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A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf

Overview
Journal Plant Physiol
Specialty Physiology
Date 2015 Aug 13
PMID 26265776
Citations 17
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Abstract

Flux balance analysis of plant metabolism is an established method for predicting metabolic flux phenotypes and for exploring the way in which the plant metabolic network delivers specific outcomes in different cell types, tissues, and temporal phases. A recurring theme is the need to explore the flexibility of the network in meeting its objectives and, in particular, to establish the extent to which alternative pathways can contribute to achieving specific outcomes. Unfortunately, predictions from conventional flux balance analysis minimize the simultaneous operation of alternative pathways, but by introducing flux-weighting factors to allow for the variable intrinsic cost of supporting each flux, it is possible to activate different pathways in individual simulations and, thus, to explore alternative pathways by averaging thousands of simulations. This new method has been applied to a diel genome-scale model of Arabidopsis (Arabidopsis thaliana) leaf metabolism to explore the flexibility of the network in meeting the metabolic requirements of the leaf in the light. This identified alternative flux modes in the Calvin-Benson cycle revealed the potential for alternative transitory carbon stores in leaves and led to predictions about the light-dependent contribution of alternative electron flow pathways and futile cycles in energy rebalancing. Notable features of the analysis include the light-dependent tradeoff between the use of carbohydrates and four-carbon organic acids as transitory storage forms and the way in which multiple pathways for the consumption of ATP and NADPH can contribute to the balancing of the requirements of photosynthetic metabolism with the energy available from photon capture.

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References
1.
Poolman M, Fell D, Thomas S . Modelling photosynthesis and its control. J Exp Bot. 2000; 51 Spec No:319-28. DOI: 10.1093/jexbot/51.suppl_1.319. View

2.
Kruger N, Ratcliffe R . Fluxes through plant metabolic networks: measurements, predictions, insights and challenges. Biochem J. 2015; 465(1):27-38. DOI: 10.1042/BJ20140984. View

3.
Sweetlove L, Lytovchenko A, Morgan M, Nunes-Nesi A, Taylor N, Baxter C . Mitochondrial uncoupling protein is required for efficient photosynthesis. Proc Natl Acad Sci U S A. 2006; 103(51):19587-92. PMC: 1748269. DOI: 10.1073/pnas.0607751103. View

4.
Poolman M, Sebu C, Pidcock M, Fell D . Modular decomposition of metabolic systems via null-space analysis. J Theor Biol. 2007; 249(4):691-705. DOI: 10.1016/j.jtbi.2007.08.005. View

5.
Poolman M . ScrumPy: metabolic modelling with Python. Syst Biol (Stevenage). 2006; 153(5):375-8. DOI: 10.1049/ip-syb:20060010. View