Metabolic modeling identifies determinants of thermal growth responses in Arabidopsis thaliana

Abstract

Temperature is a critical environmental factor affecting nearly all plant processes, including growth, development, and yield. Yet, despite decades of research, we lack the ability to predict plant performance at different temperatures, limiting the development of climate-resilient crops. Further, there is a pressing need to bridge the gap between the prediction of physiological and molecular traits to improve our understanding and manipulation of plant temperature responses. Here, we developed the first enzyme-constrained model of Arabidopsis thaliana ’s metabolism, facilitating predictions of growth-related phenotypes at different temperatures. We showed that the model can be employed for in silico identification of genes that affect plant growth at suboptimal growth temperature. Using mutant lines, we validated the genes predicted to affect plant growth, demonstrating the potential of metabolic modeling in accurately predicting plant thermal responses. The temperature-dependent enzyme-constrained metabolic model provides a template that can be used for developing sophisticated strategies to engineer climate-resilient crops.

Plant phenotyping relevance

温度依存性の酵素制約代謝モデルを開発し、成長関連表現型の予測と変異体による検証を行っており、植物表現型推定手法が研究の中心です。

Code and data availability

The paper's computational analysis code (simulations and statistics), the machine-learning tool for protein thermostability optima, and the refined AraCore metabolic model are all explicitly deposited in public GitHub repositories by the authors. No standalone public phenotype dataset URL is given; compiled RGR and CO2