Computational metabolic engineering strategies for growth-coupled biofuel production by Synechocystis
2016 (English)In: Metabolic Engineering Communications, ISSN 2214-0301, Vol. 3, 216-226 p.Article in journal (Refereed) Published
Chemical and fuel production by photosynthetic cyanobacteria is a promising technology but to date has not reached competitive rates and titers. Genome-scale metabolic modeling can reveal limitations in cyanobacteria metabolism and guide genetic engineering strategies to increase chemical production. Here, we used constraint-based modeling and optimization algorithms on a genome-scale model of Synechocystis PCC6803 to find ways to improve productivity of fermentative, fatty-acid, and terpene-derived fuels. OptGene and MOMA were used to find heuristics for knockout strategies that could increase biofuel productivity. OptKnock was used to find a set of knockouts that led to coupling between biofuel and growth. Our results show that high productivity of fermentation or reversed beta-oxidation derived alcohols such as 1-butanol requires elimination of NADH sinks, while terpenes and fatty-acid based fuels require creating imbalances in intracellular ATP and NADPH production and consumption. The FBA-predicted productivities of these fuels are at least 10-fold higher than those reported so far in the literature. We also discuss the physiological and practical feasibility of implementing these knockouts. This work gives insight into how cyanobacteria could be engineered to reach competitive biofuel productivities.
Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 3, 216-226 p.
Biofuel, Cyanobacteria, Flux balance analysis, Modeling, MOMA, OptFlux, OptKnock, butanol, reduced nicotinamide adenine dinucleotide phosphate, algorithm, Article, biofuel production, biomass production, cyanobacterium, fatty acid oxidation, fermentation, gene mutation, metabolic engineering, nonhuman, priority journal, Synechocystis
IdentifiersURN: urn:nbn:se:kth:diva-195191DOI: 10.1016/j.meteno.2016.07.003ScopusID: 2-s2.0-84979497796OAI: oai:DiVA.org:kth-195191DiVA: diva2:1051711
FunderSwedish Foundation for Strategic Research , RBP14–0013
QC 201612022016-12-022016-11-022016-12-02Bibliographically approved