Work

Metabolic modelling: Insights into the machine room of plant metabolism

Year: 2025

Type: article

Abstract: Plant growth, development, and environmental interactions is enabled through the coordinated activity of numerous biochemical reactions that constitute plant metabolic networks. The inherent complexity and interconnectivity within these networks underscore the importance of investigating plant metabolism from a network perspective. Metabolic modelling provides a holistic in silico representation of plant metabolism, enabling mechanistic insights into network-level processes. In this review, we consolidate recent trends in plant metabolic modelling, highlighting how these approaches can be exploited to study metabolism from subcellular to community and ecosystem levels. We discuss how the scope of plant metabolic modelling has broadened to represent diverse plant species, genotype- and context-specific metabolism as well as specialized metabolic pathways, and to capture spatiotemporal resolution and plant-microbe interactions. Moreover, we review machine learning and deep learning frameworks that assist model reconstruction, parameterization, and analysis, explore hybrid strategies that enhance mechanistic models, and address current challenges and future directions in the field of plant metabolic modelling.

Source: Journal of Plant Physiology

DOI: 10.1016/j.jplph.2025.154584

Authors: Tiago M. Machado, Nadine Töpfer, Fatemeh Soltani

Institutions: University of Cologne, Cluster of Excellence on Plant Sciences

Language: English

Topic: Microbial Metabolic Engineering and Bioproduction

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,370