Tailoring a dynamic model of photosynthetic metabolism towards greater carbon assimilation in rice

Vijayakumar, Supreeta and Wang, Yu and Lin, Hsiang Chun and Carmo-Silva, Elizabete and Long, Stephen P and Taylor, Samuel H (2025) Tailoring a dynamic model of photosynthetic metabolism towards greater carbon assimilation in rice. in silico Plants. ISSN 2517-5025

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Abstract

Modelling crops in silico can identify bottlenecks in photosynthetic metabolism that limit the realisation of maximum theoretical crop yields. Resource investment among photosynthetic enzymes in C3 photosynthesis can be optimised to maximise carbon assimilation via targeted alterations, e.g. by regulating the expression of proteins in the Calvin Benson Bassham (CBB) cycle. In this work, the e-Photosynthesis dynamic model of C3 metabolism was modified to create a rice-specific version. Species-specific equations for temperature dependences of ribulose-1,6-bisphosphate carboxylase/oxygenase (Rubisco) catalytic properties were combined with leaf-level gas exchange measurements for Oryza sativa cv. IR64 to derive photosynthetic parameters describing CBB cycle activity (Vcmax and J). These were used to re-scale enzyme activities in e-Photosynthesis before identifying redistributions of protein among photosynthetic enzymes that were optimal for CO2 assimilation at different [CO2s. Target sets of enzymes were identified for over-expression to engineer improved photosynthesis under [CO2] scenarios of limited diffusion, as might be experienced during abiotic stress, current yield potential, and future elevated [CO2]. These were evaluated using sensitivity analysis that assumed variability around achieved protein fold-changes for photosynthetic improvement reported in the literature. Increases in as few as two to as many as six enzymes would achieve photosynthetic rates upto 28% higher under water stressed conditions. In non-stressed leaves under current and future [CO2], increases in photosynthesis of upto 22% required over-expression of 4-6 enzymes.

Item Type:
Journal Article
Journal or Publication Title:
in silico Plants
ID Code:
232016
Deposited By:
Deposited On:
08 Sep 2025 10:30
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Sep 2025 14:42