Research Focus

CultureBotAI’s research spans the intersection of artificial intelligence, machine learning, and microbiology, with a focus on transforming how we understand and manipulate microbial systems.

Primary Research Areas

🦠 Cultivation of Isolated and Novel Organisms

We develop AI-powered approaches to successfully cultivate previously unculturable microorganisms. Our work focuses on:

  • Novel isolation techniques guided by machine learning predictions
  • Automated culture monitoring using computer vision and sensor networks
  • Optimization of growth media through iterative AI-driven experimentation
  • Scaling cultivation methods from lab bench to industrial applications

Key Challenges Addressed:

  • The “great plate count anomaly” - cultivating the 99% of microbes that resist standard cultivation
  • Identifying optimal growth conditions for fastidious organisms
  • Reducing time and resources required for successful cultivation

🔬 Culture Optimization Through Data-Driven Approaches

Our culture optimization research leverages big data and machine learning to dramatically improve cultivation success rates:

  • Environmental parameter optimization (temperature, pH, oxygen, nutrients)
  • Media composition prediction using computational approaches
  • Co-culture design for synthetic microbial communities

Technologies Employed:

  • High-throughput screening platforms
  • Automated liquid handling systems
  • Real-time monitoring sensors
  • Multi-objective optimization algorithms
  • Graph learning

🧠 Growth Preference Prediction Using AI/ML Methods

We develop sophisticated predictive models to understand and forecast microbial behavior:

Machine Learning Approaches

  • Deep neural networks for complex pattern recognition in microbial data
  • Gradient boosted decision trees for predictive modeling
  • Ensemble methods combining multiple predictive approaches

Data Integration:

  • Genomic and metagenomic sequences
  • Environmental metadata
  • Cultivation historical data
  • Literature-derived growth parameters

Knowledge Graph Development

kg-microbe: Comprehensive Microbial Knowledge Integration

Our flagship kg-microbe project represents a breakthrough in microbial data integration:

  • Multi-source data integration from major biological databases
  • Ontology-driven organization ensuring semantic consistency
  • Machine-readable formats enabling automated reasoning
  • Community-driven updates ensuring data currency

Data Sources Integrated:

  • NCBI Taxonomy
  • UniProt protein databases
  • Environmental ontologies
  • Cultivation databases
  • Literature-derived facts

Current Projects

🔬 Automated Culture Monitoring Platform

Development of AI-powered systems for continuous culture monitoring using:

  • Iterative computational-experimental process
  • High-throughput cultivation
  • Physical parameter scanning

📊 Predictive Growth Modeling

Creating comprehensive models that predict:

  • Optimal growth conditions for target organisms
  • Media composition requirements
  • Co-culture compatibility

🌐 Knowledge Graph Applications

Expanding kg-microbe capabilities for:

  • Automated literature mining and fact extraction
  • Cross-organism growth condition prediction
  • Novel organism property inference
  • Integration with laboratory information systems

Collaborative Research

We actively collaborate with:

  • Academic institutions developing novel cultivation techniques
  • Industry partners scaling up microbial production processes
  • Government laboratories studying environmental microorganisms
  • Open source communities building computational biology tools

Future Directions

Short-term Goals (1-2 years)

  • Release production version of kg-microbe knowledge graph
  • Deploy automated culture monitoring in partner laboratories
  • Publish comprehensive growth prediction models

Long-term Vision (3-5 years)

  • Achieve high success rate in novel organism cultivation
  • Enable fully automated microbial cultivation pipelines
  • Integrate CultureBotAI tools into standard laboratory workflows

Publications & Preprints

For detailed methodology and results from our research, see our Publications page.

Get Involved

Interested in collaborating on microbial cultivation research? We welcome:

  • Research partnerships with academic and industry groups
  • Student researchers seeking challenging projects
  • Open source contributors to our software tools
  • Data contributors sharing cultivation datasets

Contact us to explore collaboration opportunities.