Cooperative interactions among sensory receptors provide a general mechanism to increase the sensitivity of signal transduction. In particular, bacterial chemotaxis receptors interact cooperatively to produce an ultrasensitive response to chemoeffector concentrations. However, cooperativity between receptors in large macromolecular complexes is necessarily based on local interactions and consequently is fundamentally connected to slowing of receptor conformational dynamics, which increases intrinsic noise. Therefore, it is not clear whether or under what conditions cooperativity actually increases the precision of the concentration measurement. We explictly calculate the signal-to-noise ratio (SNR) for sensing a concentration change using a simple, Ising-type model of receptor-receptor interactions, generalized via scaling arguments, and find that the optimal SNR is always achieved by independent receptors.
General Chemical Reaction Network Theory for Olfactory Sensing Based on G-Protein-Coupled Receptors : Elucidation of Odorant Mixture Effects and Agonist-Synergist Threshold
Limits of sensing temporal concentration changes by single cells
Berg和Purcell [Biophys. J. 20，193（1977）]计算了单细胞生物体的浓度传感的准确性如何受到来自少量计数分子的噪声的限制。在这里，我们将他们的结果推广到浓度斜坡的检测，这通常是生物学相关的情况（例如，在细菌趋化过程中）。我们计算了三个测量设备的斜坡传感的不确定性的下限：一个单一的受体，一个吸收球，和一个监测领域。我们对比两种策略，简单的线性回归的输入信号与最大似然估计，并表明，后者可以是前者的两倍准确。最后，我们考虑这两种策略的生物实现，并确定可能的签名，最大似然估计是由真实的生物系统实现的。
Information processing and signal integration in bacterial quorum sensing
细菌通过分泌的化学信号分子进行交流，这种分子被称为“群体感应”。海洋细菌{ \it Vibrio harveyi}的群体感应网络采用三种自诱导物，每种已知编码不同的生态信息。然而，细胞如何整合和解释包含在三个自诱导信号中的信息仍然是一个谜。在这里，我们开发了一个基于信息论的分析信号集成的新框架，并使用它来分析{ \it V. harveyi}中的群体感应。我们量化多少细胞可以了解个人的自诱导和解释实验观察到的{ \it V. harveyi}群体感应电路的输入输出关系。我们的研究结果表明，需要限制输入信号之间的干扰对细菌信号整合网络的架构有很强的约束，并且细菌可能已经进化出主动策略来最大限度地减少这种干扰。在这里，我们分析了两个这样的策略：操纵的autoinducer生产和反馈受体的数量比例。
