Genome-scale reconstruction and constraint-based modelling of Arabidopsis thaliana energy metabolism

Recent advances in sequencing technology have enabled the elucidation of entire genome sequences for a number of key organisms. The bioinformatics post-processing of these sequences should reveal the complete set of molecular components involved in cellular biochemical activities. The next challenge for the emerging eld of systems biology is to integrate this data into genome-scale reconstructions to describe and simulate whole-cell metabolism. Such computational models have immense potential to speed up the rate of discovery (while reducing the need for expensive lab work) by their ability to rapidly generate and test new hypotheses. In plant biology, metabolic network modelling can generate new knowledge for improving plant performance.

This approach is particularly useful for metabolic engineering purposes, predicting the necessary changes needed in order to enhance the yield and nutritional value of a range of agricultural products. This project involved the comprehensive reconstruction of a series of genomescale models describing Arabidopsis thaliana energy metabolism. Three individual metabolic networks of energy organelles were reconstructed and validated by simulating various scenarios under chosen constraints. The models were used to investigate how plant energy metabolism alters with changes in environmental conditions and to design metabolic engineering strategies for improving plant performance. The individual models were then combined into a whole-cell reconstruction providing overall insights into the energy metabolism of plant cells and the importance of cellular compartmentalisation. Several applications of the model are presented, including the prediction of essential metabolic genes and synthetic lethal genetic interactions. A novel approach for simulating plant heterosis in metabolic networks is also presented. The analysis provides new insights into the potential mechanisms by which heterosis leads to efficiencies in energy metabolism.