Abstract
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.
Dr. Ehecatl Antonio del Rio Chanona
Principal Investigator of OptiML
Antonio del Rio Chanona is the head of the Optimisation and Machine Learning for Process Systems Engineering group based in thee Department of Chemical Engineering, as well as the Centre for Process Systems Engineering at Imperial College London. His work is at the forefront of integrating advanced computer algorithms from optimization, machine learning, and reinforcement learning into engineering systems, with a particular focus on bioprocess control, optimization, and scale-up. Dr. del Rio Chanona earned his PhD from the Department of Chemical Engineering and Biotechnology at the University of Cambridge, where his outstanding research earned him the prestigious Danckwerts-Pergamon award for the best PhD dissertation of 2017. He completed his undergraduate studies at the National Autonomous University of Mexico (UNAM), which laid the foundation for his expertise in engineering.