A genome-scale model of Arabidopsis metabolism

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The molecules that make up the cells of biological organisms do not function in isolation but do so by interacting with other molecules. Cells thus consist of a complex network of interacting molecules. Because the behaviour of an individual molecule is influenced not only by its own properties but also by those of interacting molecules, networks display emergent properties - that is the behaviour of the network as a whole cannot be simply predicted from the properties of its components in isolation. One way of getting to grips with network behaviour is to construct mathematical models that allow network parameters to be computed. This proposal aims to generate a mathematical model that will provide insight into the metabolic network of the model plant species, Arabidopsis thaliana. Metabolism is one of the best described and most studied of all biological networks and yet our understanding of the behaviour of metabolism as a whole remains rather limited. A mathematical model will not only provide new insight into fundamental aspects of control of the plant metabolic network, but it will also be a useful tool to allow predictions to be made about the best way to manipulate the flow of metabolic intermediates. Such metabolic engineering is an important part of attempts to generate new varieties of crop plants that are better equipped to deal with challenges imposed by a changing global climate and the requirements for increased yield.