John Sona, Seetharaman Sarada
Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur P.O., Bangalore 560064, India.
PLoS One. 2016 Mar 18;11(3):e0151795. doi: 10.1371/journal.pone.0151795. eCollection 2016.
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.
无性种群的适应性由有益突变驱动,因此,除其他因素外,这一过程的动态取决于有益适合度效应的分布。众所周知,在不相关的适合度景观上,这种分布只能有三种类型:截断型、指数型和幂律型。我们进行了广泛的随机模拟,以研究崎岖适合度景观上的适应动态,并确定了两个可用于区分有益适合度效应潜在分布的量。这里研究的第一个量是在种群中传播的连续突变之间的适合度差异,发现在截断分布的情况下它会减小,对于指数衰减分布它几乎保持不变,而当适合度分布按幂律衰减时它会增加。第二个感兴趣的量,即适合度随时间的变化率,对于不同的有益适合度分布也显示出定量上不同的行为。发现上述两个量所显示的模式在低突变率和高突变率情况下都成立。我们讨论了如何利用这些模式来确定微生物实验中有益适合度效应的分布。