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• W3199355302 abstract "ABSTRACT Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose an eXtended version of Flux Scanning based on Enforced Objective Flux (XFSEOF), identify intervention strategies to co-optimize for a set of metabolites. XFSEOF can be used to identify all pairs of products that can be co-optimized with ease, by a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae , and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses." @default.
• W3199355302 created "2021-09-27" @default.
• W3199355302 creator A5011489319 @default.
• W3199355302 creator A5023997844 @default.
• W3199355302 date "2021-09-20" @default.
• W3199355302 modified "2023-10-16" @default.
• W3199355302 title "A computational framework to identify metabolic engineering strategies for the co-production of metabolites" @default.
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• W3199355302 doi "https://doi.org/10.1101/2021.09.18.460904" @default.
• W3199355302 hasPublicationYear "2021" @default.
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