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Minisymposium Presentation
AI-Driven Systems Biology for Addiction: Large-Scale Multi-Omics Network Modeling and AI Agents for Mechanistic Discovery
Presenter
Matthew Lane is a Graduate Research Assistant in the Computational and Predictive Biology Group at Oak Ridge National Laboratory working under Dr. Dan Jacobson.His current research centers around the application of statistical, graph theoretical, machine and deep learning methods for prediction and analysis of complex biological systems using the leadership class systems Andes, Summit, and Perlmutter.Research undertakings currently include:- Using Explainable AI to create massive Predictive Expression Networks for use in Multiplex Omics models.- Employing geometric deep learning for node embedding and link prediction on large and sparse networks.- Metabolomic profile network creation through peak extraction and statistical processing of LC/GC-MS data.- Scientific Software engineering for the production of well-tested and documented packages for publication.- Topological Perturbation of networks for the phenotypic prediction of genetic modulation.Matthew Lane earned his M.S. in Computer Science under Dr. Sharlee Climer at the University of Missouri in St. Louis, working on network theory techniques for the analysis of cerebrospinal fluid metabolites in patients of Alzheimer’s disease. After his tenure at the University of Missouri, he worked as a software engineer at Bayer Crop Science, developing software for the collection, storage, and analysis of crops in the field.He actively volunteers to teach coding and robotics to local high school students and community members working with the East Tennessee STEM Hub.
Description
Understanding the genetic and molecular underpinnings of addiction and related disorders requires integrative approaches that leverage large-scale omics data, network biology, and artificial intelligence. This work presents a systems biology framework that combines predictive expression networks, foundation models, and AI agents to elucidate mechanisms underlying opioid and nicotine addiction. By integrating genome-wide association studies (GWAS), transcriptomics, and multiplex network modeling, we identify gene clusters linked to addiction-related phenotypes, emphasizing shared mechanisms between opioid use and smoking cessation. Using the MENTOR framework, we partition genes of interest into mechanistically coherent clades, revealing significant overlap between addiction pathways. Network-based analyses uncover key regulators, including BDNF/NTRK2 and MAPK signaling, which influence neuronal plasticity and reinforcement learning. AI-driven interpretation automates gene-function annotation, improving mechanistic inference. Further, retrieval-augmented generation (RAG) agents and reinforcement learning models facilitate high-throughput interpretation of biological networks, accelerating hypothesis generation. This study highlights AI’s role in translating multi-omics data into actionable insights for addiction biology. The framework extends to broader disease contexts, offering a scalable model for systems medicine. Future directions include validation through retrospective clinical trials and experimental assays, emphasizing the potential for AI-guided therapeutic discovery.