(727c) Spatiotemporal Metabolic Modeling of Clostridium Ljungdahlii for Renewable Alcohol Production from Synthesis Gas

Chen, J., University of Massachusetts Amherst
Gomez, J. A., Massachusetts Institute of Technology
Höffner, K., Massachusetts Institute of Technology
Barton, P. I., Massachusetts Institute of Technology
Henson, M. A., University of Massachusetts Amherst

One of the most promising routes to renewable liquid fuels and chemicals is the fermentation of synthesis gas (syngas; mainly comprised of H2/CO/CO2) streams by specialized bacteria to synthesize desired products such as ethanol and 2,3-butanediol. The most commonly studied syngas fermenting bacterium is Clostridium ljungdahlii, and a genome-scale reconstruction of C. ljungdahlii metabolism has recently been published. While commercial development of syngas fermentation technology is underway, many research problems must be addressed to further advance the technology towards economic competitiveness. Commercial development efforts are currently focused on bubble column reactors due to their superior gas-liquid mass transfer characteristics and enhanced operational flexibility.

In this presentation, we describe a spatiotemporal metabolic model of syngas bubble column reactors that combines the C. ljungdahlii genome-scale reconstruction with multiphase transport equations that govern convective and dispersive processes within the spatially varying column. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We show that the model is able to predict biomass, dissolved gas, and secreted ethanol and acetate concentrations in a spatially and time resolved manner. To expand the range of metabolic byproducts that can be synthesized, we utilize the metabolic reconstruction and the OptKnock framework to identify gene knockouts that lead to the production of 2,3-butanediol and butanol as well as the overproduction of ethanol. These knockout strategies are further screened using our syngas bubble column reactor model to access their relative performance under realistic fermentation conditions. The simulation results provide new metabolic engineering targets for C. ljungdahlii that can be experimentally tested with the continued development of genetic engineering tools.

In addition to providing new insights into bottlenecks to biochemical production in syngas bubble column reactors, the study established a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations.