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 co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using 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.
Highlights
Recent years have seen several advances in the usage of bioprocessing to produce a wide range of chemicals (Erickson et al, 2012)
We have extended the Flux Scanning based on Enforced Objective Flux (FSEOF) (Choi et al, 2010) algorithm to study co-production
We identified the intervention strategies required to optimize the co-production of metabolites in both aerobic and anaerobic conditions
Summary
Recent years have seen several advances in the usage of bioprocessing to produce a wide range of chemicals (Erickson et al, 2012). Microorganisms can produce diverse and complex products from simple carbon sources. There are many challenges in designing economically feasible bioprocesses. The advancements in synthetic biology have enabled the metabolic engineering of organisms to improve yield and productivity (Yadav et al, 2019). Various computational strain design algorithms have been developed to identify the genetic manipulations required to overproduce a single product (Burgard et al, 2003; Rocha et al, 2008; Yang et al, 2011). Despite the increase in yield achieved through such rational strain design, the bioprocesses are unable to compete
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