MicroGrowAgents: An Agentic AI System for Microbial Cultivation Engineering

Microbial cultivation optimization remains labor-intensive and inefficient, requiring extensive experimental screening to identify suitable growth conditions. Traditional one-factor-at-a-time approaches are particularly ineffective for exploring complex, multidimensional nutrient parameter spaces. We present MicroGrowAgents, an AI-driven, agent-based system that automates the design of optimized growth media through integration of knowledge graphs, metabolic modeling, and optimal experimental design. The system employs 28 specialized agents and 50 skills that query structured biological knowledge (KG-Microbe: 864,363 validated species), mine literature evidence (245+ papers), perform genome-guided design (57 genomes, 667,000+ annotated features), and generate statistically optimal experimental designs using the MaxPro algorithm. We applied the approach to Methylorubrum extorquens AM1 by cultivating 70 designed conditions in quadruplicate and assessing three concurrent objectives: biomass (OD600 at 740 nm), redox activity (Abs590 Biolog proxy), and lanthanide uptake (residual Nd measured by arsenazo III). Monte-Carlo resampling of the replicate-level uncertainty (1000 iterations) identified a single stable Pareto-optimal medium, MPOB_058 (membership frequency 0.99), together with two borderline candidates and six rare appearers, providing a robust anchor set for subsequent rounds of design-build-test-learn. The integration of chemical similarity search (208,000+ embeddings), metabolic gap analysis, and multi-modal reasoning enables evidence-based hypothesis generation that reduces experimental burden while accelerating discovery of growth-promoting conditions. MicroGrowAgents provides complete provenance tracking with cryptographic checksums and 90.5% literature citation coverage, advancing reproducible, data-driven approaches to microbial cultivation.