Characterizing emerging features in cell dynamics using topological data analysis methods.
Filament-motor interactions inside cells play essential roles in many developmental as well as other biological processes. For instance, actin-myosin interactions drive the emergence or closure of ring channel structures during wound healing or dorsal closure. These dynamic protein interactions and the resulting protein organization lead to rich time-series data generated by using fluorescence imaging experiments or by simulating realistic stochastic models. We propose methods based on topological data analysis to track topological features through time in cell biology data consisting of point clouds or binary images. The framework proposed here is based on computing the persistent homology of the data at each time point and on connecting topological features through time using established distance metrics between topological summaries. The methods retain aspects of monomer identity when analyzing significant features in filamentous structure data, and capture the overall closure dynamics when assessing the organization of multiple ring structures through time. Using applications of these techniques to experimental data, we show that the proposed methods can describe features of the emergent dynamics and quantitatively distinguish between control and perturbation experiments.
Duke Scholars
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Related Subject Headings
- Proteins
- Cytoskeleton
- Bioinformatics
- 4901 Applied mathematics
- 4004 Chemical engineering
- 0904 Chemical Engineering
- 0903 Biomedical Engineering
- 0102 Applied Mathematics
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Proteins
- Cytoskeleton
- Bioinformatics
- 4901 Applied mathematics
- 4004 Chemical engineering
- 0904 Chemical Engineering
- 0903 Biomedical Engineering
- 0102 Applied Mathematics