Genesis-DB: a database for autonomous laboratory systems.


Journal

Bioinformatics advances
ISSN: 2635-0041
Titre abrégé: Bioinform Adv
Pays: England
ID NLM: 9918282081306676

Informations de publication

Date de publication:
2023
Historique:
received: 16 05 2023
revised: 13 07 2023
accepted: 01 08 2023
medline: 21 8 2023
pubmed: 21 8 2023
entrez: 21 8 2023
Statut: epublish

Résumé

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. 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.

Identifiants

pubmed: 37600845
doi: 10.1093/bioadv/vbad102
pii: vbad102
pmc: PMC10432352
doi:

Types de publication

Journal Article

Langues

eng

Pagination

vbad102

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press.

Déclaration de conflit d'intérêts

None declared.

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Auteurs

Gabriel K Reder (GK)

The Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 58, Sweden.

Alexander H Gower (AH)

The Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 58, Sweden.

Filip Kronström (F)

The Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 58, Sweden.

Rushikesh Halle (R)

Engineering for Research (e4r™), Thoughtworks Technologies (India) Pvt Ltd, Pune, 411006, India.

Vinay Mahamuni (V)

Engineering for Research (e4r™), Thoughtworks Technologies (India) Pvt Ltd, Pune, 411006, India.

Amit Patel (A)

Engineering for Research (e4r™), Thoughtworks Technologies (India) Pvt Ltd, Pune, 411006, India.

Harshal Hayatnagarkar (H)

Engineering for Research (e4r™), Thoughtworks Technologies (India) Pvt Ltd, Pune, 411006, India.

Larisa N Soldatova (LN)

Department of Computing, Goldsmiths, University of London, London, SE14 6AD, United Kingdom.

Ross D King (RD)

The Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 58, Sweden.
Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB3 0AS, United Kingdom.
Alan Turing Institute, London, NW1 2DB, United Kingdom.

Classifications MeSH