Abstract
Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.
Highlights
BackgroundThe standardization and automation of genome characterization and editing techniques has been accompanied by a rapid increase in the number of prokaryotic and eukaryotic microbial organisms available for engineering for overproduction of target commodity metabolites
Kinetic models of metabolism have re-emerged as a means for characterizing metabolism, offering improvements over their stoichiometric counterparts in both the type of information that can be gleaned from them, and in prediction accuracy
A lack of raw experimental data needed for flux elucidation and, subsequently, kinetic parameterization, and high computation cost have prevented the development of a uniform workflow for construction of the most informative kinetic models
Summary
BackgroundThe standardization and automation of genome characterization and editing techniques has been accompanied by a rapid increase in the number of prokaryotic and eukaryotic microbial organisms available for engineering for overproduction of target commodity metabolites. With annotated genomes and CRISPR-Cas toolboxes consolidated in organism-specific biofoundries [1,2,3,4,5], the demand for biologically robust genetic intervention strategies for target metabolite overproduction has increased. This has created a need for a standardized computational workflow capable of reliably predicting phenotype based on genetic intervention strategies. Traditional stoichiometric models of metabolic networks and integer programming design algorithms such as OptKnock [6] have provided insight into metabolic state as a function of genetic perturbation These tools provide information on how an organism may behave under a specific genetic condition. Mass action [18,19,20], S-system [21, 22], and log-
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