Sonifying stochastic walks on biomolecular energy landscapes

TL;DR

This paper introduces a novel method to sonify biomolecular free energy landscapes using Markov models, aiming to enhance understanding of complex molecular data through auditory cues alongside visual tools.

Contribution

It presents a new approach to map features of biomolecular energy landscapes to sound, integrating auditory and visual methods for better data interpretation.

Findings

Developed a sonification strategy for free energy landscapes

Enabled simultaneous auditory and visual analysis of biomolecular data

Enhanced interpretability of complex molecular simulations

Abstract

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.