Reder Gabriel K, Gower Alexander H, Kronström Filip, Halle Rushikesh, Mahamuni Vinay, Patel Amit, Hayatnagarkar Harshal, Soldatova Larisa N, King Ross D
The Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 58, Sweden.
Engineering for Research (e4r™), Thoughtworks Technologies (India) Pvt Ltd, Pune, 411006, India.
Bioinform Adv. 2023 Aug 2;3(1):vbad102. doi: 10.1093/bioadv/vbad102. eCollection 2023.
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.
人工智能（AI）驱动的实验室自动化——将机器人实验器具与自主软件代理相结合——是现代生物学中的一个强大趋势。我们开发了Genesis-DB，这是一个数据库系统，旨在通过为软件代理提供对大量结构化领域信息的访问来支持AI驱动的自主实验室。此外，我们提出了一种新的本体，用于在Genesis机器人科学家系统的框架中对自主进行的酵母微量恒化器培养的数据和元数据进行建模。我们展示了一个示例，说明Genesis-DB如何通过对酵母基因调控进行建模、指导未来假设的生成和实验设计来实现研究生命周期。Genesis-DB通过自动推理支持AI驱动的发现，其设计具有可移植性、通用性，并且易于扩展到其他AI驱动的分子生物学实验室数据及其他领域。
