Predicting plant thermal responses using a temperature-aware plant metabolic model: a primer

Abstract Understanding plant temperature responses at a molecular level can speed up the development of climate-resilient crops. However, the threats due to anthropogenic warming necessitate imminent solutions to ensure food security. Modelling plant thermal responses at a molecular level offers a feasible strategy to identify gene targets that can mitigate the negative effects of projected temperature increases. Implementation of this strategy in practice requires the development and usage of temperature-aware models that incorporate a systems-level description of the existing knowledge on plant metabolism. Here, we detail the assumptions and building blocks of the recently assembled ecAraCore, an enzyme-constrained temperature-aware model of central metabolism for the model plant Arabidopsis thaliana. The goal of this study is to provide a primer for building a temperature-aware model of plant metabolism and to offer step-by-step instructions for using the ecAraCore model to simulate metabolic traits, including: relative growth rate, reaction fluxes, and enzyme abundances for specified environmental inputs. In addition, we provide another case study of how the ecAraCore model can be used to simulate temperature-sensitive knockouts. The primer sets the basis for the development of more involved metabolic engineering strategies aimed at the mitigation of the negative effects of temperature increases—the hallmark of future climate scenarios.

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Publication Details

Journal
in silico Plants
Published
2025-01-01
DOI
https://doi.org/10.1093/insilicoplants/diaf020
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
Field-Weighted Citation Impact
0.00

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article

Predicting plant thermal responses using a temperature-aware plant metabolic model: a primer

Zoran Nikoloski, Philipp Wendering
in silico Plants
Microbial Metabolic Engineering and Bioproduction
article

Predicting plant thermal responses using a temperature-aware plant metabolic model: a primer

Zoran Nikoloski, Philipp Wendering
article en

Abstract

Abstract Understanding plant temperature responses at a molecular level can speed up the development of climate-resilient crops. However, the threats due to anthropogenic warming necessitate imminent solutions to ensure food security. Modelling plant thermal responses at a molecular level offers a feasible strategy to identify gene targets that can mitigate the negative effects of projected temperature increases. Implementation of this strategy in practice requires the development and usage of temperature-aware models that incorporate a systems-level description of the existing knowledge on plant metabolism. Here, we detail the assumptions and building blocks of the recently assembled ecAraCore, an enzyme-constrained temperature-aware model of central metabolism for the model plant Arabidopsis thaliana. The goal of this study is to provide a primer for building a temperature-aware model of plant metabolism and to offer step-by-step instructions for using the ecAraCore model to simulate metabolic traits, including: relative growth rate, reaction fluxes, and enzyme abundances for specified environmental inputs. In addition, we provide another case study of how the ecAraCore model can be used to simulate temperature-sensitive knockouts. The primer sets the basis for the development of more involved metabolic engineering strategies aimed at the mitigation of the negative effects of temperature increases—the hallmark of future climate scenarios.

in silico PlantsVol. 7(2)
University of Potsdam (DE), University of Cambridge (GB), Cambridge School (PT), Bridge University (SS), Max Planck Institute of Molecular Plant Physiology (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 76%
Microbial Metabolic Engineering and Bioproduction
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Predicting plant thermal responses using a temperature-aware plant metabolic model: a primer — Zoran Nikoloski, Philipp Wendering · in silico Plants (2025) | TGRS Research Map | TGRS