# Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities

## TL;DR

This paper models quorum sensing crosstalk in heterogeneous microbial communities using neural networks to analyze how it influences collective decision-making capacity, revealing conditions that maximize information processing.

## Contribution

It introduces a neural network framework to map quorum sensing crosstalk, providing analytical insights into community decision-making and evolutionary diversification.

## Key findings

- Analytical formula for optimal number of variants maximizing capacity.
- Model matches experimental data on Staphylococcus aureus Agr system.
- Identifies critical parameters where community capacity peaks.

## Abstract

Microbial communities frequently communicate via quorum sensing (QS), where cells produce, secrete, and respond to a threshold level of an autoinducer (AI) molecule, thereby modulating gene expression. However, the biology of QS remains incompletely understood in heterogeneous communities, where variant bacterial strains possess distinct QS systems that produce chemically unique AIs. AI molecules bind to 'cognate' receptors, but also to 'non-cognate' receptors found in other strains, resulting in inter-strain crosstalk. Understanding these interactions is a prerequisite for deciphering the consequences of crosstalk in real ecosystems, where multiple AIs are regularly present in the same environment. As a step towards this goal, we map crosstalk in a heterogeneous community of variant QS strains onto an artificial neural network model. This formulation allows us to systematically analyze how crosstalk regulates the community's capacity for flexible decision making, as quantified by the Boltzmann entropy of all QS gene expression states of the system. In a mean-field limit of complete cross-inhibition between variant strains, the model is exactly solvable, allowing for an analytical formula for the number of variants that maximize capacity as a function of signal kinetics and activation parameters. An analysis of previous experimental results on the Staphylococcus aureus two-component Agr system indicates that the observed combination of variant numbers, gene expression rates and threshold concentrations lies near this critical regime of parameter space where capacity peaks. The results are suggestive of a potential evolutionary driving force for diversification in certain QS systems.