E. colichemotaxis is information-limited

Abstract

Organisms must acquire and use environmental information to guide their behaviors. However, it is unclear whether and how information quantitatively limits behavioral performance. Here, we relate information to behavioral performance in Escherichia coli chemotaxis. First, we derive a theoretical limit for the maximum achievable gradient-climbing speed given a cell's information acquisition rate. Next, we measure cells' gradient-climbing speeds and the rate of information acquisition by the chemotaxis pathway. We find that E. coli make behavioral decisions with much less than the 1 bit required to determine whether they are swimming up-gradient. However, they use this information efficiently, performing near the theoretical limit. Thus, information can limit organisms' performance, and sensory-motor pathways may have evolved to efficiently use information from the environment.

A mechanism for migrating bacterial populations to non-genetically adapt to new environments

“…We investigated the effects of diversity in tumble bias, but bacteria also exhibit diversity in other phenotypic traits. These include, but are not limited to (71), swimming speed (23,52), receptor kinase adaptation time (52,72,73), receptor array composition (73)(74)(75)(76)(77)(78), and attractant consumption rate. By influencing cells' chemotaxis performance or the attractant gradient, these phenotypes can also become spatially sorted within the migrating group, and their organization can change in different physical and chemical environments.…”

“…Here, the persistence of tumbles determines how much a tumble reorients the cell, and is the rotational diffusion coefficient. The prefactor arises from the effects of rotational diffusion on chemotaxis (46,(49)(50)(51)(52), which has a substantial effect in liquid. This form is roughly justified because both ( ) and ( ) arise from cells' run-and-tumble motility (14,15,53).…”

Non-Genetic Diversity in Chemosensing and Chemotactic Behavior

“…Recently, the wide availability of high-sensitivity cameras allowed the classic CheZ/CheY FRET method [59]-originally developed to measure kinase activity in a population of cells-to be adapted for quantifying kinase activity in single cells [36,[60][61][62] (Figure 1f). Measurements of the kinase activity in individual unstimulated cells revealed large fluctuations in the kinase activity that could be attributed to two different sources.…”

“…However, the same experiments revealed that E. coli used that little information efficiently, achieving drift speed up the gradient around 70% of the theoretical maximum The multi-task nature of the chemotaxis signaling network raises the question of whether the system still effectively operates as an information-processing system in biasing run-and-tumble behavior. Combining theory and quantitative measurements, Mattingly and Kamino et al measured information-theoretic properties of the E. coli chemotaxis pathway for cells climbing shallow gradients [62]. Due to the presence of the signaling fluctuations, the amount of information E. coli acquired from the environment during chemotaxis was quite low-on the order of 0.01 bits/s in a shallow gradient where chemoattractant concentration varies on centimeter scale.…”

Optimal sensing and control of run-and-tumble chemotaxis

“…One uses optimal control models, which describe the optimal strategy to modulate the tumble rate based on sensing signals. This approach formulates the tumble rate as a functional of sensing signals and considers the optimization of the functional with respect to chemotactic performance such as gradient climbing [22][23][24][25]. The other uses optimal filtering models.…”