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  • Letter
  • Open Access

Compression algorithms reveal memory effects and static disorder in single-molecule trajectories

Kevin Song1, Dmitrii E. Makarov2,3,*, and Etienne Vouga1

  • 1Department of Computer Science, University of Texas at Austin, Austin, Texas 78712, USA
  • 2Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, USA
  • 3Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA

  • *makarov@cm.utexas.edu
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Phys. Rev. Research 5, L012026 – Published 27 February, 2023

DOI: https://doi.org/10.1103/PhysRevResearch.5.L012026

Abstract

A key challenge in single-molecule studies is deducing underlying molecular kinetics from low-dimensional data, as distinct physical scenarios can exhibit similar observable behaviors such as anomalous diffusion. We show that information-theoretic analysis of single-molecule time series can reliably differentiate Markov (memoryless) from non-Markov dynamics and static from dynamic disorder. This analysis is based on the idea that non-Markov time series can be compressed, using lossless compression algorithms and transmitted within shorter messages than appropriately constructed Markov approximations. In practice, this method detects differences between Markov and non-Markov trajectories even when they are much smaller than the errors of the compression algorithm.

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Outline

Information

Phys. Rev. Research 5, L012026– Published 27 February, 2023
  • Received 20 April 2022
  • Revised 7 September 2022
  • Accepted 17 January 2023
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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