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Predicting altered pathways using extendable scaffolds

B M Broom1, T J McDonnell, D Subramanian

  • 1Department of Biostatistics and Applied Mathematics, MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA. broom@mdanderson.org

International Journal of Bioinformatics Research and Applications
|December 1, 2007
PubMed

Insights

This study introduces PAPES, a new computational method to model biological systems by analyzing gene expression data. PAPES predicts altered cellular pathways in diseases like cancer.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Cellular process deregulation is common in diseases, particularly solid tumors.
  • Current methods often identify differentially expressed genes but lack pathway-level insights.
  • Understanding pathway interactions is crucial for disease mechanism elucidation.

Purpose of the Study:

  • To develop a novel computational method for reverse-engineering biological system models.
  • To predict and analyze altered biological pathways in disease contexts.
  • To capture complex interactions among multiple pathways.

Main Methods:

  • Proposed a new method: PAPES (predicting altered pathways using extendable scaffolds).
  • Constructed component process models using genes from known biological pathways.
  • Composed pathway models to build larger networks representing pathway interactions.

Main Results:

  • Successfully applied PAPES to computationally reverse-engineer biological system models.
  • Demonstrated the ability to learn process modifications in coupled metabolic pathways.
  • Identified pathway alterations in the context of prostate cancer cells.

Conclusions:

  • PAPES offers a novel approach to model biological systems and understand pathway deregulation.
  • The method facilitates the analysis of complex pathway interactions.
  • PAPES has potential applications in understanding disease mechanisms, including cancer.

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