The ARC Digital Bioprocess Development Hub will address key bioprocessing research challenges and develop new process and digital models that can predict and optimise manufacturing processes, resulting in greater yields, faster and more flexible processes and enhanced product stability.
The Hub is a 5-year collaborative program of significant scope and scale with contributions from all partners of more than $18M (cash and in kind). This includes $5M of funding awarded in 2021 by the Commonwealth Government through the ARC’s Industrial Transformation Research Program.
The Hub builds upon the Faster, Smarter Pharma and Food Manufacturing program at The University of Melbourne (2021-2022) funded through the Victorian Higher Education State Investment Fund.
The ARC Digital Bioprocess Development Hub will undertake research which aims to transform Australia’s biopharmaceutical manufacturing industry by increasing digital innovation, productivity and competitiveness through the development of digitally integrated advanced manufacturing processes and a platform for industry adoption which will target rapid translation and up-take of sector-focused outputs.
A series of 10 projects led by a total of 11 chief investigators will be conducted across two research themes – Bioprocessing Research and Digital Processing Research.
A substantial team draws together expertise from The University of Melbourne, University of Technology Sydney and RMIT University, together with partner organisations CSL Innovation, Cytiva (Global Life Sciences Solutions Australia) and Patheon Biologics Australia. Three other organisations (Yokogawa Insilico Biotechnology, Mass Dynamics and Sartorius) and three leading international universities (University of Tartu, Estonia; Utrecht University, Netherlands; The University of Nottingham, United Kingdom) are also participants of the Hub.
The Hub will be led by Professor Sally Gras of the University of Melbourne. Professor Gras and her research group from the Department of Chemical Engineering are located at the Bio21 Institute in Parkville. Professor Uwe Aickelin of Computing and Information Systems also at the University of Melbourne is the Hub Deputy Director and will lead the digital research. Other CIs at the University of Melbourne include Prof Sandra Kentish, Prof Marcello La Rosa, Prof Michael Kirley, Prof Greg Martin, Assoc Prof Artem Polyvyanyy, Dr Abel Armas Cervantes and Dr Ling Luo.
Input from the University of Technology Sydney will be led by CI Professor Bogdan Gabrys from the Data Science Institute. Dr Celine Valery (CI) will lead the work undertaken by RMIT University within the School of STEM, Health and Biomedical Sciences.
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Our Growing Team
Current PhD Opportunities
The ARC Digital Bioprocess Development Hub was launched on 9 June 2023 by University of Melbourne Deputy Vice-Chancellor (Research) Professor James McCluskey and ARC Acting CEO Dr Richard Johnson with Hub participants and representatives of the sector. This marks the start of a five-year program that will employ 8 postdoctoral research fellows and 3 research assistants and engage over 13 PhDs and more than 50 Masters internship students.
Back row, left to right: Dr Anthony Stowers (CSL Innovation), Kym Baker (Patheon by Thermofisher), James Atherton (Cytiva), Prof Bogdan Gabrys (University of Technology Sydney), Dr Celine Valery (RMIT University), Matthias Zimmermann (CSL Innovation), Front row, left to right: Professor James McCluskey, Professor Sally Gras, Dr Richard Johnson
(Absent: Prof Uwe Aickelin)
Yuan Sun, Winton Nathan-Roberts, Tien Dung Pham, Ellen Otte, Uwe Aickelin (2022) Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data, https://arxiv.org/abs/2211.14493
Masih Karimi Alavijeh, Irene Baker, Yih Yean Lee, Sally L. Gras (2022) Digitally enabled approaches for the scale up of mammalian cell bioreactors, Digital Chemical Engineering, https://doi.org/10.1016/j.dche.2022.100040
Chaitanya Manapragada, Tien Dung Pham, Nikitaa Rajan, Uwe Aickelin (2023) Pharmaceutical process optimisation: Decision support under high uncertainty, Computers and Chemical Engineering, https://doi.org/10.1016/j.compchemeng.2022.108100
Tien Dung Pham, Chaitanya Manapragada, Yuan Sun, Robert Bassett, Uwe Aickelin (2023) A scoping review of supervised learning modelling and data-driven optimisation in monoclonal antibody process development, Digital Chemical Engineering, https://doi.org/10.1016/j.dche.2022.100080
Yiran Qu, Innocent Bekard, Ben Hunt, Jamie Black, Louis Fabri, Sally L. Gras & Sandra E. Kentish (2023) The Transition from Resin Chromatography to Membrane Adsorbers for Protein Separations at Industrial Scale, Separation & Purification Reviews, https://doi.org/10.1080/15422119.2023.2226128
Tien Dung Pham, Robert Bassett, Uwe Aickelin (2023) Capturing prediction uncertainty in upstream cell culture models using conformal prediction and Gaussian processes, Proceedings of Machine Learning Research 204:1–3, https://proceedings.mlr.press/v204/pham23a/pham23a.pdf
Yiran Qu, Innocent Bekard, Ben Hunt, Jamie Black, Louis Fabri, Sally L. Gras, Sandra. E. Kentish (2024) Economic optimization of antibody capture through Protein A affinity nanofiber chromatography, Biochemical Engineering Journal, https://doi.org/10.1016/j.bej.2023.109141
Yiran Qu, I. Baker, Jamie Black, Louis Fabri, Sally L Gras, Abraham Lenhoff, Sandra E Kentish (2024) Application of Mechanistic Modelling in Membrane and Fiber Chromatography for Purification of Biotherapeutics— A review, Journal of Chromatography A, https://doi.org/10.1016/j.chroma.2023.464588
Thanh Tung Khuat, Robert Bassett, Ellen Otte, Alistair Grevis-James, Bogdan Gabrys (2024) Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities, Computers and Chemical Engineering, https://doi.org/10.1016/j.compchemeng.2024.108585
Bhagya S. Yatipanthalawa,Sally L. Gras (2024) Predictive models for upstream mammalian cell culture development - A review, Digital Chemical Engineering, https://doi.org/10.1016/j.dche.2023.100137
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