Optimal sensing and control of run-and-tumble chemotaxis
Optimal sensing and control of run-and-tumble chemotaxis
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DOI：
10.1103/physrevresearch.4.013120
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发表时间：
2021-06
影响因子：
4.2
通讯作者：
Kento Nakamura;Tetsuya J. Kobayashi
中科院分区：
文献类型：
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作者：
Kento Nakamura;Tetsuya J. Kobayashi
奔跑-翻滚趋化是通过感知气味的空间梯度来搜索气味源的典型策略之一。本文从理论上分析了在奔跑和翻滚趋化过程中感知和控制的最佳方式，以阐明在生物体中实施的策略的效率。然而，由于理论上的困难，大多数尝试仅限于线性或确定性分析，即使真正的生物化学趋化系统在其感觉过程和控制反应中涉及相当大的随机性和非线性。本文结合部分观测马尔可夫决策过程（POMDP）的最优滤波和Kullback-Leibler控制理论，导出了基于配体梯度噪声感知的最优全非线性跑滚运动控制策略。导出的最优策略包括从噪声感官输入估计运行方向的最优滤波动态和调节电机输出的控制函数。我们进一步表明，这种最佳策略可以自然地与标准的生化模型和大肠杆菌趋化性的实验数据相关联。这些结果表明，我们的理论框架可以作为分析滚筒式趋化性效率和最优性的基础。
Run-and-tumble chemotaxis is one of the representative search strategies of an odor source via sensing its spatial gradient. The optimal ways of sensing and control in the run-and-tumble chemotaxis have been analyzed theoretically to elucidate the efficiency of strategies implemented in organisms. However, because of theoretical difficulties, most of attempts have been limited only to either linear or deterministic analysis even though real biological chemotactic systems involve considerable stochasticity and nonlinearity in their sensory processes and controlled responses. In this paper, by combining the theories of optimal filtering and Kullback-Leibler control of partially observed Markov decision process (POMDP), we derive the optimal and fully nonlinear strategy for controlling run-and-tumble motion depending on noisy sensing of ligand gradient. The derived optimal strategy consists of the optimal filtering dynamics to estimate the run-direction from noisy sensory input and the control function to regulate the motor output. We further show that this optimal strategy can be associated naturally with a standard biochemical model and experimental data of the Escherichia coli ’s chemotaxis. These results demonstrate that our theoretical framework can work as a basis for analyzing the efficiency and optimality of run-and-tumble chemotaxis.