Abstract
Flux-balance modeling of plant metabolic networks provides an important complement to 13C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.
Highlights
The coupling of energy-releasing processes to energy-consuming anabolic reactions drives the biosynthesis of the polymers and metabolites that constitute the fabric of the cell
Because the growth and survival of plants is intimately connected to metabolism (Smith and Stitt, 2007; Stitt et al, 2010) there is a need to understand and predict metabolic behavior
The aim of this article is to review what has been learnt from these models, to discuss the advantages and limitations of flux-balance modeling and to look to the future
Summary
Mostly catalyzed by enzymes, define a metabolic network that supports all biological activity. The rates of all the enzymecatalyzed reactions, including the associated relocation of ions and metabolites across membranes, are tightly controlled through the regulation of enzyme activity, allowing metabolic outputs to be adjusted according to varying environmental conditions and growth patterns. Plant metabolic networks are arguably the most complex of any organism, both because of the tremendous variation in their metabolic output and because of the range of environmental conditions that they encounter. There is a need to connect genotype to specific metabolic outputs so that plant breeders and metabolic engineers can generate new varieties of crops with increased yield or altered chemical composition (Fernie and Schauer, 2009)
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