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  1. Resources
Comparison of strategies for iterative model-based upstream bioprocess development with single and parallel reactor set-ups
Publications · 26. January 2023

Comparison of strategies for iterative model-based upstream bioprocess development with single and parallel reactor set-ups

Full title: Comparison of strategies for iterative model-based upstream bioprocess development with single and parallel reactor set-ups Abstract: The bioprocess industry shows an increased interest to use model-based approaches for upstream bioprocess development. Iteratively, one or multiple experiments need to be designed with the objective to iteratively learn the process behavior and drive it towards a desired state. Due to the inherent dynamic nature of upstream bioprocesses, dynamic...

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How to select the right approach or solution for your digitalization plan?
Bioprocess Blog · 18. January 2023

How to select the right approach or solution for your digitalization plan?

As innovation floods the Biopharma industry, innovation leads have more choices than ever. This fortunate problem creates a new challenge - how to select the best approach?

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Digital with a purpose
Bioprocess Blog · 10. January 2023

Digital with a purpose

Digitalization is the buzzword in Pharma as the industry embraces new technology to create more effective organizations. Before rushing into new projects, pause and take a step back to determine what your goals are.

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Hybrid modeling supported development of an industrial small-molecule flow chemistry process
Publications · 31. December 2022

Hybrid modeling supported development of an industrial small-molecule flow chemistry process

Full title: Hybrid modeling supported development of an industrial small-molecule flow chemistry process Abstract: Continuous manufacturing of small molecules finds increasing application in developing novel processes due to its significant advantages. The development of these processes is increasingly being supported by models, in a quest to reduce the number of experiments and increase insight. With the Quality by Design paradigm in mind, answers to questions anent the impact of changes in...

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Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions
Publications · 15. February 2022

Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions

Full title: Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions Abstract: Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledgeor black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged...

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Hybrid modeling — a key enabler towards realizing digital twins in biopharma?
Publications · 01. December 2021

Hybrid modeling — a key enabler towards realizing digital twins in biopharma?

Full title: Hybrid modeling — a key enabler towards realizing digital twins in biopharma? Abstract: Digital twins (DTs) represent a vividly emerging technology in the manufacturing industry strongly motivated by the goals of industry 4.0. It strives for smart factories with completely interconnected devices and data archives as well as their automated and adaptive operation based on autonomous decisions of smart algorithms. In this article, we want to provide our vision on the broad...

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Design of Biopharmaceutical Formulations Accelerated by Machine Learning
Publications · 14. September 2021

Design of Biopharmaceutical Formulations Accelerated by Machine Learning

Full title: Design of Biopharmaceutical Formulations Accelerated by Machine Learning Abstract: In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced algorithms from the domain of machine learning are highly beneficial in complementing analytical screening and rational design. Here, we propose a Bayesian...

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Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning
Publications · 01. September 2021

Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning

Full title: Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning Abstract: Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture...

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A roadmap to AI-driven in silico process development: bioprocessing 4.0 in practice
Publications · 01. September 2021

A roadmap to AI-driven in silico process development: bioprocessing 4.0 in practice

Full title: A roadmap to AI-driven in silico process development: bioprocessing 4.0 in practice Abstract: In silico process development constitutes a viable option for accelerating CMC development timelines and can be achieved through either a hybrid-modeling-driven or an Artificial Intelligence (AI)-driven avenue. Each pathway has its own pros and cons but the biggest difference is that the former can be developed in-house whereas the latter requires inter-corporation collaboration. Motivated...

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Knowledge transfer across cell lines using hybrid Gaussian process models with entity embedding vectors
Publications · 03. August 2021

Knowledge transfer across cell lines using hybrid Gaussian process models with entity embedding vectors

Full title: Knowledge transfer across cell lines using hybrid Gaussian process models with entity embedding vectors Abstract: To date, a large number of experiments are performed to develop a biochemical process. The generated data is used only once, to take decisions for development. Could we exploit data of already developed processes to make predictions for a novel process, we could significantly reduce the number of experiments needed. Processes for different products exhibit differences in...

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