Turnover Dependent Phenotypic Simulation: A Quantitative Constraint-Based Simulation Method That Accommodates All Main Strain Design Strategies

The
uncertain relationship between genotype and phenotype can make
strain engineering an arduous trial and error process. To identify
promising gene targets faster, constraint-based modeling methodologies
are often used, although they remain limited in their predictive power.
Even though the search for gene knockouts is fairly established in
constraint-based modeling, most strain design methods still model
gene up/down-regulations by forcing the corresponding flux values
to fixed levels without taking in consideration the availability of
resources. Here, we present a constraint-based algorithm, the turnover
dependent phenotypic simulation (TDPS) that quantitatively simulates
phenotypes in a resource conscious manner. Unlike other available
algorithms, TDPS does not force flux values and considers resource
availability, using metabolite production turnovers as an indicator
of metabolite abundance. TDPS can simulate up-regulation of metabolic
reactions as well as the introduction of heterologous genes, alongside
gene deletion and down-regulation scenarios. TDPS simulations were
validated using engineered Saccharomyces cerevisiae strains available in the literature by comparing the simulated and
experimental production yields of the target metabolite. For many
of the strains evaluated, the experimental production yields were
within the simulated intervals and the relative strain performance
could be predicted with TDPS. However, the algorithm failed to predict
some of the production changes observed experimentally, suggesting
that further improvements are necessary. The results also showed that
TDPS may be helpful in finding metabolic bottlenecks, but further
experiments would be required to confirm these findings.