(291a) Constructing Predictive Kinetic Models of Metabolism with Transcriptional Regulation

Dash, S. - Presenter, The Pennsylvania State University
Gopalakrishnan, S., The Pennsylvania State University
Foster, C., Rutgers University
Maranas, C. D., The Pennsylvania State University
In this study, we describe the development of metabolic models using the Ensemble Modeling (EM) paradigm coupled with a description of transcriptional regulation for two organisms: 1) C. thermocellum, a cellulolytic microbe and 2) E. coli. For C. thermocellum, we constructed a core kinetic model k-ctherm118 [1], parameterized by simultaneously imposing fermentation yield data in lactate, malate, acetate and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis-Menten kinetic parameters. k-ctherm118 captures significant metabolic changes caused by (i) nitrogen limitation leading to increased yields for lactate, pyruvate and amino acids, and (ii) ethanol stress causing an increase in intracellular ammonia and sugar phosphate concentrations due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. Overall, the C. thermocellum case study demonstrated that the developed kinetic model k-ctherm118 predicts phenotypes under genetic perturbations with a higher degree of accuracy than stoichiometric model as well as provides insight into missing metabolic pathways and regulations. Kinetic models learn metabolic redirections through careful parameterization that aims to recapitulate metabolic responses seen in multiple flux datasets with the aid of 13C-MFA data [2]. A recent study [3] provides a comprehensive metabolic flux characterization of wild-type E. coli and 22 knockouts of enzymes in the upper part of central carbon metabolism, including glycolysis, pentose phosphate pathway and ED pathway. We apply these flux datasets and the EM paradigm to construct a genome-scale kinetic model of E. coli containing 787 model reactions, 674 metabolites and 295 substrate-level regulatory interactions.

Kinetic models simulate genetic perturbations by modifying specific enzyme levels a priori based on the mutant genotype information, which in turn modulates the concentrations of metabolites that the enzyme acts upon. However, the enzyme levels of an organism are also regulated by the available transcriptional machinery (global regulation) as well as by the changes in concentration levels of regulatory metabolites (specific regulation) under any given genetic or environmental perturbation. Thus, we expand the scope of the developed kinetic models by incorporating transcriptional regulatory layer which refines enzyme levels based on a linear combination of log-normalized changes in growth rate (global) and select intracellular metabolite pool (specific) levels. We use transcriptomic datasets that provide genome-wide collections of mRNA levels under genetic and environmental perturbations to estimate the transcriptional regulatory model parameters and validate their statistical significance. The developed transcriptional regulatory model captures the impact of perturbations in key metabolite pools on transcript levels of 85 enzymes spanning glycolysis, amino acid and nucleotide metabolism pathways. Ultimately, the transcriptional regulation integrated kinetic model captures the impact of enzyme level changes on metabolite concentrations as well as the regulation of enzyme levels by metabolite concentration changes increasing prediction fidelity.

1. Dash S, Khodayari A, Zhou J, Holwerda EK, Olson DG, Lynd LR, Maranas CD: Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations. Biotechnol Biofuels accepted.

2. Khodayari A, Zomorrodi AR, Liao JC, Maranas CD: A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Metabolic Engineering 2014, 25:50-62.

3. Long CP, Gonzalez JE, Sandoval NR, Antoniewicz MR: Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism. Metab Eng 2016, 37:102-113.