Evolution of the rate of biological aging using a phenotype based computational model.
In this work I introduce a simple model to study how natural selection acts upon aging, which focuses on the viability of each individual. It is able to reproduce the Gompertz law of mortality and can make predictions about the relation between the level of mutation rates (beneficial/deleterious/neutral), age at reproductive maturity and the degree of biological aging. With no mutations, a population with low age at reproductive maturity R stabilizes at higher density values, while with mutations it reaches its maximum density, because even for large pre-reproductive periods each individual evolves to survive to maturity. Species with very short pre-reproductive periods can only tolerate a small number of detrimental mutations. The probabilities of detrimental (P(d)) or beneficial (P(b)) mutations are demonstrated to greatly affect the process. High absolute values produce peaks in the viability of the population over time. Mutations combined with low selection pressure move the system towards weaker phenotypes. For low values in the ratio P(d)/P(b), the speed at which aging occurs is almost independent of R, while higher values favor significantly species with high R. The value of R is critical to whether the population survives or dies out. The aging rate is controlled by P(d) and P(b) and the amount of the viability of each individual is modified, with neutral mutations allowing the system more "room" to evolve. The process of aging in this simple model is revealed to be fairly complex, yielding a rich variety of results.
Kittas
Aristotelis
A
Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. akittas@gmail.com
D000375:Aging / Q000235:genetics / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D005075:Biological Evolution; D003198:Computer Simulation; D008954:Models, Biological; D009154:Mutation; D010641:Phenotype; D011157:Population Dynamics; D012098:Reproduction / Q000235:genetics / Q000502:physiology; D012641:Selection, Genetic