Abstract
Potato (Solanum tuberosum) is a significant non-grain food crop in terms of global production. However, its yield potential might be raised by identifying means to release bottlenecks within photosynthetic metabolism, from the capture of solar energy to the synthesis of carbohydrates. Recently, engineered increases in photosynthetic rates in other crops have been directly related to increased yield - how might such increases be achieved in potato? To answer this question, we derived the photosynthetic parameters Vcmax and Jmax to calibrate a kinetic model of leaf metabolism (e-Photosynthesis) for potato. This model was then used to simulate the impact of manipulating the expression of genes and their protein products on carbon assimilation rates in silico through optimizing resource investment among 23 photosynthetic enzymes, predicting increases in photosynthetic CO2 uptake of up to 67%. However, this number of manipulations would not be practical with current technologies. Given a limited practical number of manipulations, the optimization indicated that an increase in amounts of three enzymes - Rubisco, FBP aldolase, and SBPase - would increase net assimilation. Increasing these alone to the levels predicted necessary for optimization increased photosynthetic rate by 28% in potato.
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