Abstract
We apply topological data analysis to the behavior of C. elegans, a widely studied model organism in biology. In particular, we use topology to produce a quantitative summary of complex behavior which may be applied to high-throughput data. Our methods allow us to distinguish and classify videos from various environmental conditions and we analyze the trade-off between accuracy and interpretability. Furthermore, we present a novel technique for visualizing the outputs of our analysis in terms of the input. Specifically, we use representative cycles of persistent homology to produce synthetic videos of stereotypical behaviors.
Highlights
IntroductionStudies of model organisms have played a major role in discoveries of disease mechanisms, disease treatment, and neuroscience principles
Model organisms are indispensable in understanding basic principles of biology
We have demonstrated that persistent homology is a viable technique for studying C. elegans behavior and provides useful interpretations and visualizations
Summary
Studies of model organisms have played a major role in discoveries of disease mechanisms, disease treatment, and neuroscience principles. The behavior of these model organisms can illuminate responses and phenotypes important for understanding the effects of experimental conditions on subjects. We propose persistent homology as a new tool for assessing behavior of Caenorhabditis elegans, worms that are a widely used model organism. To the authors’ knowledge, persistent homology has not been previously used to analyze C. elegans behavior, though it and similar techniques have been used to study C. elegans neural data (Petri et al, 2013; Backholm et al, 2015; Sizemore et al, 2019; Helm et al, 2020; Lütgehetmann et al, 2020)
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