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Title: Well-temperate phage: Optimal bet-hedging against local environmental collapses

Upon infection of their bacterial hosts temperate phages must chose between lysogenic and lytic developmental strategies. Here we apply the game-theoretic bet-hedging strategy introduced by Kelly to derive the optimal lysogenic fraction of the total population of phages as a function of frequency and intensity of environmental downturns affecting the lytic subpopulation. “Well-temperate” phage from our title is characterized by the best long-term population growth rate. We show that it is realized when the lysogenization frequency is approximately equal to the probability of lytic population collapse. We further predict the existence of sharp boundaries in system’s environmental, ecological, and biophysical parameters separating the regions where this temperate strategy is optimal from those dominated by purely virulent or dormant (purely lysogenic) strategies. We show that the virulent strategy works best for phages with large diversity of hosts, and access to multiple independent environments reachable by diffusion. Conversely, progressively more temperate or even dormant strategies are favored in the environments, that are subject to frequent and severe temporal downturns.
Authors:
 [1] ;  [2]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Univ. of Copenhagen, Copenhagen (Denmark)
Publication Date:
OSTI Identifier:
1213376
Report Number(s):
BNL--108254-2015-JA
Journal ID: ISSN 2045-2322; R&D Project: PM-031; KP1601040
Grant/Contract Number:
SC00112704
Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 5; Journal Issue: 8; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES computational models; information theory and computation