Sonifying Stochastic Walks on Biomolecular Energy Landscapes

Abstract

Translating the complex, multi-dimensional data produced by simulations of biomolecules into an intelligible form 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. This allows for concurrent visual display of the physical configuration of a biomolecule and auditory display of characteristics of the corresponding free energy landscape. The resulting sonification provides information about the relative free energy features of a given configuration including its stability.