Instructions for Loging to the Webinar:
Event number: 950 444 757
Event password: nvWyEF3k
Event address for attendees: https://imperialcollegelondon.webex.com/imperialcollegelondon/onstage/g.php?MTID=e51013081a8d5a8e1fbcb45309c086574
Biography: Professor Athanasios (Sakis) Mantalaris is Professor of BioSystems Engineering in the Department of Chemical Engineering at Imperial College London and a member of the Centre for Process Systems Engineering (CPSE). He received his PhD (2000) in Chemical Engineering from the University of Rochester. His expertise is in modelling of biological systems and bioprocesses with a focus on mammalian cell culture systems, stem cell bioprocessing, and tissue engineering. He has published over 160 original manuscripts, co-edited one book, and holds several patents with several more pending. He has received several awards including the Junior Moulton Award for best paper by the Institute of Chemical Engineers (IChemE) in 2004. In 2012, he was elected Fellow of the American Institute for Medical & Biological Engineering and in 2013 he was awarded a European Research Council (ERC) Advanced Award. In 2015, he was awarded the Donald Medal by the Institution of Chemical Engineers (IChemE) for his contributions to biochemical engineering.
Abstract: Currently, design and optimisation of biotechnological bioprocesses is performed either through exhaustive experimentation and/or with the use of empirical, unstructured growth kinetics models. Whereas, elaborate systems biology approaches have been recently explored, utilisation of mixed-substrates is predominantly ignored despite its key significance in robustly enhancing bioprocess performance. In this webinar, the novel experimental-modelling gene regulatory network – growth kinetic (GRN-GK) hybrid framework will be presented. Bioprocess optimisation for an industrially-relevant bioremediation process involving a mixture of highly toxic substrates, m-xylene and toluene, and Pseudomonas putida mt-2 will be demonstrated. The gene regulatory network model captured the transcriptional kinetics of gene expression and informed the formulation of the growth kinetics model replacing the empirical and unstructured Monod kinetics. The GRN-GK framework’s predictive capability and potential as a systematic optimal bioprocess design tool was demonstrated by effectively predicting bioprocess performance, which was in agreement with experimental values, when compared to four commonly used models that deviated significantly from the experimental values. Significantly, a fed-batch biodegradation process was designed and optimised through the model-based control of gene expression (TOL Pr promoter) resulting in 23% and 58% enhanced pollutant removal and biomass formation, respectively, compared to the batch process. This provides the first evidence of model-based bioprocess optimisation at the gene level, rendering the GRN-GK framework as a novel and applicable approach to optimal bioprocess design. Finally, model analysis using global sensitivity analysis (GSA) suggests an alternative, systematic approach for model-driven strain modification for synthetic biology and metabolic engineering applications.
The talk is for one-hour online session: 40 minutes’ presentation + 20 minutes’ Q&A.
Instructions for Loging to the Webinar:
Event number: 950 444 757
Event password: nvWyEF3k
Event address for attendees: https://imperialcollegelondon.webex.com/imperialcollegelondon/onstage/g.php?MTID=e51013081a8d5a8e1fbcb45309c086574