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We analyze a four-channel cell-signal quorum-sensing reaction network, demonstrating that scaling signal production and removal by a factor $r$ alters the quasipotential barrier for rare extinction transitions, even though deterministic equilibria remain invariant. The quasipotential barrier $\Delta V(r)$ is computed via path action minimization with the signal as a fluctuating coordinate, revealing a $\Delta V(r)=\Delta V_\infty+O(1/r)$ scaling and a significant increase (55%) at $r=1$ compared to signal elimination. Stochastic simulations confirm that simulated arrival times align with the computed saddle barrier, excluding the signal-eliminated limit for $r \le 2$.
Scaling signal dynamics in quorum sensing dramatically increases extinction barriers, with a 55% rise at $r=1$ compared to signal elimination, challenging assumptions about invariant stability under such perturbations.
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 $\Delta V(r)$ 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 $e^{N\Delta V(r)}$. 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 $\Delta V(r)=\Delta V_\infty+O(1/r)$, where $\Delta V_\infty$ is obtained by eliminating the signal first. At $r=1$, the barrier is 55% larger than $\Delta V_\infty$. The saddle barrier $\Delta V(r)$ 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 $\Delta V_\infty$ at $r\le2$ by at least $4.4$ fitted standard errors, without using the action solver.