Computational Biophysics

Computational Biophysics is about understanding biological processes  by building theoretical models and simulating them under a given set of environmental conditions. Ideally, a detailed atomic model and sampling would let us predict the outcomes of the bio-molecular events. But in practice, such exhaustive sampling is computationally expensive. To address this, we often simplify the system, either by building a model at lower resolution model (coarse-grained models) or by focusing on essential variables that capture the system's dynamics. Finally, we apply statistical and machine learning tools to recover the thermodynamic and kinetic information; such as transition pathways, free energy landscapes, or meta-stable states, capturing the complexity of molecular motion.