Apparent Selection Pressure for Channel Capacity in Bacterial Chemotactic Sensors

TL;DR

The paper investigates whether single-cell bacterial chemotaxis sensors maximize information transfer. Using a heterogeneous Monod-Wyman-Changeux model for mixed Tar/Tsr receptors and a seven-parameter parameter sweep, it computes channel capacity $C$, dynamic range DR, and effective Hill coefficient $n_{ m eff}$, finding robust local maxima in $C$ across strains, while $n_{ m eff}$ and DR do not reach analogous optimization. The capacity-achieving input distribution $p^*(c)$ is bimodal, favoring both low and high ligand concentrations, implying that cells maximize information by sampling across regimes. These results suggest evolutionary pressure on channel capacity in receptor clusters and provide testable predictions about input distributions in natural environments, with broader implications for information processing in sensory systems.

Abstract

Bacterial chemotactic sensing converts noisy chemical signals into running and tumbling. We analyze the static sensing limits of mixed Tar/Tsr chemoreceptor clusters in individual Escherichia coli cells using a heterogeneous Monod-Wyman-Changeux (MWC) model. By sweeping a seven-dimensional parameter space, we compute three sensing performance metrics-channel capacity, effective Hill coefficient, and dynamic range. Across E. coli-like parameter regimes, we consistently observe pronounced local maxima of channel capacity, whereas neither the effective Hill coefficient nor the dynamic range exhibit comparable optimization. The capacity-achieving input distribution is bimodal, which implies that individual cells maximize information by sampling both low- and high concentration regimes. Together, these results suggest that, at the individual-cell level, channel capacity may be selected for in E. coli receptor clusters.

Figures (7)

• Figure 1: Schematic demonstrating the run--tumble behavior of E. coli.
• Figure 2: Schematic demonstrating bacterial chemotactic behavior and receptor response. (a) In a spatially uniform environment (no attractant or repellent), the run--tumble trajectory is an unbiased random walk with no net drift. (b) Positive chemotaxis in an attractant gradient: run segments are longer when the cell swims up the gradient and shorter when it swims down, yielding a net displacement toward higher concentration. Bar indicates increasing concentration. (c) Negative chemotaxis in a repellent gradient: more frequent reorientation when moving up-gradient biases motion toward lower concentration.
• Figure 3: Probability of the active state of a Tar/Tsr receptor cluster for the attractant MeAsp for ten different strains. Normalized activity decreases monotonically with MeAsp concentration on a log scale because ligand stabilizes the inactive state.
• Figure 4: Optimal use of the bacterial chemotactic receptors for all strains using parameters in Table \ref{['data']}. At top, capacity-achieving input distribution $p^*(c)$ versus ligand concentration $c$ as obtained by the Blahut-Arimoto algorithm. At bottom, optimal output distribution $p^*(s)$ among the active and inactive state for all strains.
• Figure 5: Channel capacity versus $K_d^{(A),1}$ for each of the ten strains holding all other parameters fixed.