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 affect chemotactic performance, especially when considering
that the swimming behavior of the cell determines the input signal. Using
analytical models and simulations that incorporate recent experimental
evidences about behavioral feedback and flagellar motor adaptation we identify
an operational regime of the pathway that maximizes drift velocity for various
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.Comment: Corrected one typo. First two authors contributed equally. Notably,
there were various typos in the values of the parameters in the model of
motor adaptation. The results remain unchange
