BVT at the ACS Spring 2022 National Meeting & Exposition

The biannual national meeting of the American Chemical Society (ACS) is covering the complete field of chemistry. It gives scientific professionals a platform to present, publish, discuss, and exhibit the most exciting research discoveries and technologies in chemistry and its related disciplines. The meeting will facilitate networking opportunities, career development and placement, and provide companies an opportunity to exhibit products and services to a targeted audience. The spring meeting is focussing on the topic: Bonding Through Chemistry

Marie-Therese Schermeyer from the Chair of Bioprocess Engineering at the TU Berlin, a member of the Bio-PAT Network will present will talk on the 23.03.22 at 5:20 pm about Artificial intelligence meets upstream process development (Abstract see below). The lecture takes places in the Pacific Ballroom: Section 18 (Marriott Marquis San Diego Marina).

The event is planned as a hybrid event, with in-person and virtual components.

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Abstract to the talk „Artificial intelligence meets upstream process development“:

The development of a biotechnological process is time-consuming and cost-intensive. If we want to produce biotechnological products as environmentally friendly alternatives to chemically produced materials and reduce production costs of high-value biopharmaceuticals, the development process must become more efficient. This can only be achieved through the integration of data-based models and the associated automation of process steps. The KIWI-biolab (Artificial Intelligence for Knowledge-Based Integrated Biolabs) – brings international top scientists in artificial intelligence, machine learning and bioprocess engineering together to explore the opportunities and challenges of automation in upstream bioprocesses. As a cross-disciplinary team, with High-Throughput labs at the TU Berlin and the University in Greifswald, we are working on the optimal integration of AI and ML technologies to automate the complex data analysis and standardization of biotechnological processes. With the goal of enabling computers to design and optimize experiments without human intervention.
In the presented case study, we show which and how data driven models can be used for upstream process development steps and how they can be linked with already integrated mechanistic models to achieve optimization in real time. In order to fully utilize the power of the models, they are linked to a data management system of the High-Throughput facilities. The data management system handles the automated planning of the experiment, the storage of all collected process and metadata and the manipulation of the experiment in a closed loop operation environment (LARA – gitlab.com/larasuite) . Experimental planning, execution and evaluation meet the F.A.I.R data principles. With that we aim to create a F.A.I.R data pipeline by generating and using open (source) data and communication standards.
The case study presented is a step towards our long-term goal of making advances in bioprocess automation for more efficient and sustainable production.

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