title: The value of monitoring to control evolving populations.
abstract: 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.
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom; and.
authors.forename: Andrej
authors.initials: A
authors.lastname: Fischer
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom; and Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, United Kingdom.
authors.forename: Ignacio
authors.identifier: http://orcid.org/0000-0003-0427-2639
authors.initials: I
authors.lastname: Vázquez-García
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom; and vm5@sanger.ac.uk.
authors.forename: Ville
authors.initials: V
authors.lastname: Mustonen
mesh_terms: D019008:Drug Resistance, Neoplasm; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism
keywords: adaptive cancer therapy; drug resistance; stochastic optimal control