# Statistics of correlated percolation in a bacterial community

## TL;DR

This study investigates how correlated signaling in Bacillus subtilis biofilms affects percolation theory's applicability, revealing that despite violations of independence and constant signaling fraction, the theory remains valid due to renormalization effects.

## Contribution

The paper introduces a mechanistic model explaining correlated cell signaling and its impact on percolation statistics in bacterial communities, extending traditional percolation theory.

## Key findings

- Correlated signaling does not significantly alter spatial statistics.
- Variability in signaling fraction broadens the percolation threshold range.
- Model validation with mutant biofilm confirms theoretical predictions.

## Abstract

Signal propagation over long distances is a ubiquitous feature of multicellular communities. In biofilms of the bacterium Bacillus subtilis, we recently discovered that some, but not all, cells participate in the propagation of an electrical signal, and the ones that do are organized in a way that is statistically consistent with percolation theory. However, two key assumptions of percolation theory are violated in this system. First, we find here that the probability for a cell to signal is not independent from other cells but instead is correlated with its nearby neighbors. We develop a mechanistic model, in which correlated signaling emerges from cell division, phenotypic inheritance, and cell displacement, that reproduces the experimental results. Second, we observed previously that the fraction of signaling cells is not constant but instead varies from biofilm to biofilm. We use our model to understand why percolation theory remains a valid description of the system despite these two violations of its assumptions. We find that the first violation does not significantly affect the spatial statistics, which we rationalize using a renormalization argument. We find that the second violation widens the range of signaling fractions around the percolation threshold at which one observes the characteristic power-law statistics of cluster sizes, consistent with our previous experimental results. We validate our model using a mutant biofilm whose signaling probability decays along the propagation direction. Our results identify key statistical features of a correlated percolating system and demonstrate their functional utility for a multicellular community.