Physics-based, data-driven cell-scale membrane simulations with HMFF

Simulating entire cells represents the next frontier of computational biology. Achieving this goal requires methods that accurately describe cellular membranes across spatial and temporal scales. Although three-dimensional electron microscopy enables detailed membrane visualization, limitations on acquisition geometry, data quality, and field-of-view often result in fragmented membrane representations incompatible with simulations. To resolve this, here we introduce Helfrich Monte Carlo Flexible Fitting (HMFF), an approach that integrates experimental density data into physical simulations to determine membrane structure. Through the accompanying Mosaic software platform, we apply HMFF to influenza virus particles, Mycoplasma pneumoniae cells, and entire eukaryotic organelles. The resulting models enable multi-scale simulations spanning millions of lipids and proteins at experimentally determined positions, support quantitative morphological analysis, assess uncertainty in membrane localization, and reveal physical effects implicit in the data. Together, these capabilities establish a foundation for data-driven whole-cell simulations.