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Article

Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

by 1,2 and 1,2,*
1
Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, TN 37996, USA
2
Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
*
Author to whom correspondence should be addressed.
Processes 2019, 7(6), 361; https://doi.org/10.3390/pr7060361
Received: 22 April 2019 / Revised: 29 May 2019 / Accepted: 30 May 2019 / Published: 11 June 2019
(This article belongs to the Collection Principles of Modular Design and Control in Complex Systems)
A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has recently been proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. In our previous study, we mathematically formulated the modular cell design problem based on the multi-objective optimization framework. In this study, we evaluated a library of state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutions for modular cells compatible with many product synthesis modules. Furthermore, the best performing algorithm could provide better and more diverse design options that might help increase the likelihood of successful experimental implementation. We identified key parameter configurations to overcome the difficulty associated with multi-objective optimization problems with many competing design objectives. Interestingly, we found that MOEA performance with a real application problem, e.g., the modular strain design problem, does not always correlate with artificial benchmarks. Overall, MOEAs provide powerful tools to solve the modular cell design problem for novel biocatalysis. View Full-Text
Keywords: modularity; modular design; modular cell; metabolic engineering; metabolic network modeling; constraint-based modeling; multi-objective optimization; multi-objective evolutionary algorithms; MOEA modularity; modular design; modular cell; metabolic engineering; metabolic network modeling; constraint-based modeling; multi-objective optimization; multi-objective evolutionary algorithms; MOEA
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MDPI and ACS Style

Garcia, S.; Trinh, C.T. Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis. Processes 2019, 7, 361. https://doi.org/10.3390/pr7060361

AMA Style

Garcia S, Trinh CT. Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis. Processes. 2019; 7(6):361. https://doi.org/10.3390/pr7060361

Chicago/Turabian Style

Garcia, Sergio, and Cong T. Trinh. 2019. "Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis" Processes 7, no. 6: 361. https://doi.org/10.3390/pr7060361

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