Despite the significant advances, our quantitative understanding of biological function at the molecular and cellular level is still in its relative infancy. Experimental and theoretical approaches to characterize macromolecular dynamics and function have evolved dramatically in the last few decades. However, experiment and computation have co-existed with limited feedback. On one hand simulations can, in principle, resolve details not accessible to experiment, but are based on empirical models and, alone, cannot be quantitatively predictive. On the other hand, a wealth of indirect data on the structure and dynamics of macromolecular complexes is available from thermodynamic and kinetic measurements on parts of the systems of interest, but there is no way to systematically combine these data into a structural model.
Our group works on the definition and implementation of strategies to study complex biophysical processes on long timescales. On one hand, we design multiscale models, adaptive sampling approaches, and data analysis tools that allow to explore large regions of a system's free energy landscape. On the other hand, we work on the theoretical formulation to exploit the complementary information that can be obtained in theory and experiment, to combine the approximate but high-resolution structural and dynamical information from computational models with the “exact” but lower resolution information available from experiments.