A genome-scale model of Arabidopsis metabolism

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.

Our understanding of the metabolism of higher plants is based on a knowledge of the properties of individual enzymes that catalyse the reactions within metabolic pathways. However, because metabolism is a highly connected network, metabolic pathways do not operate in isolation and changes within one pathway will have consequences across the network. Reductionist explanations of metabolism generally fail to take this network property into account and this explains why, despite considerable effort over the last 20 years, attempts to manipulate plant metabolism for agronomic purposes have met with limited success. It is apparent that a more sophisticated understanding of the metabolic network as a whole will be required if metabolic engineering is to move away from the trial and error approach and towards a more predictive one. This proposal therefore seeks to establish a mathematical model of the metabolic network of heterotrophic Arabidopsis cells. The model will be based on the principles of stoichiometric flux balancing, an approach that has been used to good effect to understand microbial metabolism. The model will integrate several lines of 'omic data (transcriptomic, proteomic and metabolomic) to provide constraints to the mathematical solution space, as well as a point of parameter comparison for the purposes of model validation. In addition, the 'omic datasets will be used to introduce enzyme capacity parameters into the model to allow predictions to be made as to the effect of altered enzyme abundance. Models will be generated both for cells under optimal growth conditions as well as those experiencing osmotic stress, a condition relevant to conditions of drought and salinity experienced by plants in the field. It is anticipated that these models will bring about a fundamentally new level of understanding of metabolic network behaviour in plants and will represent an important new tool to guide metabolic engineering strategies.