Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation |
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| Authors: | Adam L Meadows Rahi Karnik Harry Lam Sean Forestell Brad Snedecor |
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| Institution: | 1. Chemical and Biological Engineering;2. Late Stage Cell Culture, Process R&D Genentech, Inc. 1 DNA Way, MS 32 South San Francisco CA 94080;1. KU Leuven, BioTeC – Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, Gebroeders De Smetstraat 1, 9000 Gent, Belgium;2. OPTEC – Center of Excellence: Optimization in Engineering, Department of Chemical Engineering, Gebroeders De Smetstraat 1, 9000 Gent, Belgium;1. Institute for Automation Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany;2. Department of Mathematics, Imperial College London, SW7 2AZ London, United Kingdom;1. Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, United States;2. Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, WI 53706, United States;1. Oregon State University, United States;2. USDA, ARS, NCAUR, United States;1. Chemical Engineering, Stanford University, Stanford, CA 94305, USA;2. Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA;3. Bioengineering, Stanford University, Stanford, CA 94305, USA |
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| Abstract: | We have developed a reactor-scale model of Escherichia coli metabolism and growth in a 1000L process for the production of a recombinant therapeutic protein. The model consists of two distinct parts: (1) a dynamic, process specific portion that describes the time evolution of 37 process variables of relevance and (2) a flux balance based, 123-reaction metabolic model of E. coli metabolism. This model combines several previously reported modeling approaches including a growth rate-dependent biomass composition, maximum growth rate objective function, and dynamic flux balancing. In addition, we introduce concentration-dependent boundary conditions of transport fluxes, dynamic maintenance demands, and a state-dependent cellular objective. This formulation was able to describe specific runs with high-fidelity over process conditions including rich media, simultaneous acetate and glucose consumption, glucose minimal media, and phosphate depleted media. Furthermore, the model accurately describes the effect of process perturbations—such as glucose overbatching and insufficient aeration—on growth, metabolism, and titer. |
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