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Using pathway modules as targets for assay development in xenobiotic screening.

Richard S Judson¹, Holly M Mortensen, Imran Shah

¹National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. judson.richard@epa.gov

Molecular Biosystems

|November 15, 2011

Summary

This study introduces a computational method to select key genes for high-throughput screening (HTS) assays. This approach efficiently predicts chemical impacts on biological pathways, aiding toxicology and pharmaceutical research.

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Area of Science:

Toxicology
Genomics
Computational Biology

Background:

High-throughput screening (HTS) is vital for assessing chemical effects.
Whole-genome microarray analysis is impractical for large-scale chemical evaluations.
A focused gene set is needed for scalable systems-level response assessment.

Purpose of the Study:

To develop a computational approach for selecting informative in vitro expression assay targets.
To identify genes that are broadly distributed in biological pathway space using pathway modularity.
To create a scalable method for evaluating thousands of chemicals across diverse biological contexts.

Main Methods:

Decomposed canonical pathways into functionally related gene subnetworks (modules).
Utilized rules like co-regulated expression and protein-protein interactions to construct pathway modules.
Selected and validated target genes using independent microarray data for tumor development and cancer progression pathways.

Main Results:

Identified a targeted set of genes for assay development.
Validated selected genes as sensitive predictors of chemical perturbation in pathway modules.
Demonstrated the approach's effectiveness using cancer-related gene subsets.

Conclusions:

The developed computational method successfully identifies informative gene targets for HTS assays.
The selected genes can form a battery for testing pathway-chemical interactions in various biological contexts.
This approach offers a scalable solution for toxicological and pharmaceutical research.