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Computational Tools for Quantitative Analysis of Cell Growth Patterns and Morphogenesis in Actively Developing Plant Stem Cell Niches

  • Anirban Chakraborty
  • Ram Kishor Yadav
  • Min Liu
  • Moses Tataw
  • Katya Mkrtchyan
  • Amit Roy Chowdhury
  • G. Venugopala Reddy
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 876)

Abstract

Pattern formation in developmental fields involves precise spatial arrangement of different cell types in a dynamic landscape wherein cells exhibit a variety of behaviors, such as cell division, cell expansion, and cell migration [Reddy (Curr Opin Plant Biol 11:88–931, 2008) and Meyerowitz (Cell 88:299–3082, 2007)]. The information is exchanged between multiple cell layers through cell–cell communication processes to regulate gene expression and cell behaviors in specifying distinct cell types. Therefore, a quantitative and dynamic understanding of the spatial and temporal organization of gene expression and cell behavioral patterns within multilayered and actively growing developmental fields is crucial to model the process of development. The quantification of spatiotemporal dynamics of cell behaviors requires computational tools in image analysis, statistical modeling, pattern recognition, machine learning, and dynamical system identification. Here, we give a brief account of recently developed methods in analyzing both local and global growth patterns in Arabidopsis shoot apical meristems. The computational toolkit can be used to gain new insights into causal relationships among cell growth, cell division, changes in gene expression patterns, and organ development by analyzing various mutants that affect these processes. This may allow us to develop function space models that capture variations in several growth parameters both at local/single-cell level and at global/organ level. In the long run, this may enable clustering of molecular pathways that mediate distinct cell behaviors.

Key words

Arabidopsis Shoot apical meristem Cell segmentation Cell tracking Self-renewal Differentiation Function space model 

Notes

Acknowledgments

We thank the microscopy core facility of center for plant cell biology (CEPCEB) and institute of integrative genome biology (IIGB), University of California, Riverside. This work is funded by National Science Foundation grants (IOS-0820842 to GVR and IIS-0712253 to ARC).

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Anirban Chakraborty
    • 1
  • Ram Kishor Yadav
    • 2
  • Min Liu
    • 1
  • Moses Tataw
    • 1
  • Katya Mkrtchyan
    • 1
  • Amit Roy Chowdhury
    • 1
  • G. Venugopala Reddy
    • 2
  1. 1.Department of Electrical EngineeringUniversity of CaliforniaRiversideUSA
  2. 2.Department of Botany and Plant Sciences, and Center for Plant Cell Biology, Institute of Integrative Genome BiologyUniversity of CaliforniaRiversideUSA

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