Sonifying stochastic walks on biomolecular energy landscapes
This addresses the problem of interpreting multi-dimensional simulation data for computational chemists and biologists, though it appears incremental as it builds on existing visual techniques.
The authors tackled the challenge of making complex biomolecular simulation data more intuitive by mapping features of the free energy landscape to sonic parameters, using Markov models to design a strategy for sonification alongside visual displays.
Translating the complex, multi-dimensional data from simulations of biomolecules to intuitive knowledge is a major challenge in computational chemistry and biology. The so-called "free energy landscape" is amongst the most fundamental concepts used by scientists to understand both static and dynamic properties of biomolecular systems. In this paper we use Markov models to design a strategy for mapping features of this landscape to sonic parameters, for use in conjunction with visual display techniques such as structural animations and free energy diagrams.