HiLoop

HiLoop identifies high-feedback loops in gene regulatory networks (GRNs) to analyze their roles in cellular dynamics such as multistability and oscillation.

Key Features:

• Identification and Visualization: Identifies high-feedback loop motifs within large gene regulatory networks and generates visual representations of those loop structures.
• Statistical Analysis and Enrichment Quantification: Quantifies enrichment of overrepresented high-feedback structures and performs statistical analysis of their prevalence within pathways.
• Mathematical Modeling and Random Parameterization: Applies random parameterization to mathematical models derived from target networks to explore dynamical behaviors such as multistability and oscillation.
• Hypothesis Generation and Motif Enrichment Analysis: Detects high-feedback subnetworks to generate hypotheses and quantifies motif enrichment to aid discovery of regulatory mechanisms.

Scientific Applications:

• Network-scale Analysis: Applied to gene regulatory networks (GRNs) containing dozens to hundreds of genes to identify numerous small high-feedback systems.
• Transcription Factor Discovery: Identified over 100 human transcription factors involved in previously unstudied high-feedback loops.
• EMT Pathway Enrichment: Revealed enrichment of high-feedback structures in pathways related to epithelial-mesenchymal transition (EMT), implicating roles in cell differentiation and lineage progression.

Methodology:

Extracts high-feedback loop structures from complex networks, visualizes them, and applies random parameterization to mathematical models to evaluate dynamical features such as multistability and oscillation.