Kinetic Modeling of Metabolism in Obligatory Anaerobes
Industrially relevant anaerobes such as C. thermocellum can metabolize cellulose and C. ljungdahlii can metabolize syngas to produce biofuels, but they remain poorly characterized with significant uncertainty in their metabolic repertoire. To this end, we develop cell-wide dynamic metabolic models for the two organisms using the Ensemble Modeling (EM) paradigm which requires curated genome-scale metabolic (GSM) model of the organism as its foundation. We constructed a second-generation GSM model (iCth446) for C. thermocellum with 446 genes, 598 metabolites and 660 reactions, along with gene-protein-reaction associations by correcting cofactor dependencies, restoring elemental and charge balances and updating GAM and NGAM values. The GSM model was subsequently used to construct a core kinetic metabolic model of the C. thermocellum’s central metabolism containing 119 reactions and 93 metabolites with cellobiose as the carbon source under anaerobic growth condition. It encompasses the cellobiose degradation pathway, glycolysis/gluconeogenesis, the pentose phosphate (PP) pathway, the TCA cycle, pyruvate metabolism, anaplerotic reactions, alternative carbon metabolism, nucleotide salvage pathway, along with all biomass precursors and 22 substrate level regulatory interactions extracted from BRENDA. Model parameterization was carried out by simultaneously imposing the mutant library data recently measured and provided by the Lynd group. This dataset includes C. thermocellum variants with mutations in lactate, malate, acetate, and hydrogen production pathways and combinations thereof defining 22 specific mutants with measured concentrations of various fermentation products such as acetate, lactate, formate, hydrogen, pyruvate, ethanol, and cellobiose (19 measured concentrations per mutant). The kinetic model accurately predicted metabolic phenotypes in multiple mutant strains not included in the model parametrization. Examples include cytosolic concentrations for fourteen out of eighteen metabolites in the Dldh mutant. The kinetic model also alludes to a systemic level effect of limiting nitrogen source resulting in increased yields for lactate, pyruvate and amino acids and an increase in ammonia and sugar phosphates concentrations (~ 1.5 fold) due to down-regulation of fermentation pathways under ethanol stress. In addition, a leave-one-out cross validation analysis revealed that the robustness of the estimated parameters remained limited to the mutants located in the same vicinity of the parameterization mutants. The constructed model will also be analyzed using the ensemble modeling robustness analysis (EMRA) to identify the robustness of enzymes and the metabolic conditions which render them unstable. A similar effort is also underway for C. ljungdahlii. A core metabolic model composed of 77 reactions and 63 metabolites was constructed with 41 substrate level regulatory interactions from BRENDA based on other Clostridia species. The network spans glycolysis, the Wood-Ljungdahl pathway, and TCA cycle. Experimental flux data for a wild-type and mutants will be used to estimate the core model parameters using the same proposed framework. The constructed kinetic models will be ultimately used to explore metabolic drivers that underpin the over-production of iso-butanol in C.thermocellum and acetate in C. ljungdahlii.