| layout | default |
|---|---|
| title | MicroGrowAgents |
| description | Agent-based AI system for microbial cultivation and growth media design, integrating knowledge graphs, literature mining, and genome-guided reasoning across 864,363 validated species. |
| permalink | /microgrowagents/ |
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 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. The RuleML/GOBLIN lecture describes it as a hierarchical agentic-AI framework of 100+ specialist agents; the four agent roles documented below are the ones described in detail.
Mines 245+ papers for cultivation protocols, extracting growth conditions and media compositions from the published record.
Performs cross-organism comparison and reasoning, transferring cultivation knowledge from well-characterized organisms to related, less-studied taxa.
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.
Produces schema-driven media recommendations with evidence-based ingredient suggestions, assembling the other agents' findings into a concrete, formulatable recipe.
- 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
Target Organism
↓
┌─────────────────────────────────────────────┐
│ LiteratureAgent AnalogyReasoningAgent │
│ GenomeFunctionAgent MediaFormulationAgent │
└─────────────────────────────────────────────┘
↓ (evidence integration over kg-microbe)
Organism-Specific Media Recommendation
-
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
- GitHub: github.com/CultureBotAI/MicroGrowAgents (private repository — public release planned)
- Preprint: MicroGrowAgents: An Agentic AI System for Microbial Cultivation Engineering (bioRxiv, 2026)
- License: BSD-3-Clause
- Languages: Python, HTML, Shell, R
- Topics:
ai4curation·monarchinitiative
- X-Mech Suite overview - All ten Mechs, their shared vocabulary and cross-references, and the culturebotai-claw orchestrator
- TaxonMech - Microbial taxa and strains grounded in NCBI Taxonomy, harmonized with GTDB, LPSN and BacDive
- HabitatMech - Habitats harmonized from GOLD, BacDive, PREGO and Madin et al. into ENVO-grounded records
- CommunityMech - Microbial community interaction modeling
- TraitMech - Autonomous knowledge factory for microbial ecophysiological traits
- CellStructureMech - Microbial cell structures, between the trait and protein layers
- ProteinTraitsMech - Protein sequence, structure, and function traits
- NaturalProductMech - Natural product structures with their producer organisms and gene clusters
- AntibioticMech - Antimicrobial structures harmonizing ChEBI and CARD/ARO
- MediaIngredientMech - LLM-assisted ingredient ontology mapping
- CultureMech - Autonomous knowledge factory for microbial culture media (6,286 canonical media)
- MicroGrowLink - Graph-based growth media prediction
- kg-microbe - Central knowledge graph for microbial cultivation
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
Applications: The RuleML/GOBLIN lecture reports high-throughput results in which MicroGrowAgents and KOGUT improved growth and rare-earth-element depletion in Methylorubrum extorquens AM1.
Citation: Naseem, S., Miller, M. A., Martinez-Gomez, N. C., Sun, N., & Joachimiak, M. P. (2026). MicroGrowAgents: An Agentic AI System for Microbial Cultivation Engineering. bioRxiv. 10.64898/2026.06.04.729985
See also the KG-Microbe publication in GigaScience for details on the broader knowledge graph ecosystem.
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
- Naseem S, Miller MA, Martinez-Gomez NC, Sun N, Joachimiak MP. MicroGrowAgents: An Agentic AI System for Microbial Cultivation Engineering. bioRxiv. 2026. doi:10.64898/2026.06.04.729985
- Santangelo BE, Hegde H, Caufield JH, Reese J, Kliegr T, Hunter LE, Lozupone CA, Mungall CJ, Joachimiak MP. KG-Microbe — Building Modular and Scalable Knowledge Graphs for Microbiome and Microbial Sciences. GigaScience. 2026;giag077. doi:10.1093/gigascience/giag077
- Máša P, Kliegr T, Joachimiak MP. Explainable rule-based prediction of cultivation media for microbes. Computational and Structural Biotechnology Journal. 2025;27:5194–5206. doi:10.1016/j.csbj.2025.10.014 · free full text
- Joachimiak MP. Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1 [software]. DOE CODE; 2025. doi:10.11578/dc.20260210.3 · DOE CODE 175162
- Joachimiak MP. "RuleML/GOBLIN COST Action Lecture on Data Science: Teaching AI to Teach Humans About Microbiology" [talk]. RuleML / COST GOBLIN Action Seminar; 2026. Recording {: .bibliography}