into continuum PDE models for cell population dynamics are still in their infancy. In this paper, we consider chemotaxis of run-and-tumble bacteria and derive continuum models that take into account of the detailed biochemistry of intracellular signaling. We analytically show that the macroscopic bacterial density can be approximated by the Patlak-Keller-Segel equation in response to signals that change slowly in space and time. We derive, for the first time, general formulas that represent the chemotactic sensitivity in terms of detailed descriptions of single-cell signaling dynamics in arbitrary space dimensions. These general formulas are useful in explaining relations of single cell behavior and population dynamics. As an example, we apply the theory to chemotaxis of bacterium Escherichia coli and show how the structure and kinetics of the intracellular signaling network determine the sensing properties of E. coli populations. Numerical comparison of the derived PDEs and the underlying cell-based models show quantitative agreements for signals that change slowly, and qualitative agreements for signals that change extremely fast. The general theory we develop here is readily applicable to chemotaxis of other run-and-tumble bacteria, or collective behavior of other individuals that move using a similar strategy.
机译:已经对单细胞的趋化性进行了广泛的研究,并且已知许多有关细胞内信号传导和细胞运动的信息。但是,将此类信息嵌入到细胞种群动态的连续PDE模型中的系统方法仍处于起步阶段。在本文中,我们考虑了游击细菌的趋化性,并得出了考虑到细胞内信号转导的详细生物化学的连续模型。我们分析地表明,响应于在空间和时间上缓慢变化的信号,宏观细菌密度可以通过Patlak-Keller-Segel方程来近似。我们首次得出在任意空间维度上单细胞信号动力学的详细描述方面代表趋化敏感性的通用公式。这些通用公式可用于解释单细胞行为与种群动态之间的关系。例如,我们将该理论应用于大肠杆菌的趋化性,并展示了细胞内信号网络的结构和动力学如何决定大肠杆菌种群的感应特性。派生的PDE和基础的基于细胞的模型的数值比较显示,对于缓慢变化的信号,存在定量一致性,而对于快速变化的信号,则存在定性一致性。我们在这里建立的一般理论很容易适用于其他奔腾细菌的趋化性,或使用类似策略移动的其他个体的集体行为。

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