Biological cellular function relies on the coordination of many information processing steps in which specific biomolecular complexes are formed. But how can proteins and ligands find their targets in extremely crowded cellular membranes or cytosol? Here, I propose that intracellular signal processing depends upon the spatiotemporal order of molecules arising from “dynamical sorting”, i.e. no stable structure may exist at any time and yet the molecules might be ordered at all times. I propose to investigate the existence and the physicochemical driving forces of such dynamical sorting mechanisms in selected neuronal synaptic membrane proteins that must be tightly coordinated during neurotransmission and recovery. To this end, massive atomistic and coarse-grained molecular simulations will be combined with statistical mechanical theory, producing predictions to be experimentally validated through our collaborators.The main methodological challenge of this proposal is the infamous sampling problem: Even with immense computational effort, unbiased molecular dynamics trajectory lengths are at most microseconds for the protein systems considered here. This is insufficient to calculate relevant states, probabilities, and rates for these systems. Based on recent mathematical and computational groundwork developed by us and others, I propose to develop enhanced sampling methods that will yield an effective speedup of at least 4 orders of magnitude over current simulations. This will allow protein-ligand and protein-protein interactions to be sampled efficiently using atomistic models, thus having far reaching impact on the field.The proposed project is at the forefront of an interdisciplinary field, spanning traditional areas such as physical chemistry, computer science, mathematics, and biology. The project claims fundamental advances in both simulation techniques and in the understanding of the physicochemical principles that govern the functionality of the cell