Optimal phenotypic adaptation in fluctuating environments.


Journal

Biophysical journal
ISSN: 1542-0086
Titre abrégé: Biophys J
Pays: United States
ID NLM: 0370626

Informations de publication

Date de publication:
21 Nov 2023
Historique:
received: 05 04 2023
revised: 28 08 2023
accepted: 18 10 2023
pmc-release: 21 11 2024
medline: 24 11 2023
pubmed: 25 10 2023
entrez: 25 10 2023
Statut: ppublish

Résumé

Phenotypic adaptation is a universal feature of biological systems navigating highly variable environments. Recent empirical data support the role of memory-driven decision making in cellular systems navigating uncertain future nutrient landscapes, wherein a distinct growth phenotype emerges in fluctuating conditions. We develop a simple stochastic mathematical model to describe memory-driven cellular adaptation required for systems to optimally navigate such uncertainty. In this framework, adaptive populations traverse dynamic environments by inferring future variation from a memory of prior states, and memory capacity imposes a fundamental trade-off between the speed and accuracy of adaptation to new fluctuating environments. Our results suggest that the observed growth reductions that occur in fluctuating environments are a direct consequence of optimal decision making and result from bet hedging and occasional phenotypic-environmental mismatch. We anticipate that this modeling framework will be useful for studying the role of memory in phenotypic adaptation, including in the design of temporally varying therapies against adaptive systems.

Identifiants

pubmed: 37876159
pii: S0006-3495(23)00655-0
doi: 10.1016/j.bpj.2023.10.019
pmc: PMC10698328
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

4414-4424

Informations de copyright

Copyright © 2023 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The author declares no competing interests.

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Auteurs

Jason T George (JT)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas; Engineering Medicine Program, Texas A&M University, Houston, Texas; Center for Theoretical Biological Physics, Rice University, Houston, Texas. Electronic address: jason.george@tamu.edu.

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Classifications MeSH