0000000000343224
AUTHOR
Lin Chao
Prisoner's dilemma in an RNA virus
The evolution of competitive interactions among viruses1 was studied in the RNA phage φ6 at high and low multiplicities of infection (that is, at high and low ratios of infecting phage to host cells). At high multiplicities, many phage infect and reproduce in the same host cell, whereas at low multiplicities the viruses reproduce mainly as clones. An unexpected result of this study1 was that phage grown at high rates of co-infection increased in fitness initially, but then evolved lowered fitness. Here we show that the fitness of the high-multiplicity phage relative to their ancestors generates a pay-off matrix conforming to the prisoner's dilemma strategy of game theory2,3. In this strateg…
Nonlinear trade-offs allow the cooperation game to evolve from Prisoner's Dilemma to Snowdrift.
[EN] The existence of cooperation, or the production of public goods, is an evolutionary problem. Cooperation is not favoured because the Prisoner s Dilemma (PD) game drives cooperators to extinction. We have re-analysed this problem by using RNA viruses to motivate a model for the evolution of cooperation. Gene products are the public goods and group size is the number of virions co-infecting the same host cell. Our results show that if the trade-off between replication and production of gene products is linear, PD is observed. However, if the trade-off is nonlinear, the viruses evolve into separate lineages of ultra-defectors and ultra-cooperators as group size is increased. The nonlinear…
Figure S1 from Nonlinear trade-offs allow the cooperation game to evolve from prisoner's dilemma to snow drift
Comparing Monte Carlo and numerical solutions for evolved values of i. Solutions obtained as described in Materials and Methods. Individual points represent i values obtained by the Monte Carlo (MC) and numerical solutions. MC simulations were run for 10,000 generations with population size of N = 500m, where m is group size, and a mutation rate of u = 0.2 and a Gaussian distribution of mutational effects with mean zero and standard deviation = 0.005. Populations generally reach equilibrium values after 1000 generations (figure 2A). Reported values of i are the mean value at the last generation. The parameter space represented in figures 4A-D was explored by letting m range from 2, 3, 4, …,…