Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms.

Recent progress in metabolic engineering and synthetic biology enables the use of microorganisms for the production of chemicals-"bio-based chemicals." However, it is still unclear which chemicals have the highest economic prospect. To this end, we develop a framework for the identification of such promising ones. Specifically, we first develop a genome-scale constraint-based metabolic modeling approach, which is used to identify a candidate pool of 209 chemicals (together with the estimated yield, productivity, and residence time for each) from the intersection of the high-production-volume chemicals and the KEGG and MetaCyc databases. Second, we design three screening criteria based on a chemical's profit margin, market volume, and market size. The total process cost, including the downstream separation cost, is systematically incorporated into the evaluation. Third, given the three aforementioned criteria, we identify 32 products as economically promising if the maximum yields can be achieved, and 22 products if the maximum productivities can be achieved. The breakeven titer that renders zero profit margin for each product is also presented. Comparisons between extracellular and intracellular production, as well as Escherichia coli and Saccharomyces cerevisiae systems are also discussed. The proposed framework provides important guidance for future studies in the production of bio-based chemicals. It is also flexible in that the databases, yield estimations, and criteria can be modified to customize the screening.

The low cost of natural gas has driven significant interest in using C1 carbon sources (e.g. methane, methanol, CO, syngas) as feedstocks for producing liquid transportation fuels and commodity chemicals. Given the large contribution of sugar and lignocellulosic feedstocks to biorefinery operating costs, natural gas and other C1 sources may provide an economic advantage. To assess the relative costs of these feedstocks, we performed flux balance analysis on genome-scale metabolic models to calculate the maximum theoretical yields of chemical products from methane, methanol, acetate, and glucose. Yield calculations were performed for every metabolite (as a proxy for desired products) in the genome-scale metabolic models of three organisms: Escherichia coli (bacterium), Saccharomyces cerevisiae (yeast), and Synechococcus sp. PCC 7002 (cyanobacterium). The calculated theoretical yields and current feedstock prices provided inputs to create comparative feedstock cost surfaces. Our analysis shows that, at current market prices, methane feedstock costs are consistently lower than glucose when used as a carbon and energy source for microbial chemical production. Conversely, methanol is costlier than glucose under almost all price scenarios. Acetate feedstock costs could be less than glucose given efficient acetate production from low-cost syngas using nascent biological gas to liquids (BIO-GTL) technologies. Our analysis suggests that research should focus on overcoming the technical challenges of methane assimilation and/or yield of acetate via BIO-GTL to take advantage of low-cost natural gas rather than using methanol as a feedstock.

Supplementary data are available at Bioinformatics online.