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Automated oncogene detection in complex protein networks with applications to the MAPK signal transduction pathway

Dhruv Pant1, Avijit Ghosh

  • 1Drexel University, 3141 Chestnut St, Philadelphia, PA 19104, USA.

Biophysical Chemistry
|December 29, 2004
PubMed

Insights

A new nonlinear analysis algorithm predicts cellular transformation by examining extracellular signal-regulated kinases (ERK1/2) phosphorylation. This method identifies novel oncogenes and tumor suppressor genes impacting the RAS/MAPK pathway.

Area of Science:

  • Molecular Biology
  • Systems Biology
  • Oncology

Background:

  • Extracellular signal-regulated kinases (ERK1/2; p42/p44 mitogen-activated protein kinase (MAPK)) signaling is crucial and frequently dysregulated in cancer, particularly downstream of oncogenic RAS.
  • The canonical RAS/RAF/MEK/ERK pathway is well-characterized, providing a foundation for computational analysis.

Purpose of the Study:

  • To evaluate a novel nonlinear analysis algorithm for predicting cellular transformation likelihood.
  • To identify novel modifiers of the RAS/MAPK signaling module using experimental kinetic data.

Main Methods:

  • Utilized a nonlinear analysis algorithm to process experimentally determined kinetic data of the RAS/RAF/MEK/ERK pathway.
  • Employed ERK phosphorylation as the primary output signal for analysis.
  • Compared nonlinear analysis results with linearized approaches like sensitivity analysis and metabolic control analysis (MCA).

Main Results:

  • The algorithm successfully predicted modifiers of ERK/MAPK activation, including previously known and novel enzymes.
  • Identified novel enzymes not previously associated with ERK/MAPK phosphorylation.
  • The nonlinear analysis effectively ranked the importance of MAPK activation modifiers in RAS-dependent oncogenesis.

Conclusions:

  • The developed nonlinear analysis algorithm is a valuable tool for predicting cellular transformation.
  • This approach can identify novel regulators within complex signaling networks.
  • Nonlinear analysis, alongside sensitivity analysis and MCA, shows promise for studying oncogenes in complex signaling networks.

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