2007 Volume 16 Issue 9
Article Contents

Chen Jia, Li Sheng, Ma Hong-Ru. 2007: Quasispecies distribution of Eigen model, Chinese Physics B, 16(9): 2600-2607.
Citation: Chen Jia, Li Sheng, Ma Hong-Ru. 2007: Quasispecies distribution of Eigen model, Chinese Physics B, 16(9): 2600-2607.

Quasispecies distribution of Eigen model

  • Available Online: 30/09/2007
  • Fund Project: the National Natural Science Foundation of China (Grant 10105007 and 10334020)
  • We have studied sharp peak landscapes of the Eigen model from a new perspective about how the quasispecies are distributed in the sequence space. To analyse the distribution more carefully, we bring in two tools. One tool is the variance of Hamming distance of the sequences at a given generation. It not only offers us a different avenue for accurately locating the error threshold and illustrates how the configuration of the distribution varies with copying fidelity q in the sequence space, but also divides the copying fidelity into three distinct regimes. The other tool is the similarity network of a certain Hamming distance do, by which we can gain a visual and in-depth result about how the sequences are distributed. We find that there are several local similarity optima around the centre (global similarity optimum) in the distribution of the sequences reproduced near the threshold. Furthermore, it is interesting that the distribution of clustering coefficient C(k) follows lognormal distribution and the curve of clustering coefficient C of the network versus d0 appears to be linear near the threshold.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Quasispecies distribution of Eigen model

Abstract: We have studied sharp peak landscapes of the Eigen model from a new perspective about how the quasispecies are distributed in the sequence space. To analyse the distribution more carefully, we bring in two tools. One tool is the variance of Hamming distance of the sequences at a given generation. It not only offers us a different avenue for accurately locating the error threshold and illustrates how the configuration of the distribution varies with copying fidelity q in the sequence space, but also divides the copying fidelity into three distinct regimes. The other tool is the similarity network of a certain Hamming distance do, by which we can gain a visual and in-depth result about how the sequences are distributed. We find that there are several local similarity optima around the centre (global similarity optimum) in the distribution of the sequences reproduced near the threshold. Furthermore, it is interesting that the distribution of clustering coefficient C(k) follows lognormal distribution and the curve of clustering coefficient C of the network versus d0 appears to be linear near the threshold.

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