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

Seed Train Optimization in Microcarrier-Based Cell Culture Post In Situ Cell Detachment through Scale-Down Hybrid Modeling

Growing adherent cells can be complex as their production increases with surface area rather than volume. Our latest research demonstrates successful cell regrowth on microcarriers during scale-up, enhanced by our innovative hybrid modeling approach. We detail how this technique facilitates scaling the seed train from spinner flasks to 2000L single-use bioreactors.  Stay tuned for our next publication on viral vector productivity increase!

Understanding temporal cell behavior is essential for robust bioprocesses, with Intensified Design of Experiments (iDoE) offering an effective method to explore the effects of various input parameters through intra-experimental changes. By applying iDoE to monoclonal antibody production and employing ordinary least squares and hybrid models for data analysis, this approach was validated for process characterization in a Project with Boehringer-Ingelheim. The study demonstrates iDoE’s potential to optimize bioprocess inputs, providing a roadmap for future applications in biopharmaceutical manufacturing using detailed model analyses

Mathematical bioprocess modeling’s success begins with experimental planning, as demonstrated through a comparison of mechanistic and hybrid models applied to a four-dimensional CHO fed-batch process involving 33 experiments. The mechanistic model benefits from prior knowledge, showing less dependency on data partitioning, while the hybrid model, though more data-dependent, achieves higher accuracy across all data partitions. The study emphasizes the importance of strategic experimental planning in both academic and industry settings to enhance process understanding through mathematical modeling.

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

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.

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 in a project with TAKEDA. 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.

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.

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.

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.

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

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.

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.

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.

Magazine articles

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.

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.


From Experiments, Data, Hybrid Models, and Digital Twins. Several Up- and Downstream Success Stories

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.

From Experiments, Data, Hybrid Models, and Digital Twins. Several Up- and Downstream Success… – YouTube


Digital-twin technology often appears in discussions of Bioprocessing 4.0. At the Bioprocessing Summit in August, Maximilian Krippl, PhD, head of process modeling at Vienna-based Novasign, gave a presentation called “How Digital Twins Facilitate the Factories of Tomorrow: Current Obstacles and Solutions for the Biopharma Industry.” To find out more about this area, GEN talked with Krippl about this work.