An AI Agentic Framework for Automated Reconstruction and Analysis of Genome-Scale Metabolic Models

Genome-scale metabolic modeling serves as a critical interface between genomic information and hypotheses regarding cellular functionality; however, its broader adoption is impeded by substantial programming demands and fragmented software ecosystems. In response, we present GemAgents, a language-agent framework engineered to automate the processes of metabolic modeling and analysis. This system allows biologists to express research objectives through natural language, thereby eliminating the necessity for manual coding of analytical workflows. GemAgents distinctly delineates the interpretation of language-based tasks and the orchestration of computational tools from the deterministic aspects of scientific computation, grounding model construction and evaluation in explicit input data, reaction evidence, and clearly defined modeling assumptions. The reconstruction pipeline includes sequence annotation, mapping of evidence to reactions, formulation of gene–protein–reaction associations, medium-constrained gap filling, and model exportation. An independent verification module evaluates mass and charge balance, identifies erroneous metabolite and energy generation, and employs a counterexample-guided repair strategy to suggest corrections that preserve specified metabolic functions. Furthermore, an integrated auto-analysis extension facilitates direct model interrogation, the design of non-native metabolic pathways, and the in silico integration and assessment of heterologous pathways. Engineering case studies utilizing publicly available genomes and reference models illustrate the robustness and versatility of these components. By combining natural language interaction with transparent, model-based computational processes, GemAgents offers a comprehensive solution that removes programming obstacles and effectively translates biological questions into experimentally testable computational hypotheses.