MicroGrowAgents: Multi-Agent AI for Microbial Cultivation
Overview
MicroGrowAgents is an agent-based system for AI-driven microbial cultivation and growth media design. It bridges the microbial cultivation gap through AI-powered multi-agent systems that integrate knowledge graphs, machine learning, and experimental automation.
The Challenge: Designing growth media for novel or fastidious organisms is slow and largely manual. The knowledge needed — cultivation protocols, metabolic capabilities, chemical requirements — is scattered across literature, genomes, and culture collection databases, and no single model captures all of it.
The Solution: MicroGrowAgents coordinates a team of specialized agents, each focused on one source of evidence (literature, cross-organism analogy, genome function, media formulation). Their outputs are combined into organism-specific, evidence-based media recommendations grounded in the kg-microbe knowledge graph.
Specialized Agents
📚 LiteratureAgent
Mines 245+ papers for cultivation protocols, extracting growth conditions and media compositions from the published record.
🔁 AnalogyReasoningAgent
Performs cross-organism comparison and reasoning, transferring cultivation knowledge from well-characterized organisms to related, less-studied taxa.
🧬 GenomeFunctionAgent
Detects auxotrophies from genome annotations — built on 57 Bakta-annotated genomes spanning 667K features — to predict which nutrients an organism cannot synthesize and therefore requires in its media.
🧪 MediaFormulationAgent
Produces schema-driven media recommendations with evidence-based ingredient suggestions, assembling the other agents’ findings into a concrete, formulatable recipe.
Key Achievements
- 864,363 validated species across bacteria, archaea, fungi, and protozoa (GTDB + LPSN + NCBI)
- Multi-modal reasoning combining literature mining, metabolic modeling (FBA / gap-filling), and chemical similarity (208K+ embeddings)
- Genome-guided design for organism-specific media formulation
Technical Architecture
Multi-Agent Reasoning Pipeline
Target Organism
↓
┌─────────────────────────────────────────────┐
│ LiteratureAgent AnalogyReasoningAgent │
│ GenomeFunctionAgent MediaFormulationAgent │
└─────────────────────────────────────────────┘
↓ (evidence integration over kg-microbe)
Organism-Specific Media Recommendation
Integration with the CultureBotAI Ecosystem
- Depends on:
- kg-microbe - knowledge graph foundation
- MicroMediaParam - chemical compound mappings
- eggnogtable - genome functional annotations
- MATE-LLM - literature extraction
- Feeds Into:
- Media formulation recommendations
- PFASCommunityAgents - consortium design
- Works With:
- MicroGrowLink - complementary graph/transformer-based prediction approach
Repository & Documentation
- GitHub: github.com/CultureBotAI/MicroGrowAgents
- License: BSD-3-Clause
- Languages: Python, HTML, Shell, R
- Topics:
ai4curation·monarchinitiative
Related Tools
- CultureMech - Microbial culture media knowledge graph (10,000+ recipes)
- MediaIngredientMech - LLM-assisted ingredient ontology mapping
- CommunityMech - Microbial community interaction modeling
- MicroGrowLink - Graph-based growth media prediction
- kg-microbe - Central knowledge graph for microbial cultivation
Research Impact
MicroGrowAgents is part of the KG-Microbe knowledge graph ecosystem developed at Lawrence Berkeley National Laboratory. It supports:
- AI-driven media design for novel and fastidious organisms
- Genome-guided prediction of nutritional requirements
- Evidence-based cultivation protocol synthesis from literature
- Data-driven cultivation optimization
Citation: See the KG-Microbe preprint for details on the broader knowledge graph ecosystem.
Contact & Collaboration
For questions about MicroGrowAgents or collaboration opportunities:
- Principal Investigator: Dr. Marcin P. Joachimiak
- Email: mjoachimiak@lbl.gov
- Organization: CultureBotAI
- Laboratory: Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory