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病毒进化和适应的建模:挑战与回报。

Modelling viral evolution and adaptation: challenges and rewards.

机构信息

Centro de Astrobiología (INTA-CSIC), Ctra. de Ajalvir km. 4, 28850 Torrejón de Ardoz, Madrid, Spain.

出版信息

Curr Opin Virol. 2012 Oct;2(5):531-7. doi: 10.1016/j.coviro.2012.06.006. Epub 2012 Jul 15.

Abstract

Viral populations are extremely plastic. They maintain and steadily generate high levels of genotypic and phenotypic diversity that may result in different adaptive strategies. A major unknown factor in constructing realistic models of viral evolution is how mutations affect fitness, which amounts to unveiling the nature of viral fitness landscapes. Our understanding of viral complexity is improving thanks to new techniques as deep sequencing or massive computation, and to systematic laboratory assays. In this way, we are clearing up the role played by neutral networks of genotypes, by defective and cooperative interactions among viral mutants, or by co-evolution with immune systems. Models of viral evolution are thus improving their accuracy and becoming more competent from a conceptual and a predictive viewpoint.

摘要

病毒群体具有极强的可塑性。它们维持并不断产生高水平的基因型和表型多样性,这可能导致不同的适应策略。在构建现实的病毒进化模型时,一个主要的未知因素是突变如何影响适应性,这相当于揭示病毒适应性景观的本质。由于深度测序或大规模计算等新技术以及系统的实验室检测,我们对病毒复杂性的理解正在提高。通过这种方式,我们正在澄清基因型中性网络、病毒突变体之间的缺陷和合作相互作用,或者与免疫系统共同进化所扮演的角色。因此,病毒进化模型从概念和预测的角度来看,正在提高其准确性和能力。

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