Modelling plant respiration: a novel approach using oxygen titration curves

植物呼吸建模：使用氧滴定曲线的新方法

• 批准号: NE/E009972/1
• 负责人: Owen Atkin
• 金额: $ 6.86万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FE009972%2F1
• 关键词: Modelling; plant; respiration; novel; approach

In 1980, Farquhar et al. (Planta 149, 78-90) published a mechanistic photosynthesis model that successfully predicted rates of net carbon dioxide exchange in C3 plants. The impact of that model on carbon exchange research has been profound, with their 1980 paper having been cited over 1300 times since publication. Why was the paper so influential? One reason was the ability of the model to help researchers understand the underlying factors controlling rates of net photosynthesis. The other was that because of its simplicity, it could be readily incorporated into large scale applications (e.g. canopy photosynthesis and climate models). But having successfully modeled photosynthesis, most large scale models then dispense with about half of the assimilate in respiration without attempting to more accurately predict variations in respiratory flux. This failure to correctly model plant respiration has important consequences for the accuracy of large scale models, as plant respiration releases ten times more carbon dioxide (one of the greenhouse gases responsible for global warming) than does the burning of fossil fuels etc. Clearly, it is time that a mechanistic 'Farquhar-like' model of plant respiration be constructed. In this research project, we will use oxygen titration curves of plant respiration to construct a plant respiration-equivalent of the Farquhar et al. model. In collaboration with colleagues at the University of Illinois in the USA, we will use a state-of-the-art oxygen analyser (currently not available in the UK) to measure rates of leaf respiration over a broad range of oxygen concentrations. This data will then be used to test the effectiveness of a mathematical model that takes into account factors such as enzyme activity and the ability of individual enzymes to consume oxygen. In addition to being of high predictive value, such a model would also enable us to better understand what underlying factors regulate variations in respiratory flux, particularly in leaves exposed to two key environmental parameters associated with climate change: temperature and atmospheric carbon dioxide concentration. Having developed the model under moderate temperature conditions at current concentrations of atmospheric carbon dioxide, we will then subject leaves to high and low temperatures and assess the impact of the temperature treatments on the model parameters. Then, the model will be used to better understand why rates of leaf respiration often increase in leaves exposed to elevated atmospheric carbon dioxide. We will use soybean (Glycine max) for our experiments, as much is known about the regulation of respiration in this species and how photosynthetic and respiratory metabolism respond to elevated atmospheric carbon dioxide concentrations. Given that the University of Illinois will cover most plant growth/consumable costs, the proposal represents excellent value for money for NERC and an opportunity to achieve an outcome that is not possible within the UK.

在1980年，Farquhar等人（Planta 149，78-90）发表了一种光合作用机理模型，该模型成功地预测了C3植物中的净二氧化碳交换速率。该模型对碳交换研究的影响是深远的，他们1980年的论文自发表以来被引用了1300多次。为什么这份报纸如此有影响力？原因之一是该模型能够帮助研究人员了解控制净光合作用速率的潜在因素。另一个是，由于它的简单性，它可以很容易地纳入大规模的应用（如冠层光合作用和气候模型）。但是，在成功地模拟了光合作用之后，大多数大尺度模型就省去了大约一半的呼吸同化物，而没有试图更准确地预测呼吸通量的变化。这种未能正确模拟植物呼吸的大规模模型的准确性有重要的后果，植物呼吸释放十倍以上的二氧化碳（负责全球变暖的温室气体之一）比燃烧化石燃料等显然，现在是时候，一个机械的“法夸尔”植物呼吸模型被构建。在本研究计画中，我们将利用植物呼吸的氧滴定曲线，建构一个Farquhar等人的植物呼吸等效模型。我们将与美国伊利诺伊大学的同事合作，使用最先进的氧气分析仪（目前在英国还没有）来测量叶片呼吸在广泛的氧气浓度范围内的速率。然后，这些数据将用于测试数学模型的有效性，该模型考虑了酶活性和单个酶消耗氧气的能力等因素。除了具有较高的预测价值外，这种模型还将使我们能够更好地了解哪些潜在因素调节呼吸通量的变化，特别是在暴露于与气候变化相关的两个关键环境参数（温度和大气二氧化碳浓度）的叶片中。在中等温度条件下，在目前的大气二氧化碳浓度的模型，我们将叶片高温和低温，并评估温度处理对模型参数的影响。然后，该模型将被用来更好地理解为什么叶片呼吸速率经常增加暴露于大气二氧化碳浓度升高的叶片。我们将使用大豆（Glycine max）进行实验，因为我们对该物种的呼吸调节以及光合作用和呼吸代谢如何对大气二氧化碳浓度升高做出反应了解得很多。考虑到伊利诺伊大学将承担大部分植物生长/消耗品成本，该提案对NERC来说是物有所值的，也是一个在英国实现不可能的结果的机会。