Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8654, Japan.
Phys Rev Lett. 2021 Mar 26;126(12):128102. doi: 10.1103/PhysRevLett.126.128102.
The chemotactic network of Escherichia coli has been studied extensively both biophysically and information theoretically. Nevertheless, connection between these two aspects is still elusive. In this work, we report such a connection. We derive an optimal filtering dynamics under the assumption that E. coli's sensory system optimally infers the binary information whether it is swimming up or down along an exponential ligand gradient from noisy sensory signals. Then we show that a standard biochemical model of the chemotactic network is mathematically equivalent to this information-theoretically optimal dynamics. Moreover, we demonstrate that an experimentally observed nonlinear response relation can be reproduced from the optimal dynamics. These results suggest that the biochemical network of E. coli chemotaxis is designed to optimally extract the binary information along an exponential gradient in a noisy condition.
大肠杆菌的趋化网络在生物物理和信息理论方面都得到了广泛的研究。然而，这两个方面之间的联系仍然难以捉摸。在这项工作中，我们报告了这种联系。我们假设大肠杆菌的感应系统从噪声感应信号中最优地推断出它是否沿着指数配体梯度向上或向下游动的二进制信息，在此假设下推导出最优滤波动力学。然后我们表明，趋化网络的标准生化模型在数学上等同于这种信息理论上的最优动力学。此外，我们证明了从最优动力学中可以再现实验观察到的非线性响应关系。这些结果表明，大肠杆菌趋化作用的生化网络是为了在噪声条件下最优地从指数梯度中提取二进制信息而设计的。
