Adaptive and Bioinstructive Materials Systems (Topic 3)
Research in this topic strives to develop and integrate novel chemistries, materials and cellular systems to establish basic enabling technologies for the design of future biohybrid systems. The focus is on information-based development of adaptive and bioinstructive materials systems.
Research and systematic development is grouped in three areas:
Materials Chemistry develops and provides rationally designed small molecules, polymer systems, as well as colloidal and nanoparticle systems with accompanying surface functionalization and bioconjugation chemistries. This enables the fabrication of novel adaptive and bioinstructive surfaces and compartments with cross-scale interfaces. The Compound Platform (ComPlat) supports the entire workﬂow from design, (automated) synthesis, analysis and management of small molecular entities, and the Soft Matter Synthesis Laboratory (SML) supports polymeric structures and serves as an analytical platform for soft materials.
In order to study in detail the interaction between living cells/tissues and materials Engineered Biointerfaces uses the chemistry described above to design tailored biointerfaces to connect technical structures to biological molecules, individual cells or populations of eukaryotic and/or microbial cells. In parallel, analysis and data processing methods will be developed to provide the tools required for the comprehensive characterization and description of cell-material hybrid systems.
Integrated units for applications in red and white biotechnology are assembled in Biohybrid Materials Systems exploiting the fundamental biological principles of self-assembly, compartmentalization and self-organization. Exploratory biology and creative engineering will be combined for the development of biomimetic compartments and functional devices. Applications include cell reactors for therapeutic treatments, organ-on-a-chip systems, bioreactors for the expansion of stem cells, biofilm engineering and flow-biocatalysis. These approaches are accompanied by simulation and modeling to create digital twins for e.g. predicting the performance of bioreactors.