The value of monitoring to control evolving populations

Significance Evolution of drug resistance, as observed in bacteria, viruses, parasites, and cancer, is a key challenge for global health. We approach the problem using the mathematical concepts of stochastic optimal control to study what is needed to control evolving populations. We focus on the detrimental effect of imperfect information and the loss of control it entails, thus quantifying the intuition that to control, one must monitor. We apply these concepts to cancer therapy to derive protocols where decisions are based on monitoring the response of the tumor, which can outperform established therapy paradigms. Populations can evolve to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has become a key challenge for global health. Therefore, ideas of how to control evolving populations 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. When decisions are in this manner based on monitoring the response of the tumor, this can outperform established therapy paradigms. For both case studies, we demonstrate the importance of high-resolution monitoring of the target population to achieve a given control objective, thus quantifying the intuition that to control, one must monitor.

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