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引用次数: 0
摘要
Ben-Ari和Schinazi (J Stat Phys 162:415-425, 2016)引入了一个随机模型来研究“具有高突变率的病毒样进化种群”。该模型是一个生灭模型,其中个体在出生时要么是具有随机适应度参数在[0,1]中的突变体,要么具有均匀概率的现有适应度参数之一;而死亡事件会移除适应度最低的整个种群。我们将其改变为“适者生存”的概念,通过要求非突变个体在出生时具有根据优先依恋机制的适应度,即它的适应度f的概率与适应度f的种群大小成正比。同样,死亡只是移除一个适应度最低的个体。与Ben-Ari和Schinazi (J Stat Phys 162:415-425, 2016)获得的指数行为不同,这种优先依恋规则导致了渐近的幂律行为。
Ben-Ari and Schinazi (J Stat Phys 162:415-425, 2016) introduced a stochastic model to study 'virus-like evolving population with high mutation rate'. This model is a birth and death model with an individual at birth being either a mutant with a random fitness parameter in [0, 1] or having one of the existing fitness parameters with uniform probability; whereas a death event removes the entire population of the least fitness. We change this to incorporate the notion of 'survival of the fittest', by requiring that a non-mutant individual, at birth, has a fitness according to a preferential attachment mechanism, i.e., it has a fitness f with a probability proportional to the size of the population of fitness f. Also death just removes one individual with the least fitness. This preferential attachment rule leads to a power law behaviour in the asymptotics, unlike the exponential behaviour obtained by Ben-Ari and Schinazi (J Stat Phys 162:415-425, 2016).
期刊介绍:
The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena.
Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.