Scientific publications and related contributions
We consider it an obligation to share our expertise, findings and also to provide access to this information, contributing to the growth in scientific knowledge. Therefore, we use as many formats as possible to achieve the best reach.
Peer-reviewed research articles
Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments (12/2021)
To reduce the experimental burden in mammalian bioprocess development we investigated the potential of hybrid models. To evaluate the transferability across scales, a hybrid model was developed with shake flask DoE data and applied to 15 L stirred tank bioreactor cultivations. Additionally, we investigated the applicability of intensified design of experiments (iDoE) to cover a design space with fewer experiments.
Hybrid modeling reduces experimental effort to predict performance of serial and parallel single-pass tangential flow filtration (12/2021)
Single-pass Tangential Flow Filtration (SPTFF) is a fully continuous alternative to TFF. In this publication, we show that hybrid models trained on a single TFF experiment reliably predict the performance of various modes of SPTFF with varying number of membranes. Our findings allow for minimal product wastage during process development and enable Digital Twins to expand the gained knowledge to multiple filtration types.
Proton-transfer-reaction mass spectrometry (PTR-MS) for online monitoring of glucose depletion and cell concentrations in HEK 293 gene therapy processes (11/2021)
Advanced online sensors for real-time monitoring of bioprocesses are promising tools with high potential to enhance process understanding and transparency. We investigated the capability of such a process analyzer, proton-transfer-reaction mass spectrometry (PTR-MS), for the exhaust gas in HEK293 cultivations. Herein, we developed a cell density soft sensor and identified a sensitive online indicator for glucose depletion, which can be used to set up new process control strategies to increase consistency.
Digital Twin Application for Model-Based DoE to Rapidly Identify Ideal Process Conditions for Space-Time Yield Optimization (06/2021)
Reducing practical experiments and rapidly finding the optimum process conditions for protein production are of high interest. We demonstrated how hybrid models based on a small set of experiments can be applied as Digital Twins. The derived simulations recommended further experiments to be performed to gain confidence about the best conditions in the design space.
Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration (12/2020)
The hybrid model for TFF was expanded to model multiple product components to include the influence of impurities on the process performance. We showed that the presented model can predict complex interactions and outperforms well-known mechanistic models.
Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization (06/2020)
To significantly reduce the required number of experiments for upstream process characterization, we highlight the combined concept of hybrid modeling and intensified design of experiments. Herein, we demonstrate a reduced experimental workload by more than 66%, saving time, raw materials and goods.
Hybrid modeling of cross-flow filtration: Predicting the flux evolution and duration of ultrafiltration processes (05/2020)
In this publication, we present a new hybrid model structure to predict the duration of crossflow ultrafiltration. We highlight the advantages of this approach compared to the film theory, show how it predicts batch and fed-batch filtrations and its use as a digital twin to evaluate the influences of various process parameters on ultrafiltration processes.
Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization (02/2020)
To outline the limitations and shortcomings of state of the art modeling techniques, we performed an extensive DoE study and compared the well-established response surface and black-box model methodologies with more advanced hybrid modeling. We demonstrate that hybrid models are superior to these techniques and possess advantageous features for implementing advanced process control tools.
Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations (10/2019)
In this publication, we present the complete workflow to develop an accurate biomass soft sensor, from process data collection to implementing the final model. By the additional use of an advanced 2D-fluorescence sensor, a deeper examination of the cells' metabolism was possible, facilitating deeper process understanding.
The shortcomings of accurate rate estimations in cultivation processes and a solution for precise and robust process modeling (09/2019)
Herein, we deal with the established but disadvantageous state of the art techniques to calculate specific rates in upstream processes. Consecutively, we present a highly precise and robust method, which is not susceptible to analytical errors, enabling batch to batch comparability and closer process investigation.
Other scientific contributions
Beyond Purely Data-Driven Strategies for Efficient Knowledge Management in the Process Industries (09/2019)
This article provides an overview of the current situation in the biopharmaceutical industry and gives an understanding of the advantages of incorporating data and process knowledge to achieve the highest possible benefits for the business.
Bioprocess Characterization: What’s the Fuss? (06/2019)
Dealing with bioprocess characterization, Mark provides a comprehensive overview of the necessity and importance of this process task but also shortcomings incorporated in the current implementation, e.g., a high number of required experiments to gain sufficient process knowledge. To overcome these, implemented features of the Novasign toolbox, such as hybrid modeling and digital twin applications as well as intensified DoE, can be applied.
Hybrid Modeling and Intensified DoE Enabling Faster Process Development, Soft Sensors and Model Predictive Control
Mark presented how to efficiently combine process knowledge with process data into one beneficial hybrid model structure for bioprocess development. On the basis of our use cases, he pinpoints the huge saving of time, and how to use these models for soft-sensing and model predictive control for both up- and downstream.
Empowering Artificial Intelligence and Process Knowledge using Hybrid Models to speed up Bioprocess Development significantly
In the course of this webinar, Mark presents the advantages of combining process data and process knowledge into one beneficial hybrid model structure for bioprocess development. On the basis of our use cases, he pinpoints the huge saving of time and thereto related expenses.
Soft Sensors for Bioprocess Monitoring
In this interview with the editor of the BioProcess International magazine, addressing intelligent biomanufacturing, Benjamin talks about soft sensors, their application area, implementation as well as current limitations and how to overcome these.