Predicting chemical environments of bacteria from receptor signaling

TL;DR

This paper uses experimental data and information theory to predict the distribution of chemical gradients bacteria encounter, enhancing understanding of their sensory environments and signaling limitations.

Contribution

It introduces a method combining dose-response data, generalized Weber laws, and information theory to predict bacterial chemical environments and gradient distributions.

Findings

Broad exponential gradient distributions lead to log-normal stimuli.

Bacterial signaling is limited by external and internal noise.

Predicted gradient distributions help understand complex microenvironments.

Abstract

Sensory systems have evolved to respond to input stimuli of certain statistical properties, and to reliably transmit this information through biochemical pathways. Hence, for an experimentally well-characterized sensory system, one ought to be able to extract valuable information about the statistics of the stimuli. Based on dose-response curves from in vivo fluorescence resonance energy transfer (FRET) experiments of the bacterial chemotaxis sensory system, we predict the chemical gradients chemotactic Escherichia coli cells typically encounter in their natural environment. To predict average gradients cells experience, we revaluate the phenomenological Weber's law and its generalizations to the Weber-Fechner law and fold-change detection. To obtain full distributions of gradients we use information theory and simulations, considering limitations of information transmission from both cell-external and internal noise. We identify broad distributions of exponential gradients, which lead to log-normal stimuli and maximal drift velocity. Our results thus provide a first step towards deciphering the chemical nature of complex, experimentally inaccessible cellular microenvironments, such as the human intestine.