467184 Efficient Estimation of Maximum Theoretical Productivity from Batch Cultures Via Dynamic Optimization of Flux Balance Models

Sunday, November 13, 2016: 3:30 PM
Carmel II (Hotel Nikko San Francisco)
Peter St. John, Michael F. Crowley and Yannick J. Bomble, Biosciences Center, National Renewable Energy Laboratory, Golden, CO

Production of chemicals from engineered organisms in a batch culture typically involves a trade-off between productivity, yield, and titer. However, strategies for strain design typically involve designing mutations to achieve as high of a yield as possible while maintaining growth viability. Such approaches tend to follow the principle of designing static networks with minimum metabolic functionality to achieve desired yields. While these methods are computationally tractable, optimum productivity is likely achieved by a dynamic strategy, in which intracellular fluxes change their distribution over time. Previous work has investigated using multi-stage fermentations to increase either productivity or yield. Existing methods, however, have tended to assume an initial control strategy (i.e., a single reaction target) in maximizing productivity - but it is unclear how close this productivity comes to a global optimum. The calculation of maximum theoretical yield is well established in the metabolic engineering literature, and its use helps guide strain and pathway selection for static strain design efforts. In this talk, we present a novel method for the calculation of a maximum theoretical productivity of a batch culture system using a dynamic optimization framework. This method follows the traditional assumptions of dynamic flux balance analysis: that internal metabolite fluxes are governed by a pseudo-steady state and external metabolite fluxes are represented by dynamic system including Michealis-Menten or hill-type regulation. The productivity optimization is achieved via collocation on finite elements, and accounts explicitly for an arbitrary number of fermentation stages and flux variable changes. Solution of the dynamic program is accomplished via a large-scale nonlinear programming solver. We have applied our method to succinate production in two common microbial hosts: E. coli and Actinobacillus succinogenes. The method can be further extended to calculate the complete productivity versus yield Pareto surface. Our results demonstrate that nearly optimal yields and productivities can indeed be achieved with only two discrete flux stages.

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See more of this Session: Dynamic Simulation and Optimization
See more of this Group/Topical: Computing and Systems Technology Division