A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells

Jo A. Helmuth, Christoph J. Burckhardt, Petros Koumoutsakos, Urs F. Greber, Ivo F. Sbalzarini

Research output: Contribution to journalArticle

66 Scopus citations

Abstract

Biological trajectories can be characterized by transient patterns that may provide insight into the interactions of the moving object with its immediate environment. The accurate and automated identification of trajectory motifs is important for the understanding of the underlying mechanisms. In this work, we develop a novel trajectory segmentation algorithm based on supervised support vector classification. The algorithm is validated on synthetic data and applied to the identification of trajectory fingerprints of fluorescently tagged human adenovirus particles in live cells. In virus trajectories on the cell surface, periods of confined motion, slow drift, and fast drift are efficiently detected. Additionally, directed motion is found for viruses in the cytoplasm. The algorithm enables the linking of microscopic observations to molecular phenomena that are critical in many biological processes, including infectious pathogen entry and signal transduction.

Original languageEnglish (US)
Pages (from-to)347-358
Number of pages12
JournalJournal of Structural Biology
Volume159
Issue number3
DOIs
StatePublished - Sep 1 2007
Externally publishedYes

Keywords

  • Computation
  • Motion pattern
  • Support vector machine
  • Trajectory analysis
  • Trajectory segmentation
  • Transport
  • Virus infection

ASJC Scopus subject areas

  • Structural Biology

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