Tracing Optimal Conversion Stoichiometries Using Native and Novel Biotransformation
De novo metabolic pathways can be carefully designed to outperform native pathways with higher carbon and energy efficiencies as well as expand the product range of a host organism. Scanning through the huge repertoire of microbial enzymes to identify potential components of a novel pathway is a challenging task as one has to consider all the possible factors affecting the pathway efficiency. They include the stoichiometric and cofactor balance, thermodynamic feasibility, co-substrate and co-product choices and compatibility of the enzymes with the host. To this end, we have developed a two-stage MILP-based (optStoic and minRxn/minFlux) formalism. The first step, optStoic, explores the maximum extent of converting carbon substrate(s) to desired product(s) through non-intuitive combination of co-reactants and co-products while remaining within the limits of mass, energy and thermodynamic balances. Subsequently, minRxn/minFlux identifies a minimal reaction network to achieve the overall conversion. Identified designs consisted of improvement on existing strategies in terms of carbon and energy efficiency. Specifically, multiple alternatives for non-oxidative glycolysis and methanotrophy were identified that showed non-intuitive combinations of a core set of known pathways can lead to higher carbon and energy efficiency. Pareto analysis of metabolic pathways revealed interesting tradeoffs between pathway energy efficiency and enzyme expression. Native central metabolic pathways were found to be optimized through natural evolution for energy efficiency. Upon further analysis, improved synthetic alternatives with lower enzyme expression costs were also identified after redesigning native pathways with closely related orthologs. In addition, we developed a reaction-rule based optRule algorithm for designing de novo biotransformations absent in databases to extend the optStoic formalism towards novel metabolites. In this study, a combined (mass-balance and reaction-rule) approach was used to identify non-intuitive biological designs for optimally converting carbon substrates from renewable sources, gases and feedstock to economically valuable molecules. In particular, we identified various pharmaceutical and platform molecule bioproduction strategies from inexpensive industrial feedstocks such as toluene and naphthalene.