Genome-scale models of plant metabolism.

A genome-scale model (GSM) is an in silico metabolic model comprising hundreds or thousands of chemical reactions that constitute the metabolic inventory of a cell, tissue, or organism. A complete, accurate GSM, in conjunction with a simulation technique such as flux balance analysis (FBA), can be used to comprehensively predict cellular metabolic flux distributions for a given genotype and given environmental conditions. Apart from enabling a user to quantitatively visualize carbon flow through metabolic pathways, these flux predictions also facilitate the hypothesis of new network properties. By simulating the impacts of environmental stresses or genetic interventions on metabolism, GSMs can aid the formulation of nontrivial metabolic engineering strategies. GSMs for plants and other eukaryotes are significantly more complicated than those for prokaryotes due to their extensive compartmentalization and size. The reconstruction of a GSM involves creating an initial model, curating the model, and then rendering the model ready for FBA. Model reconstruction involves obtaining organism-specific reactions from the annotated genome sequence or organism-specific databases. Model curation involves determining metabolite protonation status or charge, ensuring that reactions are stoichiometrically balanced, assigning reactions to appropriate subcellular compartments, deleting generic reactions or creating specific versions of them, linking dead-end metabolites, and filling of pathway gaps to complete the model. Subsequently, the model requires the addition of transport, exchange, and biomass synthesis reactions to make it FBA-ready. This cycle of editing, refining, and curation has to be performed iteratively to obtain an accurate model. This chapter outlines the reconstruction and curation of GSMs with a focus on models of plant metabolism.