Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
Bioinformatics and Mathematical Modeling Department, Centre for Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria.
Cell Mol Life Sci. 2021 Jun;78(12):5123-5138. doi: 10.1007/s00018-021-03844-4. Epub 2021 May 5.
Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.
结合基于约束建模框架的方法，利用模式植物和作物的基因组规模代谢网络，可预测代谢特征并设计代谢工程策略来对其进行操作。随着从自然多样性群体和其他群体中生成大规模基因分型数据的技术的进步，全基因组关联和基因组选择已成为确定与性状相关且可预测的遗传变异的统计方法。在这里，我们综述了整合基因组规模代谢模型中的遗传变异以描述其对反应通量影响的基于约束的方法的最新进展。由于这些方法中的一些已经应用于除植物以外的生物体，因此我们特别对其在作物中的适用性进行了批判性评估。此外，我们还进一步剖析了遗传变异对反应速率常数、酶丰度和代谢物浓度的推断影响，因为它们是反应通量的主要决定因素，并将它们与对生长等复杂性状的综合影响联系起来。通过这项系统综述，我们还提供了未来研究的路线图，通过将它们与代谢的机制模型相结合，提高统计方法的预测能力。
