Figure 3. Biosynthesis of methyl ketones and FAEEs. (A-B) We optimized our first set of models for methyl ketone biosynthesis with simultaneous fits to two datasets: (A) the product profiles and titers of five strains that overexpress a thioesterase (TE), a fatty-acid-CoA ligase (FadD), an acyl-CoA dehydrogenase (FadE), an oxidoreductase (FadB), and an acyl-CoA thioesterase (FadM; (Yan et al., 2020)), and (B) the ratio of C12 fatty acids to C11 methyl ketones for a sixth strain with similar enzymes overexpressed (Goh et al., 2012). (C-D) We optimized models that generate fatty acid methyl esters (FAMEs) and fatty acid ethyl esters (FAEEs) by fitting the product profiles and titers of strains that overexpress (C) an O-methyltransferase (MT; (Sherkhanov et al., 2016)) or (D) both a wax ester synthase (WS) and a TE, (Steen et al., 2010)). Table S9 describes the compositions of the modeled pathways.
well-characterized enzymes (e.g., an alcohol pathway that uses carboxylic acid reductase, or
CAR, and a thiolase-based methyl ketone pathway discussed later). For these pathways, we fit experimental data by adjusting either a single kinetic parameter or multiple enzyme concentrations (but not both). (ii) Pathways with partially characterized enzymes (e.g., the alkane, FAME, and FAEE pathways). Here, we optimized a single substrate-specific kcat and either (a) multiple enzyme concentrations or (b) an overall kcat vector (i.e., a single factor multiplied by all substrate-specific kcats). (iii) Pathways with poorly characterized enzymes (e.g., the β-oxidation-based methyl ketone pathway and several alcohol pathways discussed later). Here, we optimized multiple enzyme-specific kinetic parameters. For the methyl ketone pathway, which is poorly characterized, we used a sensitivity analysis to identify the most influential kinetic parameters before fitting. Overall, the reasonable fits afforded by our models indicate that Michaelis-Menten parameters provide an adequate means of modeling the activities of enzymes from different oleochemical pathways (Figs. 2-3).
We parameterized enzymes consistently across models. For each enzyme, we adjusted kinetic parameters only once, and we maintained consistent concentrations between pathways. For example, when optimizing pathway that generates alcohols via a CAR and an aldehyde reductase (AHR), we changed the concentrations of FabA, FabZ, FabB, and FabF—all core FAS enzymes—and retained these concentrations for all models; this constraint is consistent with the native FAS expression levels exploited in most engineered strains. For heterologously expressed enzymes, by contrast, we occasionally adjusted concentrations. TesA, in particular, is common in engineered strains, but its overexpression relies on different promoters and ribosome binding sites (Barrick et al., 1994; Lozano Terol et al., 2021); variability in its concentration between strains is reasonable. In general, the consistent parameterization of enzymes in different pathways suggests that our models can capture the activities of important pathway enzymes in different biochemical contexts.