4 | CONCLUSION
Oleochemical-producing microbes with arbitrary, user-defined product
profiles remain an elusive goal of metabolic engineering (Marella et
al., 2018; Sharma and Yazdani, 2021; Yan and Pfleger, 2019). The
activities and substrate specificities of critical enzymes are difficult
to predict (or change), and their integration into new pathways
typically requires multiple rounds of iteration; titer and product
profile, in particular, tend to be nonintuitively coupled (Greenhalgh et
al., 2021; Grisewood et al., 2017; Sarria et al., 2018). In this study,
we developed a set of kinetic models that facilitates the design and
analysis of oleochemical pathways. These models provide good fits to
experimental data and can capture the effects of previously reported
pathway modifications. The final models enable mechanistic studies of
unexpected phenomena (e.g., the inhibition of FAEE biosynthesis by TesA
overexpression) and facilitate large, system-wide analyses that are
experimentally intractable. Our sensitivity analysis, for example,
allowed us to probe the influence of isolated shifts in protein
concentration within a broad set of pathways. Findings indicate that
product profiles are most sensitive to concentrations of FabB, FabF, and
acyl-ACP thioesterase (when present) and indicate that coordinated
shifts in these concentrations can adjust the product profiles of
pathways with promiscuous enzymes downstream of the FAS.
Oleochemical-specific enzymes, in turn, primarily influence total
production, an indication that their overexpression can improve titer
without altering final product profiles. These observations provide
guidance for optimizing enzyme expression levels in oleochemical
pathways.
The incorporation of different oleochemical pathways into our FAS model
allowed us to refine general rules for characterizing pathway enzymes.
Results indicate that substrate-specific Michaelis-Menten parameters are
sufficient to capture the activities of a broad set of enzymes. These
parameters rely on steady-state assumptions that are reasonable during
oleochemical production in stationary phase. Importantly, our consistent
parameterization of enzymes between pathways suggests that our models
can capture their activities in different biochemical contexts.
Enzyme-specific kinetic parameters are most accurately measured within vitro kinetic assays (e.g., the CAR-based alcohol pathway),
but our results indicate that in vivo product profiles that
differ in the identity or expression level of individual enzymes can
also support reasonable estimates (e.g., alternative thioesterases).
Finally, protein expression levels, which can vary between strains (Monk
et al., 2016), are important for accurate and consistent kinetic models.
The regular use of proteomics to measure protein concentrations in
engineered strains could improve model accuracy and help explain
strain-to-strain variability in oleochemical pathways.
The paper concludes by developing a gui to facilitate the rapid
implementation of our models. This gui is a useful tool for exploring
the influence of various pathway modifications on final product profiles
and could guide experimental studies (e.g., promoter selection or
configuration). It complements the complete model files, which enable
detailed analyses of oleochemical production (e.g., plots of various
metabolites or analyses of tradeoffs between objectives) with minor
changes in code or short optimization routines. Together, the gui and
its core models provide a versatile kinetic framework for studying
oleochemical biosynthesis.