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.

光合作用将大气中的二氧化碳固定下来，推动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。提高光合效率也越来越被认为是提高作物性能的一种策略。通过测量植物与周围空气之间的二氧化碳和水蒸气交换量，可以确定光合作用过程中吸收了多少碳，同时蒸发了多少水，以及这些通量如何随着环境条件的变化而变化，无论是生长期间的短期变化，还是气候变化造成的长期变化。为了分析这些气体交换数据，科学家们使用非常简单的模型，其中只有光合作用中涉及的生化过程的基本表示，尽管事实上，对光合作用和CO2同化反应的代谢网络有更详细的了解，而且存在详细的计算机模型，这些模型比简单的模型包含了更多的知识，目前使用的模型。在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大的复杂性导致了主要的参数化问题。有一个模型校准数据的短缺，数据可用，参数估计从这些数据的基础上，经典的方法需要很长的时间。这项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，一旦经过训练，只需几分钟即可运行。我们将开发这种方法的最小数据集生成的标准化协议，已经被广泛采用，易于使用。这项工作的成果将允许在大量现有数据中应用最先进的光合作用模型，以及广泛的新研究项目。