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

Abstract

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 performancemore » 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.« less

Authors:

Garcia, Sergio ;

• Search DOE PAGES for author "Garcia, Sergio"
• Search DOE PAGES for ORCID "0000-0002-3998-986X"

Trinh, Cong T.

• Search DOE PAGES for author "Trinh, Cong T."
• Search DOE PAGES for ORCID "0000-0002-8362-725X"

Publication Date:

Tue Jun 11 00:00:00 EDT 2019

Sponsoring Org.:

USDOE

OSTI Identifier:

1529693

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Published Article

Journal Name:

Processes

Additional Journal Information:

Journal Name: Processes Journal Volume: 7 Journal Issue: 6; Journal ID: ISSN 2227-9717

Publisher:

MDPI AG

Country of Publication:

Switzerland

Language:

English