Sampaio Marta, Rocha Miguel, Dias Oscar
Centre of Biological Engineering, University of Minho, Campus of Gualtar, 4710-057 Braga, Portugal.
LABBELS, Associate Laboratory, Braga, Guimarães, Portugal.
Comput Struct Biotechnol J. 2022 Apr 16;20:1885-1900. doi: 10.1016/j.csbj.2022.04.016. eCollection 2022.
As plants produce an enormous diversity of metabolites to help them adapt to the environment, the study of plant metabolism is of utmost importance to understand different plant phenotypes. Omics data have been generated at an unprecedented rate for several organisms, including plants, and are widely used to study the central dogma of molecular biology, connecting the genome to phenotypes. Constraint-based modelling (CBM) methods, working over genome-scale metabolic models (GSMMs), have been crucial for organising and analysing omics data by integrating them with biochemical knowledge. In 2009, the first plant GSMM was reconstructed and, since then, several advances have been made, including the creation of context- and multi-tissue models that have supported the study of plant metabolism. Nevertheless, plant metabolic modelling remains very challenging. In parallel, as omics datasets are complex and heterogeneous, machine learning (ML) models have been applied in their interpretation to foster knowledge discovery. Recently, the first studies combining both CBM and ML approaches have emerged and have shown promising results. Here, we present the major advances in plant metabolic modelling and review the main CBM-ML hybrid studies. Finally, we discuss the application of machine learning to address the unique challenges of plant metabolic modelling.
由于植物产生种类繁多的代谢物以帮助它们适应环境，因此植物代谢研究对于理解不同的植物表型至关重要。包括植物在内的多种生物的组学数据以前所未有的速度产生，并被广泛用于研究分子生物学的中心法则，将基因组与表型联系起来。基于约束的建模（CBM）方法作用于基因组规模的代谢模型（GSMM），通过将组学数据与生化知识整合，对于组织和分析组学数据至关重要。2009年，第一个植物GSMM被重建，从那时起，取得了多项进展，包括创建了支持植物代谢研究的上下文和多组织模型。然而，植物代谢建模仍然非常具有挑战性。同时，由于组学数据集复杂且异质，机器学习（ML）模型已被应用于对其进行解读以促进知识发现。最近，结合CBM和ML方法的首批研究已经出现，并显示出了有前景的结果。在这里，我们介绍植物代谢建模的主要进展，并综述主要的CBM-ML混合研究。最后，我们讨论机器学习在应对植物代谢建模独特挑战方面的应用。
