AI4PhotMod - Artificial Intelligence for parameter inference in Photosynthesis Models

AI4PhotMod - 用于光合作用模型中参数推断的人工智能

• 批准号: BB/Y51388X/1
• 负责人: Johannes Kromdijk
• 金额: $ 32.87万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY51388X%2F1
• 关键词: AI4PhotMod; Artificial; Intelligence; parameter; inference

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.

光合作用可将二氧化碳从大气中固定在农作物和天然植被的生长中，从而提供可再生的食物，燃料，燃料，药物和纤维。提高光合效率的提高也越来越被认为是提高作物性能的策略。使用植物和周围空气之间二氧化碳和水蒸气交换的测量，用于确定在光合作用过程中吸收了多少碳，并平行地转移了多少水，以及这些通量可能会随着环境条件的变化而变化，在生长过程中的短期变化，或者由于氛围的长期变化，或者是由于climate的长期变化。尽管可以使用光合作用，但还可以使用光合作用和二氧化碳同化的代谢反应网络的事实，并且存在详细的计算机模型，而这些计算机模型与当前使用的简单模型相比，这些知识还多得多。过度简单模型的使用在专注于提高光合作用效率的工作中是有问题的，因为它们不包含对所涉及过程的充分详细表示，因此无法可靠地为工程策略的设计提供了信息。但是，更合适的详细模型的相当复杂性导致了一个重大参数化问题。模型校准数据缺乏，并且在可用数据的地方，基于经典方法的数据估计需要很长时间。该建议将解决这两个问题。使用人工智能方法，我们将开发一种参数预测算法，一旦训练，该算法只需几分钟即可运行。我们将使用已广泛采用且易于使用的标准化协议生成的最小数据集开发此方法。这项工作的结果将允许在现有的大量数据以及广泛的新研究项目中应用光合作用的最先进模型。