Genesis-DB: a database for autonomous laboratory systems

Abstract

Summary: Artificial intelligence (AI)-driven laboratory automation-combining robotic labware and autonomous software agents-is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond.

Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology.

Figure 1.

Genesis-DB a database system for autonomous laboratories. (a) Visualization of database usage from demonstration software agent utilization of experimental metadata for biological model improvement. Flow from left to right represents a cycle of model improvement. First, a GRN is reconstructed from gene counts, retrieved from the database with query (i); then the experimental conditions are retrieved (ii) and the space is visualised; (iii) a hypothesis together with the experimental procedures to test it is written to the database; then the regulatory network is recreated from data including the new high temperature experiment, retrieved using query (iv); after (v) we see that the examined conditions now also contain an experiment performed at a higher temperature. (b) SPARQL query to retrieve all experimental conditions from the database, the query used for (ii) and (v). Here, PREFIX keywords are used to specify the relevant ontologies for terms used in the query. More detail on the syntax of SPARQL is available at, e.g. Pérez et al. (2009).