发表机构
AMOLF（阿莫夫研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过解析模型提出行为状态方程（BESTs），证实感知与执行的双向信息传输是导航的组织原则，且经大肠杆菌趋化性模拟验证。
AI 中文摘要
多种生物功能由感知与执行之间的反馈驱动，典型例子是细胞导航。导航过程中，感知系统将环境输入信号映射为感知输出，该输出进而驱动执行响应，改变未来的感知输入。目前，双向信息传输的精度如何控制导航尚不明确。本文通过解析求解描述两类主要生物导航者（空间感知细胞和时间感知细胞）的两个通用模型，研究信息如何控制导航。我们发现，在浅梯度的线性响应区域，导航性能完全由双向信息传输的强度和时间尺度决定，与导航者的基本参数无明确依赖关系。我们将这些关系命名为“行为状态方程（BESTs）”：以与系统无关的方式将信息映射到功能的等式。BESTs预测，具有不同感知和执行参数的导航者的性能会出现可实验验证的数据坍缩。我们通过对大肠杆菌趋化性进行随机模拟来检验该理论的有效性，使用TE-PWS算法精确计算转移熵，观察到的性能符合BESTs，无需拟合或缩放参数。因此，我们的理论将感知与执行之间的双向信息传输确定为导航的组织原则。
英文摘要
A wide variety of biological functions are driven by feedback between sensing and actuation. A paradigmatic example is cellular navigation. During navigation, the sensory system maps the environmental input signal onto a sensory output, which then drives an actuation response, thereby changing the future sensory input. How the accuracy of this bidirectional information transmission controls navigation is not currently understood. Here, we study how information controls navigation by analytically solving two generic models that describe two major classes of biological navigators: spatial- and temporal-sensing cells. We find that, in the linear-response regime of shallow gradients, navigation performance is fully determined by bidirectional information transmission alone, as quantified by feedforward and feedback transfer entropies. Elementary system parameters affect navigation performance only through their effect on these information flows. We call these relations Behavioral Equations of State (BESTs): equalities that map information to function in a system-independent way. BESTs predict an experimentally testable data collapse for the performance of navigators with different sensing and actuation parameters. We test the validity of our theory by performing stochastic simulations of chemotaxis of the bacterium Escherichia coli, computing the relevant transfer entropies exactly with the TE-PWS algorithm. The observed performance obeys the BEST without any fitting or scaling parameters. Thus, our theory identifies bidirectional information transmission between sensing and actuation as an organizing principle for navigation.
Comments38 pages, 6 figures
