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Related Experiment Video

Updated: May 6, 2026

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

Integrated module and gene-specific regulatory inference implicates upstream signaling networks.

Sushmita Roy¹, Stephen Lagree, Zhonggang Hou

¹Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America ; Wisconsin Institute for Discovery, Madison, Wisconsin, United States of America.

Plos Computational Biology

|October 23, 2013

Summary

MERLIN, a new method, reconstructs gene regulatory programs and identifies their modular organization. This approach enhances understanding of gene regulation in biological processes like stress response and development.

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Last Updated: May 6, 2026

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

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Published on: May 31, 2011

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Gene expression is controlled by complex regulatory networks.
These networks involve both module-level co-regulation and gene-specific fine-tuning.
Understanding these networks is crucial for diverse biological contexts like stress response and development.

Purpose of the Study:

To present a novel computational approach called MERLIN (Modular regulatory network learning with per gene information).
To infer individual gene regulatory programs while revealing the modular organization of these networks.
To apply MERLIN to analyze transcriptional regulation in yeast stress response and human embryonic stem cell differentiation.

Main Methods:

MERLIN probabilistically constrains gene regulatory programs to identify module-level organization.
The method was validated by comparing simulated networks with known ground truth using edge-, regulator-, and module-based metrics.
MERLIN was applied to analyze global transcriptional behavior in yeast and human cell differentiation datasets.

Main Results:

MERLIN reconstructs individual gene regulatory programs as effectively as or better than existing methods.
MERLIN successfully identifies the modular organization within regulatory networks.
Inferred modules captured co-regulatory relationships, identified upstream signaling systems, and highlighted functionally related gene modules.

Conclusions:

MERLIN effectively combines per-gene and per-module approaches for network reconstruction.
The method provides new insights into transcriptional regulation during stress and development.
MERLIN's inferred networks are supported by known genetic/physical interactions and reveal functional gene groupings.