Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America.
Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America; Department of Physics, Yale University, New Haven, Connecticut, United States of America.
PLoS Comput Biol. 2014 Jun 26;10(6):e1003694. doi: 10.1371/journal.pcbi.1003694. eCollection 2014 Jun.
Inputs to signaling pathways can have complex statistics that depend on the environment and on the behavioral response to previous stimuli. Such behavioral feedback is particularly important in navigation. Successful navigation relies on proper coupling between sensors, which gather information during motion, and actuators, which control behavior. Because reorientation conditions future inputs, behavioral feedback can place sensors and actuators in an operational regime different from the resting state. How then can organisms maintain proper information transfer through the pathway while navigating diverse environments? In bacterial chemotaxis, robust performance is often attributed to the zero integral feedback control of the sensor, which guarantees that activity returns to resting state when the input remains constant. While this property provides sensitivity over a wide range of signal intensities, it remains unclear how other parameters such as adaptation rate and adapted activity affect chemotactic performance, especially when considering that the swimming behavior of the cell determines the input signal. We examine this issue using analytical models and simulations that incorporate recent experimental evidences about behavioral feedback and flagellar motor adaptation. By focusing on how sensory information carried by the response regulator is best utilized by the motor, we identify an operational regime that maximizes drift velocity along chemical concentration gradients for a wide range of environments and sensor adaptation rates. This optimal regime is outside the dynamic range of the motor response, but maximizes the contrast between run duration up and down gradients. In steep gradients, the feedback from chemotactic drift can push the system through a bifurcation. This creates a non-chemotactic state that traps cells unless the motor is allowed to adapt. Although motor adaptation helps, we find that as the strength of the feedback increases individual phenotypes cannot maintain the optimal operational regime in all environments, suggesting that diversity could be beneficial.
信号通路的输入可能具有复杂的统计特性，这些特性取决于环境和对先前刺激的行为反应。这种行为反馈在导航中尤为重要。成功的导航依赖于传感器和执行器之间的正确耦合，传感器在运动过程中收集信息，执行器控制行为。由于重新定向条件会影响未来的输入，因此行为反馈可以使传感器和执行器处于与静止状态不同的工作状态。那么，生物体在导航不同环境时如何保持通过该途径的适当信息传递呢？在细菌趋化性中，稳健的性能通常归因于传感器的零积分反馈控制，该控制确保当输入保持不变时，活动会返回到静止状态。虽然这种特性在广泛的信号强度范围内提供了敏感性，但仍不清楚其他参数（如适应率和适应活性）如何影响趋化性能，特别是当考虑到细胞的游动行为决定输入信号时。我们使用包含有关行为反馈和鞭毛马达适应的最新实验证据的分析模型和模拟来研究这个问题。通过关注响应调节剂携带的感官信息如何被马达最佳利用，我们确定了一种工作状态，该状态在广泛的环境和传感器适应率下，可使化学浓度梯度上的漂移速度最大化。该最佳状态超出了马达响应的动态范围，但最大限度地提高了向上和向下梯度的运行时间之间的对比度。在陡峭的梯度中，趋化漂移的反馈可以推动系统通过分岔。这会产生一种非趋化状态，除非允许马达适应，否则会困住细胞。尽管马达适应有所帮助，但我们发现，随着反馈强度的增加，个体表型无法在所有环境中保持最佳工作状态，这表明多样性可能是有益的。
