Conversion of proteins into biofuels by engineering nitrogen flux.

Biofuels are currently produced from carbohydrates and lipids in feedstock. Proteins, in contrast, have not been used to synthesize fuels because of the difficulties of deaminating protein hydrolysates. Here we apply metabolic engineering to generate Escherichia coli that can deaminate protein hydrolysates, enabling the cells to convert proteins to C4 and C5 alcohols at 56% of the theoretical yield. We accomplish this by introducing three exogenous transamination and deamination cycles, which provide an irreversible metabolic force that drives deamination reactions to completion. We show that Saccharomyces cerevisiae, E. coli, Bacillus subtilis and microalgae can be used as protein sources, producing up to 4,035 mg/l of alcohols from biomass containing ∼22 g/l of amino acids. These results show the feasibility of using proteins for biorefineries, for which high-protein microalgae could be used as a feedstock with a possibility of maximizing algal growth and total CO(2) fixation.

Reducing the allowable kinetic space by constructing ensemble of dynamic models with the same steady-state flux.

Dynamic models of metabolism are instrumental for gaining insight and predicting possible outcomes of perturbations. Current approaches start from the selection of lumped enzyme kinetics and determine the parameters within a large parametric space. However, kinetic parameters are often unknown and obtaining these parameters requires detailed characterization of enzyme kinetics. In many cases, only steady-state fluxes are measured or estimated, but these data have not been utilized to construct dynamic models. Here, we extend the previously developed Ensemble Modeling methodology by allowing various kinetic rate expressions and employing a more efficient solution method for steady states. We show that anchoring the dynamic models to the same flux reduces the allowable parameter space significantly such that sampling of high dimensional kinetic parameters becomes meaningful. The methodology enables examination of the properties of the model's structure, including multiple steady states. Screening of models based on limited steady-state fluxes or metabolite profiles reduces the parameter space further and the remaining models become increasingly predictive. We use both succinate overproduction and central carbon metabolism in Escherichia coli as examples to demonstrate these results.