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Welcome to the GitHub repository for BioVerse, an innovative project that earned a 4th place finish at the EF 2023 Bio x AI Hackathon. BioVerse represents a groundbreaking step in combining artificial intelligence with biology research, offering a comprehensive virtual lab experience. BioVerse simulates real-world biology research workflows through multi-agent conversations. Users can pose problems and receive guided assistance across literature discovery, experimental design, computational analysis, and results interpretation and paper writing. BioVerse's aim is to automate and streamline biology research by coordinating specialized research agents, while continuously improving through user feedback. It supports the entire research journey, from initial problem formulation to publication, offering guidance in experimental strategies, data collection, and computational analysis. BioVerse represents an innovative, accessible virtual lab for comprehensive life science research.

Team

  • Ananya Bhalla - Team Lead & Bio Scientist (The Francis Crick Institute & Kings College)
  • Kosi Asuzu - AI/ML Engineer (Birmingham City University & Ivy)
  • Ruige Kong - AI/ML Engineer (University of Cambridge & European Bioinformatics Institute)
  • Oliver Hernandez - Bio Scientist (Imperial College)
  • Henry Ndubuaku - AI/NLP Scientist (Coconut Head)

Agents

  • Principal Investigator - Serves as the coordinating agent, and critiques the works of other agents to ensure high quality
  • Research Assistant - An agents which conducts literature review, documentation, project coordination.
  • Experimental Biologist - Designs and discusses hands-on wet lab experiments and protocols.
  • Bioinformatician - Provides guidance on computational analysis and omics data workflows.
  • Image Analyst - Assists with processing and extracting insights from microscopy imaging data.

Installation

  • Clone the repository:

    git clone https://github.com/your-username/biology-experiments-orchestrator.git
cd biology-experiments-orchestrator```

  • Install: pip install requirements.txt

  • Run: python3 main.py

Usage

  • Adjust agent parameters and configurations based on experimental requirements.
  • Use the Experiment Generation Agent to create customized experimental protocols.
  • Deploy the Experiment Execution Agent to carry out the experiments in the laboratory.
  • Leverage the Image Analysis Agent to process and analyze acquired experimental data.
  • Utilize the Information Retrieval Agent to access and retrieve experiment results.
  • Provide feedback through the designated channels to improve system performance.