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Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models

Francisco Salavert1, Marta R Hidago2, Alicia Amadoz2

  • 1Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Valencia, 46012, Spain.

Nucleic Acids Research
|May 4, 2016
PubMed

Insights

PathAct predicts how gene interventions affect cellular functions and disease progression. This tool aids in identifying actionable drug targets for personalized therapies by simulating gene modifications.

Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Identifying effective drug targets is essential for developing targeted therapies.
  • Understanding gene interaction networks is critical for predicting therapeutic responses.
  • Current methods may not fully capture the complex effects of gene interventions on cellular signaling.

Purpose of the Study:

  • To present PathAct, a novel web server for predicting the impact of gene interventions on cellular signaling pathways.
  • To provide an interactive platform for evaluating potential drug targets.
  • To facilitate the discovery of actionable genes for personalized and individualized therapies.

Main Methods:

  • PathAct utilizes a computational approach to model signal transmission within biological pathways.
  • It simulates gene interventions such as inhibition, activation, knock-outs, drug treatments, and over-expressions.
  • An advanced graphical interface allows interactive exploration of gene effects.

Main Results:

  • PathAct predicts the downstream effects of gene manipulations on signaling pathways.
  • The tool assesses the influence of interventions on cell functionalities.
  • It enables the evaluation of gene suitability as drug targets.

Conclusions:

  • PathAct offers a valuable resource for researchers in drug discovery and personalized medicine.
  • The web server aids in identifying and validating actionable gene targets.
  • It enhances the understanding of gene function in disease and therapeutic contexts.

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