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Cellular function prediction and biological pathway discovery in Arabidopsis thaliana using microarray data.

Trupti Joshi1, Yu Chen, Nickolai Alexandrov

  • 1Dept. of Comput. Sci., Missouri-Columbia Univ., Columbia, MO, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
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We developed a new method to predict gene functions using gene expression and protein interactions. This approach aids in understanding cellular functions and biological pathways in plants like Arabidopsis.

Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Predicting cellular functions is crucial for understanding biological systems.
  • Gene expression profiles and protein-protein interactions offer valuable data for functional inference.
  • Existing methods may not fully integrate diverse data types for robust predictions.

Purpose of the Study:

  • To develop an integrated probabilistic method for cellular function prediction.
  • To assess the relationship between gene expression correlation and functional relationships within the Gene Ontology (GO) hierarchy.
  • To identify biological pathways and their components using computational algorithms.

Main Methods:

  • Integrated statistical model using microarray gene expression profiles.

Related Experiment Videos

  • Incorporation of predicted protein-protein interactions and known protein annotations.
  • Novel assessment of gene expression correlation and GO hierarchy.
  • Application of Dijkstra's algorithm for pathway component and topology identification.

    • Main Results:

    • Successful application of the method for predicting hypothetical gene functions in Arabidopsis.
    • Identification of pathway components and topology.
    • Prediction of the phosphatidic acid signaling pathway in Arabidopsis.

    Conclusions:

    • The developed integrated probabilistic method effectively predicts cellular functions.
    • The approach enhances the understanding of gene function and biological pathways.
    • This method provides a valuable tool for plant systems biology research.