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Three-dimensional microscale modelling of CO sub(2) transport and light propagation in tomato leaves enlightens photosynthesis

We present a combined three-dimensional (3-D) model of light propagation, CO sub(2) diffusion and photosynthesis in tomato (Solanum lycopersicum L.) leaves. The model incorporates a geometrical representation of the actual leaf microstructure that we obtained with synchrotron radiation X-ray laminog...

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Bibliographic Details
Published in:Plant, cell and environment cell and environment, 2016-01, Vol.39 (1), p.50-61
Main Authors: Ho, Quang Tri, Berghuijs, Herman NC, Watte, Rodrigo, Verboven, Pieter, Herremans, Els, Yin, Xinyou, Retta, Moges A, Aernouts, Ben, Saeys, Wouter, Helfen, Lukas, Farquhar, Graham D, Struik, Paul C, Nicolai, Bart M
Format: Article
Language:English
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Summary:We present a combined three-dimensional (3-D) model of light propagation, CO sub(2) diffusion and photosynthesis in tomato (Solanum lycopersicum L.) leaves. The model incorporates a geometrical representation of the actual leaf microstructure that we obtained with synchrotron radiation X-ray laminography, and was evaluated using measurements of gas exchange and leaf optical properties. The combination of the 3-D microstructure of leaf tissue and chloroplast movement induced by changes in light intensity affects the simulated CO sub(2) transport within the leaf. The model predicts extensive reassimilation of CO sub(2) produced by respiration and photorespiration. Simulations also suggest that carbonic anhydrase could enhance photosynthesis at low CO sub(2) levels but had little impact on photosynthesis at high CO sub(2) levels. The model confirms that scaling of photosynthetic capacity with absorbed light would improve efficiency of CO sub(2) fixation in the leaf, especially at low light intensity. We present a comprehensive 3-D model of light propagation, CO sub(2) diffusion and photosynthesis in tomato (Solanum lycopersicum L.) leaves. The model incorporates the actual leaf microstructure and opens up new possibilities for in silico approaches to improving insights into leaf carbon uptake and to predicting climatic impacts on crop yield and vegetation.
ISSN:0140-7791
1365-3040
DOI:10.1111/pce.12590