211 research outputs found
Punishment in Public Goods games leads to meta-stable phase transitions and hysteresis
The evolution of cooperation has been a perennial problem in evolutionary
biology because cooperation can be undermined by selfish cheaters who gain an
advantage in the short run, while compromising the long-term viability of the
population. Evolutionary game theory has shown that under certain conditions,
cooperation nonetheless evolves stably, for example if players have the
opportunity to punish cheaters that benefit from a public good yet refuse to
pay into the common pool. However, punishment has remained enigmatic because it
is costly, and difficult to maintain. On the other hand, cooperation emerges
naturally in the Public Goods game if the synergy of the public good (the
factor multiplying the public good investment) is sufficiently high. In terms
of this synergy parameter, the transition from defection to cooperation can be
viewed as a phase transition with the synergy as the critical parameter. We
show here that punishment reduces the critical value at which cooperation
occurs, but also creates the possibility of meta-stable phase transitions,
where populations can "tunnel" into the cooperating phase below the critical
value. At the same time, cooperating populations are unstable even above the
critical value, because a group of defectors that are large enough can
"nucleate" such a transition. We study the mean-field theoretical predictions
via agent-based simulations of finite populations using an evolutionary
approach where the decisions to cooperate or to punish are encoded genetically
in terms of evolvable probabilities. We recover the theoretical predictions and
demonstrate that the population shows hysteresis, as expected in systems that
exhibit super-heating and super-cooling. We conclude that punishment can
stabilize populations of cooperators below the critical point, but it is a
two-edged sword: it can also stabilize defectors above the critical point.Comment: 22 pages, 9 figures. Slight title change, version that appears in
Physical Biolog
Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein
Epistatic interactions between residues determine a protein's adaptability
and shape its evolutionary trajectory. When a protein experiences a changed
environment, it is under strong selection to find a peak in the new fitness
landscape. It has been shown that strong selection increases epistatic
interactions as well as the ruggedness of the fitness landscape, but little is
known about how the epistatic interactions change under selection in the
long-term evolution of a protein. Here we analyze the evolution of epistasis in
the protease of the human immunodeficiency virus type 1 (HIV-1) using protease
sequences collected for almost a decade from both treated and untreated
patients, to understand how epistasis changes and how those changes impact the
long-term evolvability of a protein. We use an information-theoretic proxy for
epistasis that quantifies the co-variation between sites, and show that
positive information is a necessary (but not sufficient) condition that detects
epistasis in most cases. We analyze the "fossils" of the evolutionary
trajectories of the protein contained in the sequence data, and show that
epistasis continues to enrich under strong selection, but not for proteins
whose environment is unchanged. The increase in epistasis compensates for the
information loss due to sequence variability brought about by treatment, and
facilitates adaptation in the increasingly rugged fitness landscape of
treatment. While epistasis is thought to enhance evolvability via
valley-crossing early-on in adaptation, it can hinder adaptation later when the
landscape has turned rugged. However, we find no evidence that the HIV-1
protease has reached its potential for evolution after 9 years of adapting to a
drug environment that itself is constantly changing.Comment: 25 pages, 9 figures, plus Supplementary Material including
Supplementary Text S1-S7, Supplementary Tables S1-S2, and Supplementary
Figures S1-2. Version that appears in PLoS Genetic
Evolution of genetic organization in digital organisms
We examine the evolution of expression patterns and the organization of
genetic information in populations of self-replicating digital organisms.
Seeding the experiments with a linearly expressed ancestor, we witness the
development of complex, parallel secondary expression patterns. Using
principles from information theory, we demonstrate an evolutionary pressure
towards overlapping expressions causing variation (and hence further evolution)
to sharply drop. Finally, we compare the overlapping sections of dominant
genomes to those portions which are singly expressed and observe a significant
difference in the entropy of their encoding.Comment: 18 pages with 5 embedded figures. Proc. of DIMACS workshop on
"Evolution as Computation", Jan. 11-12, Princeton, NJ. L. Landweber and E.
Winfree, eds. (Springer, 1999
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