单峰高斯分布适应面上的误差阈

Error Threshold on Single Peak Gaussian Distributed Fitness Landscapes

在Eigen的单峰适应面模型基础上,提出了生物体的适应值为高斯分布的随机适应面模型。利用系综平均的方法,计算了在单峰高斯分布适应面上准物种的浓度分布和误差阈。结果表明,对于小的适应面涨落,准物种分布和误差阈与确定情形相比变化极小,误差阈对于小的涨落是稳定的。然而,当适应值涨落较大时,从准物种到误差灾变的转变不再明显。误差阈变宽,并且在涨落增加时向大的突变率方向移动。

Based on the Eigen model with a single peak fitness landscape, the fitness values of all sequence types are assumed to be random with Gaussian distribution. By ensemble average method, the concentration distribution and error threshold of quasispecies on single peak Gaussian distributed fitness landscapes were evaluated. It is shown that the concentration distribution and error threshold change little in comparing with deterministic case for small fluctuations, which implies that the error threshold is stable against small perturbation. However, as the fluctuation increases, the situation is quite different. The transition from quasi-species to error catastrophe is no longer sharp. The error threshold becomes a narrow band which broadens and shifts toward large values of error rate with increasing fluctuation.