Designing overall stoichiometric conversions and intervening metabolic reactions

Anupam Chowdhury, Costas D. Maranas
2015 Scientific Reports  
Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient
more » ... es for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e., minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C 2+ metabolites with higher carbon efficiency. Microbial metabolism describes the full range of enzymatic conversions of carbon substrates to cellular biomass precursors, energy equivalents and biochemical molecules. Metabolic engineering harnesses this metabolic machinery for converting feedstock substrates to a growing range of products 1-3 . Starting with single gene mutations, the range of interventions over the past decade has expanded considerably to enable genome-wide editing and pathway assembly 4 . The use of system and synthetic biology tools have enabled the tunable regulation of genes 5-8 , assembly of heterologous pathways 9,10 and temporal control of gene expression 11,12 . Recent advances in multiplex engineering (e.g., MAGE 13 ), efficient genome-editing (e.g., CRISPR-Cas 14,15 ) and genome-wide regulation of gene expression through small RNAs 16 have brought closer the dream of "designer cells" that can catalyze any tailor-made stoichiometry-balanced metabolic conversion with high specificity and control 17 . Concurrent with experimental efforts, computational strain design tools relying on stoichiometry 18-20 along with kinetic expressions 21,22 is increasingly being used to guide the redesign of microbial metabolism. So far, these redesign approaches have mostly concentrated on retrofitting the metabolic capabilities of the production host by preventing carbon loss and ensuring proper redox supply. Existing computational procedures for the de novo pathway design rely on either optimization techniques or graph-search approaches. Linear Programming (LP) and Mixed Integer Linear Programming (MILP) approaches for pathway design, in general, extract a minimal stoichiometry-balanced sub-network that converts a source metabolite to a target chemical with high yield 23 . While early work was restricted to design pathways for small-to-medium size networks 24-26 , recent procedures have reached up to genome-scale size 27-29 often using the concept of elementary modes 30 and comprehensive databases of reactions 31-34 . However, these procedures do not necessarily conform to a previously identified optimal conversion stoichiometry thereby missing the opportunity to optimally recycle intermediates to reach a maximum yield.
doi:10.1038/srep16009 pmid:26530953 pmcid:PMC4632160 fatcat:tj3dg2dqlnd6xok3fr54wnxdk4