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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 3516089, 9 pages
Research Article

A Run-Length Encoding Approach for Path Analysis of C. elegans Search Behavior

1School of Computing, College of Computing and Digital Media, DePaul University, Chicago, IL 60604, USA
2Department of Cell Biology and Anatomy, Chicago Medical School, Rosalind Franklin University, North Chicago, IL 60064-3095, USA

Received 16 February 2016; Accepted 29 May 2016

Academic Editor: Weidong Cai

Copyright © 2016 Li Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The nematode Caenorhabditis elegans explores the environment using a combination of different movement patterns, which include straight movement, reversal, and turns. We propose to quantify C. elegans movement behavior using a computer vision approach based on run-length encoding of step-length data. In this approach, the path of C. elegans is encoded as a string of characters, where each character represents a path segment of a specific type of movement. With these encoded string data, we perform -means cluster analysis to distinguish movement behaviors resulting from different genotypes and food availability. We found that shallow and sharp turns are the most critical factors in distinguishing the differences among the movement behaviors. To validate our approach, we examined the movement behavior of tph-1 mutants that lack an enzyme responsible for serotonin biosynthesis. A -means cluster analysis with the path string-encoded data showed that tph-1 movement behavior on food is similar to that of wild-type animals off food. We suggest that this run-length encoding approach is applicable to trajectory data in animal or human mobility data.