| Autor: |
Lucas Gerken-Starepravo, Xianfeng Zhu, Bovinille Anye Cho, Fernando Vega-Ramon, Oliver Pennington, Ehecatl Antonio del Río-Chanona, Keju Jing, Dongda Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Digital Chemical Engineering, Vol 2, Iss , Pp 100011- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2772-5081 |
| DOI: |
10.1016/j.dche.2022.100011 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
