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Related Experiment Video

Updated: Jul 13, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments.

Oved Ourfali¹, Tomer Shlomi, Trey Ideker

¹School of Computer Science, School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.

Bioinformatics (Oxford, England)

|July 25, 2007

Summary

We developed SPINE, a novel framework to infer gene regulatory and signaling pathways by analyzing gene knockout effects. SPINE accurately predicts gene expression changes and reconstructs complex cellular networks.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Systems Biology
Computational Biology
Genomics

Background:

Cellular function relies on complex gene expression programs responding to various conditions.
Large-scale gene knockout and molecular interaction data offer insights into cellular regulatory and signaling pathways.

Purpose of the Study:

To present SPINE (Signaling-regulatory Pathway INferencE), a novel framework for inferring gene regulatory and signaling pathways.
To explain gene expression changes observed after gene knockouts by reconstructing underlying molecular interactions.

Main Methods:

Integrated construction of protein-protein and protein-DNA interaction networks.
Formulation of pathway reconstruction as an integer programming problem to assign activation/repression attributes.
Validation using yeast mating subnetwork and application to the entire yeast interactome.

Main Results:

SPINE achieved 99% accuracy in predicting gene knockout effects in cross-validation tests.
Inferred 861 signaling and regulatory pathways with confidence across the yeast network.
Assigned functional effects to 183 genes, aligning well with existing biological knowledge.

Conclusions:

SPINE provides a robust computational framework for uncovering gene regulatory and signaling pathways.
The method effectively explains gene expression changes and predicts molecular interactions.
The inferred pathways offer valuable insights into cellular mechanisms and gene function.