Advancing Functional Genomic Interpretation via Multi-Agent Collaborative Architectures Integrating Large Language Model Reasoning and Hierarchical Biological Knowledge Graphs

Abstract

The interpretation of functional genomic data represents a critical bottleneck in precision medicine, characterized by the staggering complexity of mapping high-dimensional genetic variants to physiological biological outcomes. Traditional computational pipelines often fail to integrate the heterogeneous, multi-scale nature of biological knowledge, which spans from molecular interactions to systemic clinical responses. This paper proposes a novel system architecture based on a Multi-Agent Collaborative Framework that synergistically integrates Large Language Model (LLM) reasoning with Hierarchical Biological Knowledge Graphs (HBKGs). By delegating specialized tasks—such as variant prioritization, metabolic pathway enrichment, and clinical literature synthesis—to autonomous intelligent agents, the system enables a holistic and context-aware interpretation of genomic variants. We provide an exhaustive system-level analysis of this architecture, evaluating the structural trade-offs between agentic autonomy and deterministic grounding through symbolic knowledge bases. The discussion further explores the infrastructure requirements for large-scale deployment, focusing on the sustainability of massive-scale reasoning and the robustness of the system against biological misinformation. We also address critical governance and policy implications, particularly regarding algorithmic fairness, data sovereignty, and the ethical use of autonomous agents in clinical decision support. By framing functional genomics as a large-scale socio-technical systems challenge, this work provides a roadmap for the next generation of intelligent, explainable, and ethically grounded genomic infrastructures capable of accelerating therapeutic discovery and personalized care.