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Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways

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Correa,  S. M.
Genetics of Metabolic Traits, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Alseekh,  S.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Brotman,  Y.
Genetics of Metabolic Traits, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Fernie,  A. R.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Nikoloski,  Z.
Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Correa, S. M., Alseekh, S., Atehortúa, L., Brotman, Y., Ríos-Estepa, R., Fernie, A. R., et al. (in press). Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways. The Plant Journal, 104(1), 76-95. doi:10.1111/tpj.14906.


Cite as: http://hdl.handle.net/21.11116/0000-0007-5AAE-1
Abstract
Summary Efficient approaches to increase plant lipid production are necessary to meet contemporary industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom-up metabolic reconstruction of Jatropha curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint-based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping-stone for future rational optimization of other agronomically relevant traits in Jatropha curcas.