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. Also, sometimes we use many independent short simulations to sample events locally.

Finally, we apply statistical and machine learning tools to reconstruct the essential dynamical features; such as transition pathways, free energy landscapes, or meta-stable states, capturing the complexity of molecular motion.