Papp Balázs, Szappanos Balázs, Notebaart Richard A
Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Szeged, Hungary.
Methods Mol Biol. 2011;759:483-97. doi: 10.1007/978-1-61779-173-4_27.
One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology.
进化系统生物学这一新兴领域的主要目标之一，是检验那些不仅从群体遗传学角度看具有现实意义，而且在分子生物学机制方面也详尽的进化假说。通过为数百个基因提供基因型与表型之间的映射关系，代谢网络的基因组规模系统生物学模型已经为酵母及其他微生物物种的代谢基因含量和表型的进化提供了有价值的见解。在此，我们回顾了这些计算模型近期在预测突变的适应性效应、遗传相互作用、进化结果以及解读突变稳健性机制方面的应用。虽然这些研究表明，即使是生化反应网络的简化模型对于进化分析也可能具有很高的信息量，但它们也揭示了这个建模框架在定量预测突变效应方面的弱点，这是进化系统生物学未来取得进展需要解决的一个挑战。
