AI4PhotMod - Artificial Intelligence for parameter inference in Photosynthesis Models

光合作用固定大气中的二氧化碳，以推动农作物和自然植被的生长，从而提供可再生的食物、燃料、药品和纤维供应。提高光合作用效率也越来越多地被认为是提高作物表现的一种策略。通过测量植物与周围空气之间二氧化碳和水蒸气的交换来确定光合作用过程中有多少碳被同化，有多少水被平行蒸腾，以及这些通量如何随着环境条件的变化而变化，无论是在生长过程中的短期变化，还是由于气候变化的长期变化。为了分析这些气体交换数据，科学家们使用了非常简单的模型，其中只包含了光合作用所涉及的生化过程的基本表示，尽管对光合作用和二氧化碳同化所涉及的新陈代谢网络的了解要详细得多，而且存在详细的计算机模型，比目前使用的简单模型包含了更多的这种知识。在致力于提高光合作用效率的工作中，使用过于简单的模型是有问题的，因为它们不包含对所涉及的过程的足够详细的表示，因此不能可靠地为工程策略的设计提供信息。然而，更合适的详细模型的相当复杂已经导致了一个主要的参数化问题。缺乏模型校准数据，在有数据的情况下，基于经典方法从这些数据估计参数需要很长的时间。这项提案将解决这两个问题。使用人工智能方法，我们将开发一种参数预测算法，一旦进行训练，只需几分钟就能运行。我们将在使用标准化协议生成的最小数据集上开发此方法，这些协议已经被广泛采用并易于使用。这项工作的结果将使最先进的光合作用模型能够应用于大量先前存在的数据，以及广泛的新研究项目。

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.