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. Author SummaryGrowing microbes in the laboratory is like figuring out the right recipe: too much or too little of any nutrient and they barely grow. Scientists have traditionally tested ingredients one at a time, an approach that is slow, expensive, and poorly suited to the dozens of interacting nutrients that real microbes need. We built MicroGrowAgents, an AI system that acts like a team of specialist scientists working together. It consults structured biological databases, reads the published literature, inspects microbial genomes, and uses statistical experimental design to recommend nutrient combinations worth testing in the laboratory. Applied to Methylorubrum extorquens AM1, a methanol-eating bacterium of interest for capturing rare-earth elements, the system designed 70 growth conditions and identified one robust winner that performed well across cell growth, metabolism, and lanthanide uptake. The software is free and open-source, helping any laboratory adopt these tools.