435404 Development of Kinetic Models of Metabolism for Microbial Production

Monday, November 9, 2015: 9:06 AM
155A (Salt Palace Convention Center)
Ali Khodayari1, Satyakam Dash2, Matthew Theisen3, Ahsanul Islam4, Yuting Zheng4, James C. Liao5, Gregory Stephanopoulos6 and Costas D. Maranas7, (1)Chemical Engineering, The Pennsylvania State University, University Park, PA, (2)CHE, PSU, State College, PA, (3)Chemical and Biomolecular Engineering, UCLA, Los Angeles, CA, (4)CHE, MIT, Cambridge, MA, (5)Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, (6)Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, (7)Department of Chemical Engineering, The Pennsylvania State University, University Park, PA

Development of kinetic models of metabolism for microbial production

Ali Khodayari1 (auk241@psu.edu), Satyakam Dash1 (sud25@psu.edu), Matthew K. Theisen3, M. Ahsanul Islam2, Yuting Zheng2, James C. Liao3, Gregory Stephanopoulos2, and Costas D. Maranas1

1The Pennsylvania State University, University Park; 2Massachusetts Institute of Technology; 3University of California, Los Angeles.

Improving the accuracy of in silico metabolic models is the chief challenge for accurately identifying the metabolic drivers that underpin the production of biofuels and chemicals of interest. Despite several advances, stoichiometry-based approaches are limited to steady-state conditions, and generally are unable to describe the metabolic phenotype in terms of the underlying pool of metabolite concentrations and enzyme abundances. While kinetic models have a potential to address these shortcomings, their constructions are still plagued by a number of challenges, including the paucity of kinetic parameters, diversity of rate laws, and implementation of regulatory events. The Ensemble Modeling (EM) paradigm [1] has addressed some of these challenges by decomposing reactions into their elementary steps thus enabling systematic integration of substrate-level regulatory interactions. Recently, we have constructed a kinetic model of E. colicore metabolism including 138 reactions, 93 metabolites, and 63 regulatory interactions  under aerobic growth condition with glucose as the sole carbon source [2].

Here, we expand the core kinetic model towards genome-scale by integration of all relevant reactions from the iAF1260 model. Model parameterization is performed using experimental data for multiple carbon substrates (glucose, acetate, and pyruvate) under aerobic respiration and fermentative conditions. In addition, we developed a second generation genome scale model (GSM) for C. thermocellum(iCth446) which includes 446 genes, 598 metabolites, and 637 reactions. The GSM was subsequently used to construct a core metabolic model of the organism’s central metabolism containing 90 reactions and 76 metabolites, with cellobiose as the carbon source under anaerobic growth condition. This core metabolic network spans all major biomass precursors, as well as other chemicals of interest encompassing the cellobiose degradation pathway, glycolysis/gluconeogenesis, the pentose phosphate (PP) pathway, the TCA cycle, major pyruvate metabolism and anaplerotic reactions, alternative carbon metabolism, nucleotide salvage pathway, and 22 substrate level regulatory interactions. The constructed core model serves as the scaffold for building a kinetic model parameterized by using experimentally measured flux data to improve isobutanol production.

1.              Tan Y, Liao JC: Metabolic ensemble modeling for strain engineers. Biotechnology journal 2012, 7(3):343-353.

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

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