Metabolic pathway analysis for in silico design of efficient autotrophic production of advanced biofuels

Abstract

Abstract Herein, autotrophic metabolism of Cupriavidus necator H16 growing on CO2, H2 and O2 gas mixture was analyzed by metabolic pathway analysis tools, specifically elementary mode analysis (EMA) and flux balance analysis (FBA). As case studies, recombinant strains of C. necator H16 for the production of short-chain (isobutanol) and long-chain (hexadecanol) alcohols were constructed and examined by a combined tools of EMA and FBA to comprehensively identify the cell’s metabolic flux profiles and its phenotypic spaces for the autotrophic production of recombinant products. The effect of genetic perturbations via gene deletion and overexpression on phenotypic space of the organism was simulated to improve strain performance for efficient bioconversion of CO2 to products at high yield and high productivity. EMA identified multiple gene deletion together with controlling gas input composition to limit phenotypic space and push metabolic fluxes towards high product yield, while FBA identified target gene overexpression to debottleneck rate-limiting fluxes, hence pulling more fluxes to enhance production rate of the products. A combination of gene deletion and overexpression resulted in designed mutant strains with a predicted yield of 0.21–0.42 g/g for isobutanol and 0.20–0.34 g/g for hexadecanol from CO2. The in silico-designed mutants were also predicted to show high productivity of up to 38.4 mmol/cell-h for isobutanol and 9.1 mmol/cell-h for hexadecanol under autotrophic cultivation. The metabolic modeling and analysis presented in this study could potentially serve as a valuable guidance for future metabolic engineering of C. necator H16 for an efficient CO2-to-biofuels conversion.

Keywords

• Elementary mode analysis
• Flux balance analysis
• In silico efficient strain design
• Genetic deletion simulation
• Genetic overexpression simulation