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Efficient estimation of the maximum metabolic productivity of batch systems

Overview of attention for article published in Biotechnology for Biofuels and Bioproducts, January 2017
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Title
Efficient estimation of the maximum metabolic productivity of batch systems
Published in
Biotechnology for Biofuels and Bioproducts, January 2017
DOI 10.1186/s13068-017-0709-0
Pubmed ID
Authors

Peter C. St. John, Michael F. Crowley, Yannick J. Bomble

Abstract

Production of chemicals from engineered organisms in a batch culture involves an inherent trade-off between productivity, yield, and titer. Existing strategies for strain design typically focus on designing mutations that achieve the highest yield possible while maintaining growth viability. While these methods are computationally tractable, an optimum productivity could be achieved by a dynamic strategy in which the intracellular division of resources is permitted to change with time. New methods for the design and implementation of dynamic microbial processes, both computational and experimental, have therefore been explored to maximize productivity. However, solving for the optimal metabolic behavior under the assumption that all fluxes in the cell are free to vary is a challenging numerical task. Previous studies have therefore typically focused on simpler strategies that are more feasible to implement in practice, such as the time-dependent control of a single flux or control variable. This work presents an efficient method for the calculation of a maximum theoretical productivity of a batch culture system using a dynamic optimization framework. The proposed method follows traditional assumptions of dynamic flux balance analysis: first, that internal metabolite fluxes are governed by a pseudo-steady state, and secondly that external metabolite fluxes are dynamically bounded. The optimization is achieved via collocation on finite elements, and accounts explicitly for an arbitrary number of flux changes. The method can be further extended to calculate the complete Pareto surface of productivity as a function of yield. We apply this method to succinate production in two engineered microbial hosts, Escherichia coli and Actinobacillus succinogenes, and demonstrate that maximum productivities can be more than doubled under dynamic control regimes. The maximum theoretical yield is a measure that is well established in the metabolic engineering literature and whose use helps guide strain and pathway selection. We present a robust, efficient method to calculate the maximum theoretical productivity: a metric that will similarly help guide and evaluate the development of dynamic microbial bioconversions. Our results demonstrate that nearly optimal yields and productivities can be achieved with only two discrete flux stages, indicating that near-theoretical productivities might be achievable in practice.

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Mendeley readers

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The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 1%
Unknown 67 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 25%
Researcher 12 18%
Student > Master 8 12%
Student > Doctoral Student 6 9%
Student > Bachelor 5 7%
Other 8 12%
Unknown 12 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 24%
Biochemistry, Genetics and Molecular Biology 15 22%
Engineering 8 12%
Chemical Engineering 4 6%
Environmental Science 2 3%
Other 7 10%
Unknown 16 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 January 2017.
All research outputs
#17,289,387
of 25,382,440 outputs
Outputs from Biotechnology for Biofuels and Bioproducts
#996
of 1,578 outputs
Outputs of similar age
#269,316
of 424,113 outputs
Outputs of similar age from Biotechnology for Biofuels and Bioproducts
#32
of 48 outputs
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