Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal alignment.
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.64
Oben M. Tataw , University of California, Riverside, Riverside
Gonehal Venugopala Reddy , University of California, Riverside, Riverside
Eamonn J. Keogh , University of California, Riverside, Riverside
Amit K. Roy-Chowdhury , University of California, Riverside, Riverside
Study of the molecular control of organ growth requires establishment of the causal relationship between gene expression and cell behaviors. We seek to understand this relationship at the shoot apical meristem (SAM) of model plant Arabidopsis thaliana. This requires the spatial mapping and temporal alignment of different functional domains into a single template. Live cell imaging techniques allow us to observe real time organ primordia growth and gene expression dynamics at cellular resolution. In this paper, we propose a framework for measurement of growth features at the 3D reconstructed surface of organ primordia, as well as an algorithm for robust time alignment of primordia. We computed areas and deformation values from reconstructed 3D surfaces of individual primordia from live cell imaging data. Based on these growth measurements, we applied a multiple features landscape matching algorithm (LAM-M), to ensure a reliable temporal alignment of multiple primordia. We also present an alternate parameter free growth alignment algorithm which performs as well as LAM-M for high quality data, but is more robust to the presence of outliers or noise. Results on primordia and guppy evolutionary growth data show that the proposed alignment algorithm is significantly better in the case of increased noise.
Biology and genetics, Mathematics of Computing, Numerical Analysis, Applications, Information Technology and Systems, Database Management, Database Applications, Data mining, Feature extraction or construction, Mining methods and algorithms, Computing Methodologies, Image Processing and Computer Vision, Image Representation, Pattern Recognition, General, Computer Applications, Life and Medical Sciences
G. V. Reddy, A. K. Roy-Chowdhury, E. J. Keogh and O. M. Tataw, "Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal alignment.," in IEEE/ACM Transactions on Computational Biology and Bioinformatics.