Multi-Agent Pipeline for Omics Integration & Pathways

Integrating transcriptomic, proteomic, and metabolomic data has long been a bottleneck in systems biology, primarily because each data type follows distinct statistical properties and noise profiles. Traditional monolithic pipelines struggle to accommodate the heterogeneity of these datasets, leading to sub‑optimal feature extraction and downstream inference. A multi‑agent architecture, where each agent specializes in a particular analytical task, offers a modular solution. Agents can operate in parallel, communicate through a lightweight message bus, and dynamically adjust their behavior based on intermediate results, thereby scaling gracefully as data volumes grow. This structure also simplifies debugging and version control, as each agent can be independently updated or replaced without disrupting the entire workflow.

The tutorial begins by generating synthetic datasets that faithfully reproduce realistic biological trends, such as co‑expression modules and metabolite fluxes. Once the data are in place, the first agent performs rigorous statistical filtering and batch‑effect correction, producing a clean feature matrix. The second agent constructs a multi‑layer interaction network using correlation, mutual information, and known protein‑protein interactions, and then applies community detection to delineate functional modules. A third agent performs pathway enrichment against curated databases, translating modules into biological themes, while a fourth agent evaluates drug repurposing opportunities by overlaying the inferred network onto drug‑target interaction maps. Throughout the process, agents log intermediate artifacts, enabling reproducibility and easy debugging.

By the end of the tutorial, users have a fully automated pipeline that transforms raw omics measurements into interpretable pathway maps and therapeutic hypotheses. The modular design simplifies maintenance: new agents for, say, single‑cell resolution or spatial transcriptomics can be plugged in without re‑engineering the entire workflow. Moreover, the agent‑based approach lends itself to cloud deployment and real‑time data ingestion, making it a practical tool for both academic research and translational drug discovery. The example code and notebooks accompany the guide, encouraging readers to experiment and extend the system to their own datasets.