title: Exploiting the Adaptation Dynamics to Predict the Distribution of Beneficial Fitness Effects.
abstract: Adaptation of asexual populations is driven by beneficial mutations and therefore the dynamics of this process, besides other factors, depends on the distribution of beneficial fitness effects. It is known that on uncorrelated fitness landscapes, this distribution can only be of three types: truncated, exponential and power law. We performed extensive stochastic simulations to study the adaptation dynamics on rugged fitness landscapes, and identified two quantities that can be used to distinguish the underlying distribution of beneficial fitness effects. The first quantity studied here is the fitness difference between successive mutations that spread in the population, which is found to decrease in the case of truncated distributions, remains nearly a constant for exponentially decaying distributions and increases when the fitness distribution decays as a power law. The second quantity of interest, namely, the rate of change of fitness with time also shows quantitatively different behaviour for different beneficial fitness distributions. The patterns displayed by the two aforementioned quantities are found to hold good for both low and high mutation rates. We discuss how these patterns can be exploited to determine the distribution of beneficial fitness effects in microbial experiments.
authors.affiliation: Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur P.O., Bangalore 560064, India.
authors.forename: Sona
authors.initials: S
authors.lastname: John
authors.affiliation: Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur P.O., Bangalore 560064, India.
authors.forename: Sarada
authors.initials: S
authors.lastname: Seetharaman
mesh_terms: D000222:Adaptation, Physiological; D000465:Algorithms; D056084:Genetic Fitness; D008962:Models, Theoretical; D009154:Mutation; D011157:Population Dynamics; D012100:Reproduction, Asexual; D013269:Stochastic Processes