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  • Open Access

Phenotypic Heterogeneity in Lag Reflects an Evolutionarily Stable Bet-Hedging Strategy

Abir George1,2, Ned S. Wingreen1,3,*, and Gautam Reddy2,†

  • *Contact author: wingreen@princeton.edu
  • †Contact author: greddy@princeton.edu
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PRX Life 4, 033003 – Published 9 July, 2026

DOI: https://doi.org/10.1103/rt5t-qdr8

Abstract

Single-cell experiments in yeast reveal two distinct heritable phenotypes—‘‘arresters’’ and ‘‘recoverers’’—when a clonal population experiences a negative shift in its growth environment. Recoverers exhibit a variable yet finite lag before resuming growth in the new environment, whereas arresters remain in a nongrowing, arrested state until more favorable conditions return. Although the diversification of individual cells into arresters and recoverers is a robust phenomenon, it remains unclear whether this coexistence constitutes an evolutionarily stable strategy. Here, we demonstrate that a heterogeneous strategy composed of both arrester and recoverer phenotypes maximizes long-term population fitness across a broad spectrum of growth-lag trade-offs. Our analysis employs a dynamic programming framework to identify the fitness-maximizing distribution of phenotypes for populations that stochastically switch between preferred and nonpreferred environments. We propose a minimal model incorporating metabolism, growth, and enzyme allocation to explain the physiological origin of a power-law growth-lag trade-off that favors phenotypic heterogeneity. The theory predicts a nontrivial relationship between the fraction of recoverers and their lag time, which aligns with existing data from wild yeast strains, evolved isolates, and variations in preshift growth conditions. This relationship suggests an evolutionary ‘‘rheostat’’-like mechanism that enables populations to rapidly adapt to changing environmental conditions.

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PRX Life 4, 033003– Published 9 July, 2026
  • Received 11 November 2025
  • Accepted 12 June 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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