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Industrial bioprocesses require more efficiency in the product life cycle. The goals are an acceleration of the time to market, lower manufacturing costs and better productivity.  The biotech industry therefore requires new methods to evaluate the large amounts of data already available and to ensure efficient, targeted experimental design and the transfer of knowledge throughout the product life cycle.

AdaMO has the research hypothesis that integrated modelling workflows with basic research-oriented mathematical methods can address the challenges mentioned above. The aim of the project is to achieve a better understanding of the applicability of model-based methods in bioprocess engineering by means of basic research, to adapt existing tools and to implement them in easy-to-use workflows in order to generate, calibrate, verify and implement models and to increase their fields of application and robustness.

AdaMO develops basic scientific methods to transfer experimental data as early as possible into a workflow for model development. The desired result of AdaMo is a programming environment, not a ready-made software product, with implemented single methods represented in workflows, for efficient model development, adaptation and calibration in real-time use. To check the transferability of the workflows, case studies with different data sets, in different scales and with different host organisms, are generated at the TU WIEN and BI RCV and used for verification.

 

PROJECT START

March 2018

 

PROJECT Duration

36 Months

 

Project partners

Institute of Chemical, Environmental and Biological Engineering, TU Wien (Coordinator);

Center for Energy, AIT Austrian Institute of Technology GmbH (Project partner);

Biopharma Process Sciences Austria, Boehringer Ingelheim RCV GmbH & Co KG (Project partner)

 

State funding

Bridge 1, 26th Invitation of tenders, 2017

 

Publications

Bournazou, C., Barz, T., Nickel, D. B., Lopez Cárdenas, D. C., Glauche, F., Knepper, A., & Neubauer, P. (2017). Online optimal experimental re‐design in robotic parallel fed‐batch cultivation facilities. Biotechnology and bioengineering, 114(3), 610-619

Barz, T., Bournazou, M. N. C., Körkel, S., & Walter, S. F. (2016). Real-time adaptive input design for the determination of competitive adsorption isotherms in liquid chromatography. Computers & Chemical Engineering, 94, 104-116

Barz, T., Körkel, S., & Wozny, G. (2015). Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design. Computers & Chemical Engineering, 77, 24-42

 

AIT Contact

Tilman Barz