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Related Experiment Video

Updated: Jul 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Agentic AI integrated with scientific knowledge: laboratory validation in systems biology.

Daniel Brunnsåker¹, Alexander Howard Gower¹, Prajakta Naval²

¹Department of Computer Science and Engineering, Chalmers University of Technology , Gothenburg, Sweden.

Journal of the Royal Society, Interface

|July 7, 2026

Summary

This study introduces an AI framework combining large language models (LLMs) with lab automation for biological discovery. The system reliably validates hypotheses, identifying novel interactions in yeast.

Keywords:

automation of science inductive logic programming laboratory automation large language models machine learning systems biology

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Last Updated: Jul 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

Systems Biology
Artificial Intelligence in Science
Bioinformatics

Background:

Automation and large language models (LLMs) accelerate scientific discovery but face challenges with logical reasoning.
Integrating LLMs with laboratory automation requires robust frameworks for coherence and reliability.

Purpose of the Study:

To develop a framework integrating LLM-based agents with laboratory automation for biological discovery.
To improve the coherence and reliability of automated scientific workflows.
To enable integrated hypothesis validation and refinement in systems biology.

Main Methods:

A framework combining LLM agents with laboratory automation, guided by logical scaffolds.
Symbolic relational learning, structured vocabularies, and experimental constraints.
Automated cell-culture and metabolomics platforms for hypothesis validation.
A graph database with controlled vocabularies for capturing hypotheses, experiments, and data.

Main Results:

The integrated system demonstrated improved coherence and reliability in automated workflows.
Novel interactions in Saccharomyces cerevisiae were identified, including glutamate-induced growth inhibition and aminoadipate's partial rescue of formic-acid stress.
Extended existing ontologies and presented a novel representation of scientific hypotheses using description logics.

Conclusions:

The developed framework shows potential for a reliable machine-driven discovery process in systems biology.
Integration of AI agents and lab automation can overcome LLM limitations in logical structuring.
This approach facilitates efficient hypothesis generation, validation, and refinement.