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MP-BioPath Integration Pipeline

Author: Julian Benson (B.S. Biochemistry & Molecular Biology) Target: Reactome / Open Genome Informatics (GSoC 2026)

Read the Full GSoC 2026 Architectural Proposal Here

Project Overview

This repository contains the data ingestion and optimization bridge designed to transition MP-BioPath from a literature-validation tool to a high-throughput predictive engine for genomic data.

The pipeline utilizes a polyglot architecture:

  • Python/R (src/module_a_ingestion): Processes raw RNA-seq differential expression data into the continuous 0.01 to 100 boundaries required by the MP-BioPath non-linear mathematical models.
  • Python (src/module_b_graphs): Interfaces with the Reactome REST API to dynamically stitch sub-pathway logic graphs.
  • Julia/JuMP (src/module_c_solver): Modifies the core objective function weights to resolve entity-set dilution and automate the 15% biological significance threshold.

Repository Structure

  • /data: (Ignored via .gitignore) Local staging for MCB112 quantitative datasets and RNA-seq counts.
  • /src: Core module scripts for ingestion, API translation, and JuMP solver execution.
  • /docs: Project proposals, mathematical documentation, and architecture blueprints.

References

Wright, A.J., Orlic-Milacic, M., Rothfels, K. et al. Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase. Database (2022). DOI: 10.1093/database/baac009

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A polyglot (Python/R/Julia) integration pipeline to transition the MP-BioPath Reactome algorithm into a high-throughput genomic predictive engine.

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