AI4PhotMod - Artificial Intelligence for parameter inference in Photosynthesis Models

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Photosynthesis fixes carbon dioxide from the atmosphere to drive growth of crops and natural vegetation, thus providing renewable supplies of food, fuel, medicine and fibre. Improving photosynthetic efficiency is also increasingly being recognised as a strategy to enhance crop performance. Measurements of the exchange of carbon dioxide and water vapour between plants and the air surrounding them are used to determine how much carbon is assimilated during photosynthesis, how much water is transpired in parallel, and how these fluxes may change with a change in environmental conditions, either short-term during growth, or long-term due to climate change.To analyse these gas exchange data, scientists use very simple models with only a basic representation of the biochemical processes involved in photosynthesis, despite the fact that much more detailed understanding of the metabolic network of reactions involved in photosynthesis and CO2 assimilation is available and detailed computer models exist that incorporate much more of this knowledge than the simple models currently used. The use of overly simple models is problematic in work focused on improving the efficiency of photosynthesis, since they do not contain sufficiently detailed representation of the processes involved and therefore cannot reliably inform the design of engineering strategies.However, the considerable complexity of more appropriate detailed models has led to a major parameterization problem. There is a shortage of model calibration data and where data is available, parameter estimation from this data based on classical methodology takes a very long time. This proposal will address both of these issues. Using an artificial intelligence approach we will develop a parameter prediction algorithm which, once trained, will take only a few minutes to run. We will develop this method on a minimal set of data generated with standardized protocols that are already widely adopted and easy to use. The outcomes of the work will allow application of state of the art models of photosynthesis across a wealth of pre-existing data, as well as a wide range of new research projects.