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Updated: May 10, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Identifying proteins controlling key disease signaling pathways.

Anthony Gitter¹, Ziv Bar-Joseph

¹Computer Science Department and Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)

|July 2, 2013

Summary

We developed a new model, SDREM, to understand gene-disease links and biological pathways. This method accurately predicts gene interactions and identifies disease-specific targets, improving upon existing approaches for disease research.

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Last Updated: May 10, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Area of Science:

Systems biology
Computational biology
Genomics

Background:

Current methods like GWAS and RNAi screens have limitations in providing mechanistic insights into gene-disease associations.
Genetic variants are often population-specific, and experimental screens can be noisy and lack reproducibility.
There is a need for mechanistic models to predict the dynamic effects of gene perturbations in disease pathways.

Purpose of the Study:

To develop a computational model for understanding signaling and dynamic regulatory networks in disease.
To integrate diverse data types for improved prediction of gene function and pathway involvement.
To create a tool for predicting the effects of genetic perturbations and identifying therapeutic targets.

Main Methods:

Developed the Signaling and Dynamic Regulatory Events Model (SDREM).
Integrated static and time-series data to model protein interactions and regulatory networks.
Incorporated prior knowledge of protein-disease associations to enhance network prediction accuracy.

Main Results:

SDREM successfully modeled the human immune response to H1N1 influenza, identifying known pathways and regulators.
The model accurately predicted RNA interference effects and inferred genetic interactions, outperforming existing methods.
Application to H5N1 influenza identified novel, strain-specific therapeutic targets.

Conclusions:

SDREM provides a robust framework for building mechanistic models of biological networks.
The model enhances the understanding of gene-disease relationships and pathway dynamics.
SDREM facilitates the prediction of genetic interactions and identification of potential therapeutic targets.