A 3D Model of Photosynthesis to Inform Breeding for Improved Rice Performance in a Changing Climate

未来几十年，对全球粮食生产日益增长的需求，将给全球日益萎缩的农田带来巨大负担。据估计，需要将农业生产力提高100%，但人们普遍认为，目前的农业实践与环境和可持续性目标相冲突。此外，全球气候变化正在改变作物生长的环境，使现在的植物不太可能在未来表现良好。这些问题将影响整个世界，但最严重的问题将由最贫穷的人感受到，他们中的大多数人依赖一种作物，即大米作为食物。这个项目的目的是为植物育种者提供信息，使他们能够生产出更高效的水稻，而且，一种准备好应对气候变化将给我们带来的挑战的水稻。培育一个新的水稻品种是一个可能需要几十年的过程。任何缩短这一过程或更快地确定农民提高水稻产量所需的性状的程序，都将对数百万依赖这种作物的人的生活产生重大影响。此外，由于最近气候变化相对较快，而植物育种缓慢，我们需要开始选择新的水稻品种，这些品种将在二氧化碳水平（气候变化的一个驱动因素）实际达到这些水平之前，就能应对甚至受益于未来二氧化碳水平的升高。利用现代计算建模的力量提供了这样做的机会。光合作用是包括水稻在内的所有作物粮食生产的主要驱动力。我们对光合作用的生物化学有了很好的了解，并且已经产生了可以模拟这一过程的计算模型，使我们能够预测光合作用是如何随着特定酶的改变而变化的。然而，这些模型有一个主要的缺点。它们是一维的，将光合作用视为在细胞中均匀发生的过程。在现实中，光合作用发生在包含成千上万个细胞的叶片中，每个细胞的位置和形状影响着每个细胞中光合作用的效率。叶子的整体性能实际上反映了所有细胞的性能。这个项目的目的是创建一个光合作用的3D模型，该模型考虑了水稻叶片中每个细胞的位置和形状。这将使我们能够在计算机上研究改变细胞的数量、大小和包装对光合作用的影响，而不必实际培育植物。这将为植物育种者节省大量的时间和金钱，使他们能够更快地培育出解决本节开头所述问题所需的下一代水稻植株。为了实现这一目标，我们将结合先进的成像技术来创建三维叶片模型，并结合植物生理学和生物化学技术来测量叶片的性能，然后我们将使用计算方法在计算机上模拟整个过程。然后我们就能问这样的问题：叶片中哪种细胞分裂模式最有利于光合作用的效率？我们能否合理地设计水稻叶片以提高其性能？然后，我们将使用新的模型来探索光合作用如何可能对大气中二氧化碳含量的增加做出反应，这可能在下个世纪发生，并在实验室和野外条件下使用在二氧化碳浓度升高下生长的植物来测试这些预测。我们将探索该模型，以预测在气候变化模型预测的各种条件下，叶片结构的哪些方面对植物维持或增加光合作用是重要的。这些信息将使育种者现在就开始选择植物，以便在20-30年后水稻仍然能够为世界人口提供足够的食物。

Increasing demands for global food production over the next decades will be a huge burden on the world's shrinking farmland. It is estimated that an increase of agricultural productivity by as much as 100% is required, yet it is widely acknowledged that present agronomic practices are on a collision course with environmental and sustainability goals. Added to this, global climate change is altering the environment in which crops grow, making it unlikely that present day plants will perform well in the future. These problems will affect the whole world, but the most acute problems will be felt by the poorest people, most of whom rely on one crop, rice, for their food. The aim of this project is to provide plant breeders with information that will allow them to produce a more efficient rice plant and, moreover, a plant that is ready to cope with the challenges that climate change is going to throw at us.Breeding a new rice variety is a process which can take decades. Any procedure which shortens this process or more quickly identify the traits that farmers need to improve rice yield will have a significant affect on the lives of millions of people who depend on this crop. Moreover, due to the relative recent rapidity of climate change and the slowness of plant breeding, we need to start selecting new varieties of rice which will cope or even benefit from future elevated carbon dioxide levels (a driver of climate change) well in advance of those levels actually being reached. Exploiting the modern power of computational modelling provides the opportunity to do this. Photosynthesis is the prime driver of food production for all crops, including rice. We have a very good understanding of the biochemistry of photosynthesis and computational models have been produced which can simulate the process, allowing us to predict how photosynthesis changes to altered level of particular enzymes. However, these models have a major drawback. They are 1-dimensional, treating photosynthesis as a process that occurs uniformly in a cell. In reality, photosynthesis occurs in leaves which contain many thousands of cells and the position and shape of each cell influence the efficiency of photosynthesis in each cell. The overall performance of a leaf actually reflects the performance of all the cells put together. The aim of this project is to create a 3D model of photosynthesis which takes into account the position and shape of each cell in a rice leaf. This will allow us to investigate the affect of altering the number, size and packing of cells on photosynthesis on a computer, without having to actually breed the plants. This would save plant breeders immense time and money, allowing them to more rapidly generate the next generation of rice plants required to tackle the problems described at the beginning of this section.To achieve this aim we will use a combination of advanced imaging techniques to create 3-D leaf models and plant physiology and biochemistry techniques to measure leaf performance We will then use computational methods to model the entire process on a computer. We will then be able to ask questions such as: what pattern of cell division in the leaf is best for the efficiency of photosynthesis? Can we rationally design a rice leaf for improved performance? We will then use the new model to explore how photosynthesis is likely to respond to the increased levels of CO2 in the atmosphere which are likely to occur over the next century and test these predictions using plants grown under elevated CO2, both in laboratory and field conditions. We will explore the model to predict which aspects of leaf structure are important for plants to maintain or increase photosynthesis under the various conditions predicted by climate change models. This information will allow breeders to start selecting plants now so that in 20-30 years time rice plants will still be able to generate sufficient food for the world population.