Omics Multi-Agent Pipeline for Integrated Data & Pathways

The tutorial begins by generating realistic synthetic datasets that mimic the complex trends seen in real biological experiments. By scripting controlled perturbations across transcriptomic, proteomic, and metabolomic layers, the authors create a sandbox that lets each agent be tested on predictable, yet challenging, data. This foundational step ensures that downstream analyses can be benchmarked and that each module’s output can be traced back to a known input.

With data in hand, the pipeline launches a series of purpose‑built agents. The statistical agent performs differential expression and variance analysis, flagging significant changes across all omics layers. Next, a network inference agent constructs interaction maps, leveraging co‑expression and correlation to reveal putative regulatory modules. A dedicated pathway enrichment agent then overlays these networks onto curated pathway databases, highlighting biological processes that are over‑represented in the data. Finally, a drug repurposing agent scans chemical libraries for compounds that can modulate the identified pathways, closing the loop from data to therapeutic hypotheses.

What sets this architecture apart is its modularity and clarity. Each agent is a self‑contained component that can be swapped, updated, or replaced without disturbing the rest of the system. The tutorial also discusses best practices for orchestrating these agents using a lightweight workflow manager, ensuring reproducibility and scalability. By the end of the session, readers have a fully functional, multi‑agent pipeline that transforms raw omics measurements into a prioritized list of biological insights and potential drug candidates, all within a single, coherent workflow.