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Systems Metabolic Engineering of Arabidopsis for Increased Cellulose Production
Author(s) -
Yen Jiun,
Gillaspy Glenda,
Senger Ryan
Publication year - 2015
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.29.1_supplement.887.26
Subject(s) - flux balance analysis , metabolic engineering , cellulose , flux (metallurgy) , arabidopsis thaliana , cellulosic ethanol , biomass (ecology) , arabidopsis , metabolic flux analysis , computational biology , biofuel , biochemical engineering , genome , transgene , biological system , computer science , gene , biology , chemistry , microbiology and biotechnology , biochemistry , metabolism , engineering , agronomy , organic chemistry , mutant
Plant metabolism offers a source to a diverse variety of commodity compounds. One current focus is on improving the yield of cellulose for use of bioethanol production. Most current metabolic engineering strategies involves altering the expression of target genes. Computational methods that utilize genome‐scale models have been shown to aid in the prediction of effective gene targets to manipulate in microbes to synthesize biofuel. Most genome‐scale modeling algorithms constrain and evaluate the flux values predicted with flux balance analysis. We recently introduced a new algorithm that constrains the ratios of fluxes. This work expands the use of this algorithm and develops Reverse Flux Balance Analysis with Flux Ratios (Reverse FBrAtio) to allow prediction of metabolic engineering strategies. As a proof‐of‐concept, this novel algorithm was used with a genome‐scale model of Arabidopsis thaliana to predict genetic manipulations to increase cellulose content. Increasing mitochondrial malate dehydrogenase activity was predicted to increase cellulose yield. Transgenic overexpression plant lines were constructed and the cellulose content of mature plants were measured. The crystalline cellulose content and biomass of the transgenic plants were found to be significantly higher. These results suggest that Reverse FBrAtio can aid in the prediction of metabolic engineering targets. The accuracy of the prediction can also be addressed through a continuous improvement cycle.