Exploring synergies between plant metabolic modelling and machine learning.

Area of Science:

• Plant biology
• Metabolomics
• Systems biology

Background:

• Plants produce diverse metabolites for environmental adaptation, making plant metabolism crucial for understanding phenotypes.
• Omics data generation is rapidly increasing, aiding the study of molecular biology from genome to phenotype.
• Constraint-based modeling (CBM) using genome-scale metabolic models (GSMMs) integrates omics data with biochemical knowledge.

Purpose of the Study:

• To review major advances in plant metabolic modeling.
• To highlight the integration of constraint-based modeling (CBM) and machine learning (ML) in plant metabolism studies.
• To discuss the application of ML in addressing challenges in plant metabolic modeling.

Main Methods:

• Review of constraint-based modeling (CBM) approaches for plant genome-scale metabolic models (GSMMs).
• Analysis of hybrid CBM-ML studies applied to plant omics data.
• Discussion of machine learning (ML) techniques for interpreting complex plant metabolic data.

Main Results:

• The development of plant GSMMs has advanced significantly since 2009, with context-specific and multi-tissue models emerging.
• Hybrid CBM-ML approaches are showing promising results in analyzing complex and heterogeneous plant omics datasets.
• Machine learning offers potential solutions for the unique challenges inherent in plant metabolic modeling.

Conclusions:

• Plant metabolic modeling is critical for understanding plant phenotypes and adaptation.
• The integration of CBM and ML represents a powerful new direction for plant metabolic research.
• Further application of ML is expected to overcome existing hurdles in plant metabolic modeling.