29. GENOME-SCALE MODELING OF MICROBIAL ELECTROSYNTHESIS FOR ELECTROFUEL PRODUCTION

Department: Bioengineering
Research Institute Affiliation: Graduate Program in Bioinformatics
Faculty Advisor(s): Bernhard O. Palsson | Karsten Zengler
Award(s): Department Best Poster

Primary Student
Name: Harish Nagarajan
Email: hnagaraj@ucsd.edu
Phone: 858-822-1144
Grad Year: 2012

Student Collaborators
Juan Nogales, junogalesenrique@ucsd.edu | Merve Sahin, msahin@ucsd.edu | Ali Ebrahim, aebrahim@ucsd.edu | Adam Feist, afeist@ucsd.edu

Abstract
A novel mechanism, known as microbial electrosynthesis, in which microorganisms directly use electric current to reduce carbon dioxide to multi-carbon organic compounds that are excreted from the cells into extracellular medium, has recently been discovered. Microbial electrosynthesis differs significantly from photosynthesis in that carbon and electron flow is primarily directed to the formation of extracellular products, rather than biomass. However, extensive knowledge about the metabolism of the organism as well as its extracellular electron transfer pathways is critical to realize the potential of this technology for the production of the desired fuel compound. So far, only a few acetogens have been shown to be capable of accepting electrons from the cathode to reduce carbon dioxide to limited organic compounds such as acetate and 2-oxobutyrate. Constraint-based metabolic modeling and analysis has been useful for discovering and understanding new capabilities and content in bacteria, as well as in guiding metabolic engineering efforts for targeted production. In this study, we present the application of this constraint-based modeling technique on an electrosynthetic bacterium, Clostridium ljungdahlii, to characterize this process for autotrophic synthesis of multi-carbon organic compounds such as butanol. Following the established protocol, we have reconstructed the genome-scale metabolic network of this electrosynthetic organism that comprises of 675 metabolic reactions encoded by 618 genes. This reconstruction captures all the major central metabolic, biosynthetic and energy conservation pathways. Importantly, this network represents one of the first detailed descriptions of key electrosynthesis pathways. The genome-scale model is used in conjunction with physiological data to extensively characterize the various metabolic phenotypes of C. ljungdahlii. We will further employ in silico strain-design tools on the validated metabolic model to optimize butanol production under electrosynthetic conditions. In summary, we present the first metabolic network of a homoacetogen and its application as a strain-design platform for optimizing microbial electrosynthesis. External Collaborator: Derek R. Lovley Department of Microbiology, University of Massachusetts Amherst

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