Stochastically perturbed model of cell electropermeabilization

TL;DR

This paper introduces a stochastic version of a cell electroporation model, incorporating noise to account for uncertainties, and proves the existence and uniqueness of solutions while providing numerical simulations.

Contribution

It extends a deterministic electroporation model to include stochastic effects, establishing mathematical well-posedness and exploring numerical behavior under noise.

Findings

Existence and uniqueness of solutions for the stochastic model.

Numerical simulations suggest the presence of invariant measures.

The model accounts for random effects like temperature fluctuations.

Abstract

Reversible electropermeabilization, commonly referred to as electroporation, is a transient increase in cell membrane permeability induced by short, high-voltage electric pulses. We present a stochastically perturbed version of a phenomenological electroporation model introduced in the deterministic setting by \cite{kavian2014classical}. The deterministic model couples the electrostatic equations for the electric potential in the extra- and intracellular domains with a nonlinear evolution law for the transmembrane potential jump, itself coupled to an ordinary differential equation describing the porosity degree of the membrane. To account for various random effects, such as temperature fluctuations or uncerntainty in the applied electric field, we add noise on the cell membrane. We establish the existence and uniqueness of a variational solution to the resulting coupled SPDE-ODE system governing the membrane potential and the degree of porosity, where the stochastic perturbation is multiplicative and degenerate, acting only on the SPDE component of the coupled SPDE-ODE system. Any mixing in the ODE variables is therefore induced indirectly through the nonlinear coupling in the drift. The main technical challenge arises from the nonlinearities, which are neither Lipschitz continuous nor monotone. The result is proved by means of Galerkin method, following the methodology by Liu and R\"ockner \cite{liu2015stochastic} for treating equations under generalized monotonicity and coercivity conditions. Finally, we present numerical simulations of the solution and its time averages for both additive and multiplicative noise, that provide a numerical indication for existence of invariant measure.