The value of monitoring to control evolving populations

TL;DR

This paper explores how stochastic optimal control and high-resolution monitoring can be used to manage evolving populations, such as drug-resistant cancer cells, to improve treatment outcomes.

Contribution

It introduces a mathematical framework for adaptive control of evolving populations, demonstrating its effectiveness in maintaining polymorphisms and managing drug resistance.

Findings

Adaptive therapies outperform traditional ones in controlling tumor growth.

High-resolution monitoring is crucial for effective population control.

Mathematical models can guide treatment strategies for evolving diseases.

Abstract

Populations can evolve in order to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has quickly become a key challenge for global health. Therefore, ideas of how to control evolving populations in order to overcome this threat are valuable. Here we use the mathematical concepts of stochastic optimal control to study what is needed to control evolving populations. Following established routes to calculate control strategies, we first study how a polymorphism can be maintained in a finite population by adaptively tuning selection. We then introduce a minimal model of drug resistance in a stochastically evolving cancer cell population and compute adaptive therapies, where decisions are based on monitoring the response of the tumor, which can outperform established therapy paradigms. For both case studies, we demonstrate the importance of high-resolution monitoring of the target population in order to achieve a given control objective: to control one must monitor.