Using sound to understand protein sequence data: new sonification algorithms for protein sequences and multiple sequence alignments

Sonification of Genomic Data to Represent Genetic Load in Zoo Populations

Maintaining a diverse gene pool is important in the captive management of zoo populations, especially in endangered species such as the pink pigeon (Nesoenas mayeri). However, due to the limited number of breeding individuals and relaxed natural selection, the loss of variation and accumulation of harmful variants is inevitable. Inbreeding results in a loss of fitness (i.e., inbreeding depression), principally because related parents are more likely to transmit a copy of the same recessive deleterious genetic variant to their offspring. Genomics-informed captive breeding can manage harmful variants by artificial selection, reducing the genetic load by avoiding the inheritance of two copies of the same harmful variant. To explain this concept in an interactive way to zoo visitors, we developed a sonification game to represent the fitness impacts of harmful variants by detuning notes in a familiar musical melody (i.e., Beethoven's Für Elise). Conceptually, zoo visitors play a game aiming to create the most optimal pink pigeon offspring in terms of inbreeding depression. They select virtual crosses between pink pigeon individuals and listen for the detuning of the melody, which represents the realised load of the resultant offspring. Here we present the sonification algorithm and the results of an online survey to see whether participants could identify the most and least optimal offspring from three potential pink pigeon offspring. Of our 98 respondents, 85 (86.7%) correctly identified the least optimal offspring, 73 (74.5%) correctly identified the most optimal, and 62 (63.3%) identified both the most and least optimal offspring using only the sonification.

Listening to life: Sonification for enhancing discovery in biological research

Sonification, or the practice of generating sound from data, is a promising alternative or complement to data visualization for exploring research questions in the life sciences. Expressing or communicating data in the form of sound rather than graphs, tables, or renderings can provide a secondary information source for multitasking or remote monitoring purposes or make data accessible when visualizations cannot be used. While popular in astronomy, neuroscience, and geophysics as a technique for data exploration and communication, its potential in the biological and biotechnological sciences has not been fully explored. In this review, we introduce sonification as a concept, some examples of how sonification has been used to address areas of interest in biology, and the history of the technique. We then highlight a selection of biology-related publications that involve sonifications of DNA datasets and protein datasets, sonifications for data collection and interpretation, and sonifications aimed to improve science communication and accessibility. Through this review, we aim to show how sonification has been used both as a discovery tool and a communication tool and to inspire more life-science researchers to incorporate sonification into their own studies.

Electrochemical Synthesis of Sound: Hearing the Electrochemical Double Layer

Electrochemical double layers (EDLs) govern the operation of batteries, fuel cells, electrochemical sensors, and electrolyzers. However, their invisible nature makes their properties and function difficult to conceptualize, creating an impediment to the broader understanding of double-layer function required for future technologies in energy storage and chemical synthesis. To render the behavior of electrochemical interfaces more intuitive, we made the rearrangement of interfacial components audible by employing the EDL as a variable element in a relaxation oscillator circuit. Connecting the circuit to a speaker generated an audible output corresponding to the change in potential resulting from EDL rearrangement. Variations in the applied voltage, electrolyte concentration and identity, as well as in the electrode material, yielded audible frequency variations that provide an intuitive understanding of EDL behavior. We expect that hearing the trends in behavior will provide a helpful and alternative method for understanding molecular movement at the electrochemical interface.

In-Depth Quantification of Cell Division and Elongation Dynamics at the Tip of Growing Arabidopsis Roots Using 4D Microscopy, AI-Assisted Image Processing and Data Sonification

One of the fundamental questions in plant developmental biology is how cell proliferation and cell expansion coordinately determine organ growth and morphology. An amenable system to address this question is the Arabidopsis root tip, where cell proliferation and elongation occur in spatially separated domains, and cell morphologies can easily be observed using a confocal microscope. While past studies revealed numerous elements of root growth regulation including gene regulatory networks, hormone transport and signaling, cell mechanics and environmental perception, how cells divide and elongate under possible constraints from cell lineages and neighboring cell files has not been analyzed quantitatively. This is mainly due to the technical difficulties in capturing cell division and elongation dynamics at the tip of growing roots, as well as an extremely labor-intensive task of tracing the lineages of frequently dividing cells. Here, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots. We also implemented a data sonification tool that facilitates human recognition of cell division synchrony. Using these tools, we revealed previously unnoted lineage-constrained dynamics of cell division and elongation, and their contribution to the root zonation boundaries.

Wave-like behaviour in (0,1) binary sequences

A comprehensive study of the properties of finite (0,1) binary systems from the mathematical viewpoint of quantum theory is presented. This is a quantum-inspired extension of the GenomeBits model to characterize observed genome sequences, where a complex wavefunction \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\psi _{n}$$\end{document}ψn is considered as an analogous probability measure and it is related to an alternating (0,1) binary series having independent distributed terms. The real and imaginary spectrum of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\psi _{n}$$\end{document}ψnvs. the nucleotide base positions display characteristic features of sound waves. This approach represents a novel perspective for identifying and “observing” emergent properties of genome sequences in the form of wavefunctions via superposition states. The motivation is to develop a simple algorithm to perform wave calculations from binary sequences and to apply these wave functions to sonification.