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Digital Chemical Engineering
Volume 2, 2022, 100011

An MIQP framework for metabolic pathways optimisation and dynamic flux analysis

Lucas Gerken-Starepravoa, Xianfeng Zhub, Bovinille Anye Choa, Fernando Vega-Ramona, Oliver Penningtona, Ehecatl Antonio del Río-Chanonac

Department of Chemical Engineering and Analytical Science, University of Manchester, the Mill, Sackville Street, Manchester M1 3AL, UK.


Dynamic flux analysis methods have been widely used for deciphering complex metabolic fluxes transients. However, many of them require frequent experimental measurements and are ineffective in dealing with under-determined metabolic reaction networks. In this study, we addressed these challenges by (i) integrating a macroscale kinetic model with its dynamic metabolic flux model to enable flux simulation over the entire time course for batch operation, and (ii) constructing a single-level mixed-integer quadratic program (MIQP) to automatically identify the shortest metabolic pathways from substrate inflow to biosynthesis of biomass and desired bioproducts. To demonstrate the advantages of the proposed framework, a X. dendrorhous fermentation process for astaxanthin production was utilised as the case study. It is found that the current framework is able to efficiently identify essential pathways and reduce the size of the original metabolic network by 70% with negligible computational cost. Furthermore, the modelling consistency, robustness, and limitation of this framework were thoroughly investigated. This research provides a new avenue for efficient in-silico design, analysis, and gene knockout of microbial strains for bioproduct synthesis.

Keywords: Dynamic flux analysis, Metabolic network simulation, Mixed-integer quadratic programming (MIQP), Pathways selection, Yeast astaxanthin production.

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