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Metabolic modeling of synthesis gas fermentation in bubble column reactors

Author:
Chen, Jin, Gomez, Jose A., Höffner, Kai, Barton, Paul I., Henson, Michael A.
Source:
Biotechnology for biofuels 2015 v.8 no.1 pp. 89
ISSN:
1754-6834
Subject:
Clostridium ljungdahlii, bacteria, bioreactors, carbon monoxide, catalysts, engineering, equations, ethanol, fermentation, fuels, hydrogen, linear programming, mathematical models, prediction, synthesis gas
Abstract:
BACKGROUND: A promising route to renewable liquid fuels and chemicals is the fermentation of synthesis gas (syngas) streams to synthesize desired products such as ethanol and 2,3-butanediol. While commercial development of syngas fermentation technology is underway, an unmet need is the development of integrated metabolic and transport models for industrially relevant syngas bubble column reactors. RESULTS: We developed and evaluated a spatiotemporal metabolic model for bubble column reactors with the syngas fermenting bacterium Clostridium ljungdahlii as the microbial catalyst. Our modeling approach involved combining a genome-scale reconstruction of C. ljungdahlii metabolism with multiphase transport equations that govern convective and dispersive processes within the spatially varying column. The reactor model was spatially discretized to yield a large set of ordinary differential equations (ODEs) in time with embedded linear programs (LPs) and solved using the MATLAB based code DFBAlab. Simulations were performed to analyze the effects of important process and cellular parameters on key measures of reactor performance including ethanol titer, ethanol-to-acetate ratio, and CO and H₂ conversions. CONCLUSIONS: Our computational study demonstrated that mathematical modeling provides a complementary tool to experimentation for understanding, predicting, and optimizing syngas fermentation reactors. These model predictions could guide future cellular and process engineering efforts aimed at alleviating bottlenecks to biochemical production in syngas bubble column reactors.
Agid:
5741944