随机感觉感受器中多路复用的理论上限
Theoretical upper bound of multiplexing in stochastic sensory receptors
AI总结：
该研究通过 Langevin 模拟和 rd-MLE 框架证明随机双态受体可同时编码多种环境变量，并给出随机感觉多路复用的理论上限。
摘要：
生物感觉感受器为在随机噪声中进行微观尺度信息转导提供了极佳范例。我们认为，随机性并不总是感知的障碍；相反，它可以使单个随机传感器实现多路复用：同时将多种类型的环境信息转导给下游感觉网络。通过对配体浴中的配体-受体传感器进行 Langevin 动力学模拟，我们证明双态受体能够同时编码多个独立的环境变量，例如配体浓度和介质流动速度。我们发展了随机感觉多路复用的一般理论，并提出两个理论上限。此外，我们推测随机生成的传感器通常会达到更严格的上限。本研究建立的理论框架涉及秩亏最大似然分析（rd-MLE），提供了一种无需任何初始假设即可全面评估传感器感觉能力的系统方法。该理论框架可启发设计更高效的人工传感器。
英文摘要：
Biological sensory receptors provide excellent examples of microscopic scale information transduction amidst stochastic noise. We argue that stochasticity is not always a hindrance to sensing. Instead, it could allow a single stochastic sensor to perform multiplexing: simultaneously transducing multiple types of environmental information to the downstream sensory network. Through a Langevin dynamics simulation of a ligand-receptor sensor in a bath of ligands, we demonstrate that a binary-state receptor can simultaneously encode multiple independent environmental variables, such as ligand concentration and the speed of media flow. We develop a general theory of stochastic sensory multiplexing and suggest two theoretical upper bounds. Furthermore, we conjecture that randomly generated sensors typically saturate the tighter upper bound. The theoretical framework developed in this study, which involves a rank-deficient maximum likelihood analysis (rd-MLE), provides a systematic approach to comprehensively assess a sensor's sensory ability without any initial assumptions. This theoretical framework can inspire the design of more efficient artificial sensors.
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