The GNL Poised for Leadership in Artificial Intelligence Integration

Dr. Gene Olinger, Director of the Galveston National Lab, was recently named Associate Co-Director for AI Research within the UTMB Center for Artificial Intelligence.

Under Dr. Olinger's guidance and leadership, the GNL plans to advance the integration of artificial intelligence (AI) to modernize operations, strengthen biosafety oversight, and accelerate scientific discovery within high-containment and maximum-containment environments.

The lab is currently piloting structured AI-enabled administrative and scientific enablement tools under two complementary initiatives:

• ARISE (AI for Reproducibility, Integrity, Safety, and Excellence) enhancing documentation, training, safety compliance, and predictive maintenance
• GENESIS (Generative Networked Intelligence for Science Integration and Safety) advancing secure data integration, research reproducibility, and modern scientific workflows.

ARISE will employ AI-assisted and digital documentation technologies to enhance laboratory reproducibility, safety assurance, and procedural fidelity. Through predictive analytics, ARISE is anticipated to support maintenance forecasting, operational readiness, and biosafety performance monitoring in near real time. An initial ARISE application focuses on BSL4 freezer systems. Secure Wi-Fi infrastructure to be installed in 2026 will enable data collection and predictive analytics to forecast freezer performance, reduce unplanned downtime, and enable 48-hour failure warnings.

GENESIS functions as a research-enabling data and analytics platform designed to support scientific rigor, reproducibility, and operational excellence. The system integrates advanced machine learning, large language models, and supervised, human-in-the-loop AI tools to assist with the analysis of research datasets, laboratory performance metrics, and biosafety-related observations. By enabling secure, structured evaluation of diverse scientific and operational inputs, GENESIS will help strengthen study design, enhance data quality and traceability, and improve translational research efficiency. For example, GENESIS will be used to identify trends across experimental and biosafety datasets, supporting continuous improvement in laboratory practices and informing evidence-based containment procedures in alignment with federal guidance on responsible and trustworthy AI.

Together, these platforms establish a foundation for an AI-enabled biodefense ecosystem that strengthens laboratory performance, safety culture, and research innovation. A primary goal is to optimize operational efficiencies that enable uninterrupted research. Simultaneously, GNL is developing data repositories to support the long-term objective of advancing FDA and NIH New Approach Methodologies (NAMs) aimed at reducing or replacing animal research. These AI-enabled repositories will integrate historical animal data, real-world human data, digital twins, and microphysiological systems (MPS). These efforts will establish the tools needed to achieve this goal, ultimately enabling AI-enhanced research approaches that accelerate discovery while reducing time and costs for clinical translation while eliminating or reducing animal studies.

All AI development and deployment activities will adhere to federal guidance for responsible and trustworthy AI. The implementation of AI will include quarterly AI review boards and mandatory ethics assessments, ensuring that lessons learned (implementation strategies, governance frameworks, data protocols, and risk mitigation approaches) are systematically shared with our scientific partners.