Abstract

With the aim to improve our understanding of biological interactions involving viruses, vesicles and cells, it is worthwhile to reconstitute them on surfaces. This can facilitate the incorporation of such biological entities in bioanalytical and medical diagnostic devices. Examples of such applications range from organ-on-a-chip to sensing devices for pathogens, viruses, protein-protein interactions, etc. Although much progress has been achieved in the reconstitution of biological interactions on surfaces, it remains a formidable task to additionally incorporate the dynamicity that is found in biological systems in the natural context, for example in (bacterial) cell adhesion, in virus infection and in laterally mobile cell membranes. In such systems, dynamic behaviour often goes hand in hand with multivalent interactions, together providing control over affinity and kinetics. In this thesis, artificial cell membranes in the form of supported lipid bilayers (SLBs) were used to reconstitute biological interactions involving dynamic behavior. In addition to the intrinsic lateral dynamics of biological ligands that can be presented at these SLBs, out-of-plane dynamics of the biological ligands was controlled and studied by the choice of the interaction motifs and/or by tuning the overall valency. To this end, various types of supramolecular interactions were used. Host-guest chemistry was used to bind cells and proteins to surfaces. Tunable surface affinity was achieved by insertion of bioactive peptides with various lipid chains to SLBs and employed to study stem cell adhesion and spreading. The number of metal-ligand interactions between lipid vesicles and the SLB determined the extent of vesicle binding and provided a threshold density for vesicle binding. Finally, carbohydrate-protein interactions were reconstituted on SLBs and used to study virus binding in more detail. Learning from nature to combine dynamics and multivalency is not a straightforward endeavor; however, each of the studies in this thesis can potentially be used to improve the functionality and thus the performance of devices that rely on biological interactions.

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