Metabolic ensemble modeling for strain engineers.

Previous mathematical modeling efforts have made significant contributions to the development of systems biology for predicting biological behavior quantitatively. However, dynamic metabolic model construction remains challenging due to uncertainties in mechanistic structures and parameters. In addition, parameter estimation and model validation often require designated experiments conducted only for purpose of modeling. Such difficulties have hampered the progress of modeling in biology and biotechnology. To circumvent these problems, ensemble approaches have been used to account for uncertainties in model structure and parameters. Specifically, this review focuses on approaches that utilize readily available fermentation data for parameter screening and model validation. Time course data for metabolite measurements, if available, can further calibrate the model. The basis for this approach is explained in non-mathematical terms accessible to experimentalists. Information gained from such an approach has been shown to be useful in designing Escherichia coli strains for metabolic engineering and synthetic biology.

Reducing the allowable kinetic space by constructing ensemble of dynamic models with the same steady-state flux.

Dynamic models of metabolism are instrumental for gaining insight and predicting possible outcomes of perturbations. Current approaches start from the selection of lumped enzyme kinetics and determine the parameters within a large parametric space. However, kinetic parameters are often unknown and obtaining these parameters requires detailed characterization of enzyme kinetics. In many cases, only steady-state fluxes are measured or estimated, but these data have not been utilized to construct dynamic models. Here, we extend the previously developed Ensemble Modeling methodology by allowing various kinetic rate expressions and employing a more efficient solution method for steady states. We show that anchoring the dynamic models to the same flux reduces the allowable parameter space significantly such that sampling of high dimensional kinetic parameters becomes meaningful. The methodology enables examination of the properties of the model's structure, including multiple steady states. Screening of models based on limited steady-state fluxes or metabolite profiles reduces the parameter space further and the remaining models become increasingly predictive. We use both succinate overproduction and central carbon metabolism in Escherichia coli as examples to demonstrate these results.

Ensemble modeling of hepatic fatty acid metabolism with a synthetic glyoxylate shunt.

The liver plays a central role in maintaining whole body metabolic and energy homeostasis by consuming and producing glucose and fatty acids. Glucose and fatty acids compete for hepatic substrate oxidation with regulation ensuring glucose is oxidized preferentially. Increasing fatty acid oxidation is expected to decrease lipid storage in the liver and avoid lipid-induced insulin-resistance. To increase hepatic lipid oxidation in the presence of glucose, we previously engineered a synthetic glyoxylate shunt into human hepatocyte cultures and a mouse model and showed that this synthetic pathway increases free fatty acid beta-oxidation and confers resistance to diet-induced obesity in the mouse model. Here we used ensemble modeling to decipher the effects of perturbations to the hepatic metabolic network on fatty acid oxidation and glucose uptake. Despite sampling of kinetic parameters using the most fundamental elementary reaction models, the models based on current metabolic regulation did not readily describe the phenotype generated by glyoxylate shunt expression. Although not conclusive, this initial negative result prompted us to probe unknown regulations, and malate was identified as inhibitor of hexokinase 2 expression either through direct or indirect actions. This regulation allows the explanation of observed phenotypes (increased fatty acid degradation and decreased glucose consumption). Moreover, the result is a function of pyruvate-carboxylase, mitochondrial pyruvate transporter, citrate transporter protein, and citrate synthase activities. Some subsets of these flux ratios predict increases in fatty acid and decreases in glucose uptake after glyoxylate expression, whereas others predict no change. Altogether, this work defines the possible biochemical space where the synthetic shunt will produce the desired phenotype and demonstrates the efficacy of ensemble modeling for synthetic pathway design.