Inferring Regulatory Mechanisms from Differential Gene Expression

Presenter: Ava Sajjadi

Faculty Sponsor: Kourosh Zarringhalam

School: UMass Boston

Research Area: Computer Science

Session: Poster Session 3, 1:15 PM - 2:00 PM, 163, C9

ABSTRACT

Understanding pathological states requires moving beyond differential gene expression to identify upstream regulatory mechanisms and disrupted signaling pathways. Although conventional transcriptomic analyses detect genes that are significantly up- or down-regulated, they do not directly reveal the transcription factors (TFs) and regulatory interactions responsible for these alterations.

This project presents a unified computational platform for gene regulatory network inference that integrates two complementary statistical paradigms: a frequentist framework (QuaternaryProd) and a Bayesian network model (NLBayes). The system accepts transcriptomic signatures, harmonizes gene identifiers, and evaluates regulatory consistency against curated interaction networks. By incorporating regulatory directionality, network structure, and statistical uncertainty, the platform infers candidate TF activities and perturbed pathways associated with disease states.

Implemented as a researcher-facing web application, the framework standardizes outputs across both models and enables flexible comparison, filtering, ranking, and visualization of inferred regulatory interactions. This design allows users to examine how different statistical assumptions influence mechanistic interpretation while maintaining methodological rigor and transparency. By shifting analysis from gene-level changes to regulator-level inference, the platform enhances interpretability of transcriptomic data, supports hypothesis generation, facilitates identification of candidate therapeutic targets, and provides a scalable and reproducible computational foundation for systematic investigation of disease-associated regulatory mechanisms across diverse biological contexts.

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