Premium
Constraint‐based strain design using continuous modifications (CosMos) of flux bounds finds new strategies for metabolic engineering
Author(s) -
Cotten Cameron,
Reed Jennifer L.
Publication year - 2013
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.201200316
Subject(s) - metabolic engineering , metabolic network , metabolic flux analysis , biochemical engineering , constraint (computer aided design) , flux (metallurgy) , computer science , flux balance analysis , systems biology , strain (injury) , saccharomyces cerevisiae , synthetic biology , variety (cybernetics) , computational biology , engineering , biology , gene , chemistry , artificial intelligence , metabolism , genetics , organic chemistry , anatomy , endocrinology , mechanical engineering
In recent years, a growing number of metabolic engineering strain design techniques have employed constraint‐based modeling to determine metabolic and regulatory network changes which are needed to improve chemical production. These methods use systems‐level analysis of metabolism to help guide experimental efforts by identifying deletions, additions, downregulations, and upregulations of metabolic genes that will increase biological production of a desired metabolic product. In this work, we propose a new strain design method with continuous modifications (CosMos) that provides strategies for deletions, downregulations, and upregulations of fluxes that will lead to the production of the desired products. The method is conceptually simple and easy to implement, and can provide additional strategies over current approaches. We found that the method was able to find strain design strategies that required fewer modifications and had larger predicted yields than strategies from previous methods in example and genome‐scale networks. Using CosMos, we identified modification strategies for producing a variety of metabolic products, compared strategies derived from Escherichia coli and Saccharomyces cerevisiae metabolic models, and examined how imperfect implementation may affect experimental outcomes. This study gives a powerful and flexible technique for strain engineering and examines some of the unexpected outcomes that may arise when strategies are implemented experimentally.