Research

The development of competitive industrial bioprocesses is crucial to boost biotechnological production, which drives the transition towards a sustainable and circular economy, and the research within the FBM Initiative aims to support this development.

Download publication list - Web of Science - Status March 2024

 

research area

The loss of productivity in large-scale bioprocesses can often be related to physiological changes in the production host. This research area aims to elucidate the physiological response of a production organism to the environmental conditions encountered during a  fermentation process and develop superior cell factories and bioprocesses. Gerd Seibold’s group combines genetically encoded sensors for metabolites and signaling molecules with high-throughput methods to analyze adaptations of bacteria when up- and down-scaling fermentations. This knowledge is the basis to engineer production organisms adapted to the conditions in a large-scale bioprocess.

José Luis Martinez’s group Yeast Biotechnology and Fermentation follows a different approach towards superior production organisms: exploring and exploiting the natural biodiversity and developing bioprocesses with novel cell factories. These are naturally better adapted to the respective fermentation process due to their robustness, less demanding process conditions, and a wider range of possible feedstocks. This research is closely connected with Fermentation Scale-up and Scale-down and with the Automation and High-Throughput micro-Fermentation Unit of DTU Bioengineering’s Fermentation Core.

Research area

When translating a bioprocess from laboratory‐scale to industrial conditions, gradient formation and inhomogeneity can significantly affect the performance of the bioprocess. A better understanding of the interactions between process conditions and cell physiology is needed to develop more robust production hosts. Helena Junicke and coworkers combine applied fermentation studies and computational approaches to investigate the production hosts behavior and design efficient bio-based production systems. This includes the development of sensors and CFD-guided fermentation scaling. This research is closely connected with Fermentation Physiology and Performance and with DTU Chemical Engineering’s Pilot plant. Here, Jakob Kjøbsted Huusom and coworkers are developing Digital Twins of Pilot-Scale Operations because especially digitalization is expected to boost process understanding in real-time and operations.

Research area

Depending on the product, the downstream process can attribute up to 80% to the process operating costs and is therefore an important factor in the design of competitive bioprocesses. Manuel Pinelo and coworkers design and develop solutions for the purification of fermentation products by combining current knowledge with novel techniques. This results in new topologies or hybrid operations, which can contribute to a more efficient, economically viable, and/or environmentally friendly production. Among other aspects, this involves scale-up and scale-down of chromatography, solvent selection for liquid-liquid extraction of small molecules, in-situ product removal, and the investigation of the connection between host selection and engineering with downstream processing. The development of a digital twin for an ion-exchange chromatography separation was initiated in collaboration with Jakob Kjøbsted Huusom and coworkers.

Research area

Research in biomanufacturing is becoming increasingly data intensive. This has led to the active application of AI in various stages of system metabolic engineering. Marjan Mansourvar and her research group Bio Data Science & Digitalization focuses on digital transformation by AI to design optimized bioprocesses with a streamlined, data-driven approach, and digital models of different modules and sub-modules. They are applying new computational techniques to support synthetic biology to store and manage large amounts of biological data and to use powerful machine learning algorithms for enzymes discovery, modelling, simulation, and computational design.