Genesis-DB: a database for autonomous laboratory systems

Area of Science:

• Molecular Biology
• Bioinformatics
• Artificial Intelligence

Background:

• Artificial intelligence (AI) is transforming biological research through laboratory automation.
• Autonomous software agents and robotic systems are key components of this trend.
• Efficient data management is crucial for AI-driven biological discovery.

Purpose of the Study:

• To introduce Genesis-DB, a database system designed for AI-driven autonomous laboratories.
• To present a novel ontology for modeling data from yeast microchemostat cultivations.
• To demonstrate how Genesis-DB supports the full research lifecycle, from data modeling to hypothesis generation.

Main Methods:

• Development of the Genesis-DB database system.
• Creation of a new ontology for experimental data and metadata.
• Integration of Genesis-DB with the Genesis robot scientist system.
• Modeling of yeast gene regulation data within Genesis-DB.

Main Results:

• Genesis-DB provides structured domain information for AI software agents.
• The new ontology effectively models autonomously performed yeast microchemostat cultivation data.
• Genesis-DB facilitates hypothesis generation and experimental design for AI-driven research.
• The system's design is portable, generic, and extensible.

Conclusions:

• Genesis-DB is a valuable tool for advancing AI-driven discovery in molecular biology.
• The database and ontology support automated reasoning and the research lifecycle.
• The system's architecture allows for adaptation to various AI-driven laboratory applications.