Publications · 15. February 2022

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...

Publications · 01. December 2021

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...

Publications · 14. September 2021

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...

Publications · 01. September 2021

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...

Publications · 01. September 2021

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...

Publications · 03. August 2021

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...

Publications · 02. August 2021

Full title: Hybrid Models for the simulation and prediction of chromatographic processes for protein capture Abstract: The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsolete, and more generalized tools for process development and optimization are required to keep pace with the emerging trends. Thus,...

Publications · 06. July 2021

Full title: Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Capture Chromatographic Step Abstract: In process engineering, two paradigms of modeling approaches exist: the mechanistic and the data-driven approaches with the former being completely based on knowledge while the latter completely based on data. In our previous work, we highlighted the advantages of using hybrid models that explores the synergy between mechanistic and data-driven...

Publications · 01. March 2021

Full title: Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation Abstract: Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of protein sequences and buffer compositions. In this context, artificial intelligence (AI), and especially machine learning...

Publications · 30. December 2020

Full title: Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down Abstract: Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical...