초록
<P><B>Abstract</B></P> <P>Developing autotrophic, acetogenic bacteria strains as gas fermentation platforms is a promising avenue for converting industrial waste gas streams into valuable chemical products. One such strain, <I>Clostridium autoethanogenum</I>, naturally converts CO, CO<SUB>2</SUB>, and H<SUB>2</SUB> gases into ethanol and acetate. Currently, lowering the acetate to ethanol production ratio is a key strategy for accomplishing large-scale industrial application of <I>C. autoethanogenum</I> gas fermentation. Unfortunately, the limited availability and time-intensive implementation of genetic engineering tools for clostridia strains greatly hinders metabolic engineering efforts toward this goal. To alleviate the lack of sufficient mutant phenotype data interrogating the pathways of interest, computational tools are needed to resolve experimental observations and predict engineering targets to help minimize experimental characterization in the lab. While stoichiometric models of <I>C. autoethanogenum</I> metabolism are available, they are unable to provide insight into regulatory relationships, rate-limiting steps, or the effects of altering enzyme expression. In this work, we offer the first kinetic representation of <I>C. autoethanogenum</I> core metabolism developed using the Ensemble Modeling (EM) framework. We have adapted the existing method to enable the usage of non-genetic perturbation data, specifically the effects of changing biomass concentration, to sample and train our kinetic parameter sets. Our final kinetic parameter ensemble accurately predicts intracellular metabolite concentrations and engineering strategies for improved ethanol production.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Constructed kinetic ensemble model of C. autoethanogenum core metabolism. </LI> <LI> Adapted Ensemble Modeling framework to screen parameter sets using non-genetic perturbation data. </LI> <LI> Demonstrated good agreement between measured metabolite concentrations and model predictions. </LI> <LI> Predicted metabolic engineering strategies for improving ethanol production. </LI> </UL> </P>