Computing Extinction Barriers in a Quorum-Sensing Reaction Network

arXiv:2609.39946 · math.PR, cond-mat.stat-mech, cs.NA, math.NA, q-bio.PE · Submitted 2026-09-30 · Read on arXiv

math.PR, cond-mat.stat-mech, cs.NA, math.NA, q-bio.PE

Submitted: 2026-09-30

Updated: 2026-09-30

Comments: 26 pages, 5 figures, 3 tables. Submitted to the MMSC 2026 proceedings (EPiC Series in Computing). Code and data: https://doi.org/10.5281/zenodo.23062342

Code: https://github.com/marioayala15/qs-extinction-barriers

License: http://creativecommons.org/licenses/by/4.0/

The gist: We introduce a simple reaction-network model of a quorum-sensing population that couples the cell density x to the signal density w.

Terminology

Abstract

We introduce a simple reaction-network model of a quorum-sensing population that couples the cell density x to the signal density w. In the four-channel cell-signal network studied here, scaling signal production and removal by the same factor r leaves all deterministic equilibria, and their stability types, unchanged. Nevertheless, we show that r shifts the quasipotential barrier ΔV(r) for rare transitions towards extinction, and thus, under metastable exit assumptions, the mean time to reach a fixed neighbourhood of the extinction state on the exponential scale e NΔV(r). We compute the barrier by minimization of the path action with the signal retained as a fluctuating coordinate, and we compare it with exact stochastic simulation of population-threshold crossing times regressed in N. Over a range of r, the minimum-action barriers satisfy ΔV(r)=ΔV infinity+O(1/r), where ΔV infinity is obtained by eliminating the signal first. At r=1, the barrier is 55% larger than ΔV infinity. The saddle barrier ΔV(r) is also the least action needed to enter the basin of extinction, but the density threshold can be crossed more cheaply: at r=0.5 the cheapest crossing costs 7.7% less action, keeps the signal high, and is usually followed by recovery. Simulated arrival times in this neighbourhood, which include failed attempts, grow with slopes within two fitted standard errors of the saddle barrier at every tested rate, and within 0.004 of it if the logarithmic prefactor term is omitted. Under either regression model they exclude ΔV infinity at r 2 by at least 4.4 fitted standard errors, without using the action solver.

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