Automated, predictive, and interpretable inference of Caenorhabditis elegans escape dynamics

BRYAN DANIELS, William S. Ryu, Ilya Nemenman

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

The roundworm Caenorhabditis elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.

Original languageEnglish (US)
Pages (from-to)7226-7231
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number15
DOIs
StatePublished - Apr 9 2019

Keywords

  • Dynamical systems
  • Machine learning
  • Nociception
  • Phenomenological models

ASJC Scopus subject areas

  • General

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