Computer simulations have long been used to provide a conceptual framework to understand biophysical phenomena. Because of the high degree of complexity arising from the large number of interacting components, they constitute an essential class of tools in interpreting experimental studies, connecting them to fundamental physics, organizing our knowledge and asking new questions based on an ever improving picture.

While data-based models have provided powerful tools to generate hypotheses of biomolecular structures and interactions, current AI-models provide neither full transferability nor insights regarding emerging complex phenomena like structure formation in molecular assemblies from the  known laws of physics.

A physics based model of biomolecular processes in contrast is fully transferable and allows to zoom in to the level of individual atomic interactions to make sense of, for instance, protein folding and the interaction of proteins with their environment under different physical conditions. It can be directly used to explain the behaviour of intrinsically disordered proteins, as well as supra-molecular assembly processes such as peptide aggregation and coacervation.

Such processes typically involve thousands of interacting entities, complex interactions and inconveniently long time scales. Markov-chain Monte Carlo provides an underexplored tool to circumvent the time scale barrier characteristic for Molecular Dynamics. The workshop will introduce MCMC based simulation compare it to MD, and show ways to combine both methods. This will enable researchers to circumvent the limitations of the individual methods and to extract useful insights over a large span of spatial and temporal dimensions.