人工基因组模型中的涌现网络结构、进化鲁棒性和点突变的非线性效应
Emergent Network Structure, evolvable Robustness and non-linear Effects of Point Mutations in an Artificial Genome Model
摘要：
遗传调控是发展的一个关键组成部分，但对遗传网络的结构和动态的清晰理解还没有到手。在本文中，我们研究了这些性质的人工基因组模型最初介绍的Reil（1999）。我们分析了随机生成的基因组的统计特性的序列和网络水平上，并表明，该模型正确地预测基因组中的基因的频率在实验数据中发现。使用进化算法的基础上稳定选择的表型，我们表明，对单碱基突变的动态鲁棒性，以及对随机变化的初始状态的监管动态，模仿环境条件下的随机波动，可以并行出现。在序列水平上的点突变对网络布线具有强烈的非线性影响，包括结构中性突变和多个连接的同时重新布线，这偶尔会导致吸引子景观的强烈重组和演化动力学的亚稳定性。进化的基因组在序列和网络水平上都表现出特征模式。
英文摘要：
Genetic regulation is a key component in development, but a clear understanding of the structure and dynamics of genetic networks is not yet at hand. In this paper we investigate these properties within an artificial genome model originally introduced by Reil (1999). We analyze statistical properties of randomly generated genomes both on the sequence- and network level, and show that this model correctly predicts the frequency of genes in genomes as found in experimental data. Using an evolutionary algorithm based on stabilizing selection for a phenotype, we show that dynamical robustness against single base mutations, as well as against random changes in initial states of regulatory dynamics that mimic stochastic fluctuations in environmental conditions, can emerge in parallel. Point mutations at the sequence level have strongly non-linear effects on network wiring, including as well structurally neutral mutations and simultaneous rewiring of multiple connections, which occasionally lead to strong reorganization of the attractor landscape and metastability of evolutionary dynamics. Evolved genomes exhibit characteristic patterns on both sequence and network level.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
