论文标题
随机感觉受体中多路复用的理论上限
Theoretical upper bound of multiplexing in stochastic sensory receptors
论文作者
论文摘要
在随机噪声中，生物感觉受体提供了微观尺度信息转导的极好例子。我们认为随机性并不总是对传感的阻碍。取而代之的是，它可以允许单个随机传感器执行多路复用：同时将多种类型的环境信息转换为下游感觉网络。通过在配体浴中的配体传感器的Langevin动力学模拟，我们证明了二元状态受体可以同时编码多个独立的环境变量，例如配体浓度和培养基流量的速度。我们发展了随机感觉多路复用的一般理论，并提出了两个理论上限。此外，我们猜想随机生成的传感器通常会使更紧密的上限饱和。这项研究中开发的理论框架涉及缺乏等级的最大似然分析（RD-MLE），它提供了一种系统的方法来全面评估传感器的感觉能力，而无需任何初始假设。这个理论框架可以激发更有效的人造传感器的设计。
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.
