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DTSTAMP:20250822T115805Z
LOCATION:Room 6.0D13
DTSTART;TZID=Europe/Stockholm:20250616T125000
DTEND;TZID=Europe/Stockholm:20250616T132000
UID:submissions.pasc-conference.org_PASC25_sess137_msa236@linklings.com
SUMMARY:AI-Driven Systems Biology for Addiction: Large-Scale Multi-Omics N
 etwork Modeling and AI Agents for Mechanistic Discovery
DESCRIPTION:Daniel Jacobson and Matthew Lane (Oak Ridge National Laborator
 y)\n\nUnderstanding the genetic and molecular underpinnings of addiction a
 nd related disorders requires integrative approaches that leverage large-s
 cale omics data, network biology, and artificial intelligence. This work p
 resents a systems biology framework that combines predictive expression ne
 tworks, foundation models, and AI agents to elucidate mechanisms underlyin
 g opioid and nicotine addiction. By integrating genome-wide association st
 udies (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 fram
 ework, 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 i
 nference. Further, retrieval-augmented generation (RAG) agents and reinfor
 cement learning models facilitate high-throughput interpretation of biolog
 ical networks, accelerating hypothesis generation. This study highlights A
 I’s role in translating multi-omics data into actionable insights for addi
 ction biology. The framework extends to broader disease contexts, offering
  a scalable model for systems medicine. Future directions include validati
 on through retrospective clinical trials and experimental assays, emphasiz
 ing the potential for AI-guided therapeutic discovery.\n\nDomain: Chemistr
 y and Materials, Climate, Weather, and Earth Sciences, Life Sciences, Phys
 ics, Computational Methods and Applied Mathematics\n\nSession Chair: Micha
 el Kirby (Colorado State University)\n\n
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