Evolution of stalk/spore ratio in a social amoeba: Cell-to-cell interaction via a signaling chemical shaped by cheating risk

Kouki Uchinomiya, Yoh Iwasa

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


The social amoeba (or cellular slime mold) is a model system for cell cooperation. When food is depleted in the environment, cells aggregate together. Some of these cells become stalks, raising spores to aid in their dispersal. Differentiation-inducing factor-1 (DIF-1) is a signaling chemical produced by prespore cells and decomposed by prestalk cells. It affects the rate of switching between prestalk and prespore cells, thereby achieving a stable stalk/spore ratio. In this study we analyzed the evolution of the stalk/spore ratio. Strains may differ in the production and decomposition rates of the signaling chemical, and in the sensitivity of cells to switch in response to the signaling chemical exposure. When two strains with the same stalk/spore ratio within their own fruiting body are combined into a single fruiting body, one strain may develop into prespores to a greater degree than the other. Direct evolutionary simulations and quantitative genetic dynamics demonstrate that if a fruiting body is always formed by a single strain, the cells evolve to produce less signaling chemical and become more sensitive to the signaling chemical due to the cost of producing the chemical. In contrast, if a fruiting body is formed by multiple strains, the cells evolve to become less sensitive to the signaling chemical and produce more signaling chemical in order to reduce the risk of being exploited. In contrast, the stalk-spore ratio is less likely to be affected by small cheating risk.

Original languageEnglish
Pages (from-to)110-118
Number of pages9
JournalJournal of Theoretical Biology
Publication statusPublished - Nov 7 2013

All Science Journal Classification (ASJC) codes

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • General Biochemistry,Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Statistics and Probability
  • Modelling and Simulation


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