A stochastic model for bacteriophage therapies

TL;DR

This paper develops a stochastic model for bacteriophage therapy, demonstrating that in low-noise conditions, the system reliably approaches a biologically relevant equilibrium with high probability, using advanced mathematical techniques.

Contribution

It introduces a stochastic framework for bacteriophage treatment modeling and analyzes its behavior under small noise conditions, providing probabilistic guarantees.

Findings

System remains close to equilibrium in low-noise regime

High probability of reaching biologically relevant states

Uses concentration techniques for delayed stochastic differential equations

Abstract

In this article, we analyze a system modeling bacteriophage treatments for infections in a noisy context. In the small noise regime, we show that after a reasonable amount of time the system is close to a sane equilibrium (which is a relevant biologic information) with high probability. Mathematically speaking, our study hinges on concentration techniques for delayed stochastic differential equations.