National Resource for Network Biology (NRNB)

Mentors: Student: Shreeya456

Developing a scalable and reproducible research pipeline within SeqTrainer for bacterial DNA sequence modelling and annotation using SBOL-based datasets. The project addresses a key challenge in synthetic biology: the lack of accessible, machine learning–ready datasets and standardized workflows for genomic modelling. The primary focus is on E. coli and Gram-negative bacteria, with applications in promoter classification and sequence annotation. The work involves reproducing and benchmarking existing CNN-based baselines, followed by integrating and evaluating foundation models such as DNABERT2 and Evo 2 within structured experiment pipelines. Experiments explore challenges including overfitting and class imbalance through methods such as weighted loss functions, undersampling, learning-rate tuning, and early stopping. A major component of the project is the development of a modular and reproducible experiment framework within SeqTrainer, including configuration-driven workflows, reproducible dataset splitting, evaluation utilities, metric logging, and improved SBOL-to-dataset conversion pipelines with validation and provenance tracking. The final outcome aims to transform SeqTrainer into a more experiment-friendly framework for genomic ML research, enabling accessible, extensible, and reproducible machine learning workflows for synthetic biology researchers.

Mentors: Student: Kevin Zhu

Currently, the Cytoscape App Store only supports plugins for the Cytoscape Desktop App. Since a web version of Cytoscape is available, web plugins and service apps can add functionality, similar to the desktop plugins. With multiple platforms, a unified app registry would be ideal in organizing plugins and extensions, with layered filtering capabilities (e.g. platform type, app type). To solve this problem, the proposal includes an implementation plan on frontend UI changes and filtering, database migrations, an updated submission and approval flow for both web apps and service apps, and health monitoring for all apps. The first 3-4 weeks will be spent discussing final changes with mentors, with database migrations to accomodate the new platform types. The frontend will also receive changes including filtering, new tags and categories. Weeks 5 and 6 will follow a similar process for web service apps. Weeks 7-10 will consist of adjusting the submission workflow, including frontend UI changes for developers and database migrations. Health checks will also be implemented, with certain criteria to determine the health status of an app. Weeks 11-12 will consist of unit testing, including health check testing, testing new uploads, latency checks and documentation.

Mentors: Student: Kartik Deshpande

VCell-AI is an AI-powered platform for discovering, analyzing, and exploring biological models from the VCell database. While the platform already provides a functional chatbot interface, it requires additional capabilities to support real-world usage — including authentication, usage control, and broader integration with scientific data sources. This project aims to extend the system across these dimensions. Auth0 authentication will be integrated using the existing VCell Auth0 tenant, allowing existing VCell users to log in seamlessly. On top of this, a per-user token tracking and enforcement system will be introduced, along with an admin dashboard for managing limits. The prompt handling layer will be refactored into a centralized registry to improve consistency and reduce fragmentation. Local LLM support will be exposed as a user-facing feature. The system will also be expanded with PubMed search, complementing the already implemented BioModels DB integration. To improve usability, BNGL-based molecular visualization will be integrated. Support for SBML data will be completed, and BioPAX pathway data will be introduced as an additional source for biological knowledge. Prior to the coding period, 8 pull requests have already been raised covering API standardization, local LLM bug fixes, and BNGL visualization integration as a proof of concept, demonstrating familiarity with the codebase and commitment to the project.

Mentors: Student: hamid-ananda

openPIP is an open-source platform for hosting and visualizing protein-protein interaction (PPI) data. The current version is built on a PHP 5.6 / Symfony 2.8 stack that has reached end-of-life, making it difficult to maintain and extend. This project performs a complete rewrite of openPIP using modern web frameworks: a React single-page application frontend with Cytoscape.js for network visualization, a REST API backend (Python/Django REST Framework or Node.js/Express, to be finalized with the mentor), and a PostgreSQL database, all containerized with Docker. Key deliverables include: (1) a clean REST API replacing the 27 monolithic PHP controllers, (2) an enhanced data upload pipeline supporting PSI-MI TAB and CSV formats with drag-and-drop, real-time validation, and async processing, (3) a responsive React frontend with interactive search, network visualization, and an admin panel, (4) production-ready Docker Compose deployment, and (5) a data migration script for existing openPIP deployments (HuRI, YeRI). I have direct experience with the openPIP codebase, having created its deployment guide and migrated the platform from openpip.baderlab.org to openpip.usask.ca.

Mentors: Student: Norma Citlalcue Perez Rosas

This project aims to improve and evaluate symbolic regression methods for discovering interpretable mathematical models of biological network dynamics. A key challenge in this area is that, when working with real biological data, the true underlying equations are unknown, making it difficult to assess whether equation-discovery algorithms are accurate or biologically meaningful. To address this, I will generate synthetic time-series datasets from curated mechanistic models available in the BioModels database (e.g., SBML models), where the ground-truth equations are known. These datasets will be used to systematically evaluate and benchmark symbolic regression approaches, including PySR and neural-symbolic methods such as the Learning Law of Changes (LLC) framework. The project will also explore strategies to improve model identifiability and interpretability, including constrained search spaces and incorporation of biologically informed functional priors. The expected deliverables include: (1) a reproducible pipeline to generate simulation data from SBML models, (2) an evaluation framework to benchmark symbolic regression methods on equation recovery tasks, (3) implementation and comparison of PySR and LLC-based approaches, (4) strategies to address identifiability challenges in biological data, and (5) well-documented, open-source code integrated with existing SBML compatible tools.