E. coli do not count single molecules.

Organisms must perform sensory-motor behaviors to survive. What bounds or constraints limit behavioral performance? Previously, we found that the gradient-climbing speed of a chemotaxing Escherichia coli is near a bound set by the limited information they acquire from their chemical environments (1). Here we ask what limits their sensory accuracy. Past theoretical analyses have shown that the stochasticity of single molecule arrivals sets a fundamental limit on the precision of chemical sensing (2). Although it has been argued that bacteria approach this limit, direct evidence is lacking. Here, using information theory and quantitative experiments, we find that E. coli's chemosensing is not limited by the physics of particle counting. First, we derive the physical limit on the behaviorally-relevant information that any sensor can get about a changing chemical concentration, assuming that every molecule arriving at the sensor is recorded. Then, we derive and measure how much information E. coli's signaling pathway encodes during chemotaxis. We find that E. coli encode two orders of magnitude less information than an ideal sensor limited only by shot noise in particle arrivals. These results strongly suggest that constraints other than particle arrival noise limit E. coli's sensory fidelity.

E. coli do not count single molecules.

Organisms must perform sensory-motor behaviors to survive. What bounds or constraints limit behavioral performance? Previously, we found that the gradient-climbing speed of a chemotaxing Escherichia coli is near a bound set by the limited information they acquire from their chemical environments. Here we ask what limits their sensory accuracy. Past theoretical analyses have shown that the stochasticity of single molecule arrivals sets a fundamental limit on the precision of chemical sensing. Although it has been argued that bacteria approach this limit, direct evidence is lacking. Here, using information theory and quantitative experiments, we find that E. coli's chemosensing is not limited by the physics of particle counting. First, we derive the physical limit on the behaviorally-relevant information that any sensor can get about a changing chemical concentration, assuming that every molecule arriving at the sensor is recorded. Then, we derive and measure how much information E. coli's signaling pathway encodes during chemotaxis. We find that E. coli encode two orders of magnitude less information than an ideal sensor limited only by shot noise in particle arrivals. These results strongly suggest that constraints other than particle arrival noise limit E. coli's sensory fidelity.

Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis.

In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal's ambient concentration increases. However, among competitive ligands, the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.

Sensory diversity and precise adaptation enable independent bet-hedging strategies for multiple signals at the same time.

While navigating their environments, cells encounter many different signals at once. In the face of uncertain conditions, diversifying the sensitivity to different signals across the population can be useful. Previous studies established that one of the simplest sensory systems, the chemotaxis network of Escherichia coli , can switch between a high diversity bet-hedging strategy, and a low diversity tracking strategy for a ligand as that ligand becomes prevalent. Here, we combine mathematical modeling and single-cell experiments to show that populations of chemotactic bacteria make this transition for each ligand independently. That is, transitioning to tracking one ligand does not compromise the population’s ability to hedge its bets across other future ligands. Remarkably, we found that this independence holds even if those ligands compete for receptor binding sites with the background ligand being tracked. The independence of this transition between two diversity regimes is explained by a simple allosteric model of chemoreceptor clusters with negative integral feedback, which accurately predicts the observed diversity in sensitivity under various background stimulus conditions. Our mathematical analysis shows that similar transitions from bet-hedging to tracking also arise in feed-forward network architectures capable of precise adaptation, suggesting that environment-dependent modulation of diversity may occur in many cell types.