TY - JOUR
T1 - Kinetic ensemble model of gas fermenting Clostridium autoethanogenum for improved ethanol production
AU - Greene, Jennifer
AU - Daniell, James
AU - Köpke, Michael
AU - Broadbelt, Linda
AU - Tyo, Keith E.J.
N1 - Funding Information:
This research was supported by the Department of Energy ( DEEE0007728 and DESC0018249 ) the National Science Foundation Graduate Research Fellowship ( DGE-1324585 ). Additionally, this research was supported in part through the computational resources and staff contrinbutions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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, Clostridium autoethanogenum, naturally converts CO, CO2, and H2 gases into ethanol and acetate. Currently, lowering the acetate to ethanol production ratio is a key strategy for accomplishing large-scale industrial application of C. autoethanogenum 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 C. autoethanogenum 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 C. autoethanogenum 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.
AB - 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, Clostridium autoethanogenum, naturally converts CO, CO2, and H2 gases into ethanol and acetate. Currently, lowering the acetate to ethanol production ratio is a key strategy for accomplishing large-scale industrial application of C. autoethanogenum 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 C. autoethanogenum 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 C. autoethanogenum 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.
KW - Clostridium autoethanogenum
KW - Ensemble modeling
KW - Kinetic modeling
KW - Metabolic engineering
KW - Strain design
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U2 - 10.1016/j.bej.2019.04.021
DO - 10.1016/j.bej.2019.04.021
M3 - Article
AN - SCOPUS:85064933307
VL - 148
SP - 46
EP - 56
JO - Biochemical Engineering Journal
JF - Biochemical Engineering Journal
SN - 1369-703X
ER -