With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.

随着组学数据以前所未有的速度生成，基因组规模的建模已成为其组织和分析的关键。然而，在知识不足以表示此类数据背后的机制或在尝试机械建模之前作为数据管理手段的情况下，机器学习方法已经取得了进展。我们讨论基因组规模建模的最新进展以及网络和错误减少、细胞内约束以及菌株设计应用的优化算法的开发。我们进一步回顾了监督和无监督机器学习方法在微生物和哺乳动物细胞系统组学数据集上的应用，并提出了通过混合建模来利用两种建模方法潜力的努力。