AI-Driven Microbiome Engineering: Synthetic Microbial Consortia, Metagenomics & Predictive Bioreactor Modeling
From Microbial Communities to Predictive Bioprocesses through Metagenomics, Synthetic Consortium Design, Machine Learning & Virtual Live-Lab Experimentation
About This Course
This three-day, hands-on workshop explores how metagenomics, microbial ecology, synthetic microbial consortia, and predictive modeling can be integrated for microbiome engineering and sustainable bioprocessing.
Participants will progress from microbial community profiling and functional metagenomic analysis to understanding microbial interactions, metabolic relationships, and synthetic consortium design. The workshop also introduces established computational tools and machine-learning approaches to investigate relationships between microbial communities and bioreactor performance.
The workshop includes practical training with Microbe Lab at Live-Labs.org, an interactive Virtual Live Lab where participants will analyze microbial communities, explore functional guilds, perform virtual anaerobic bioreactor simulations, and examine how different operating conditions influence process stability and performance.
The workshop connects computational microbiome research with applications in environmental biotechnology, wastewater treatment, anaerobic digestion, bioenergy production, and circular bioprocessing, providing participants with practical experience in computational analysis, predictive modeling, and virtual experimentation.
Aim
To develop practical skills in metagenomic data analysis, microbial interaction modeling, synthetic consortium design, and predictive bioprocess analysis, enabling participants to investigate microbiome–bioreactor relationships through computational tools and virtual experimentation.
Workshop Objectives
Participants will learn to:
- Analyze taxonomic and functional microbiome profiles using metagenomic datasets.
- Interpret microbial diversity, abundance patterns, and metabolic potential.
- Explore microbial interactions, syntrophy, and functional guilds.
- Understand synthetic microbial consortium design and community stability.
- Construct and interpret microbial association networks.
- Integrate microbial community data with bioprocess-performance indicators.
- Apply machine-learning methods for introductory bioprocess prediction.
- Understand anaerobic digestion modeling and microbial perturbation.
- Perform microbial community analysis and virtual bioreactor experimentation using Microbe Lab.
- Compare simulated operating scenarios and identify potential process bottlenecks.
Workshop Structure
📅 Day 1 – Shotgun Metagenomics, Microbial Community Profiling & Functional Microbiome Analysis
- Introduction to microbiome engineering and applications in environmental and industrial biotechnology.
- Understanding shotgun metagenomics, sequencing datasets, and microbial taxonomic profiling.
- Microbial abundance analysis, alpha and beta diversity, and community composition.
- Taxonomic profiling using MetaPhlAn and functional pathway analysis using HUMAnN.
- Functional interpretation of microbial genes, metabolic pathways, and ecological roles.
- Core microbiome, functional redundancy, and microbial ecosystem resilience.
- Identification of hydrolytic, fermentative, acetogenic, and methanogenic microbial guilds.
- Connecting microbial community composition with biological functions and anaerobic bioprocesses.
🛠️ Hands-On Lab: Metagenomic Data Exploration & Functional Microbiome Profiling
- Analyze a curated metagenomic abundance dataset and generate microbial taxonomic profiles.
- Compare microbial composition and functional pathway abundance across samples.
- Visualize major microbial groups and identify functional signatures associated with anaerobic bioprocesses.
Hands-On Tools: Google Colab | Python | Pandas | Matplotlib | MetaPhlAn/HUMAnN Output Datasets
Research Tools Introduced: MetaPhlAn | HUMAnN | QIIME 2
📅 Day 2 – Microbial Interaction Networks, Synthetic Consortia & Machine Learning for Bioprocess Prediction
- Microbial cooperation, competition, syntrophy, and metabolite cross-feeding.
- Microbial association networks and identification of potentially influential taxa.
- Introduction to microbial network inference using SPIEC-EASI.
- Principles of synthetic microbial consortium design, metabolic complementarity, and community stability.
- Introduction to community metabolic modeling using MICOM and COBRApy.
- Functional microbial guilds and their contributions to anaerobic digestion.
- Integrating microbial abundance profiles with methane production and reactor-performance indicators.
- Random Forest-based bioprocess prediction, model evaluation, and feature importance.
🛠️ Hands-On Lab: Microbial Network Analysis & Predictive Bioprocess Modeling
- Construct and visualize a microbial association network using abundance data.
- Identify potentially important microbial groups and propose a conceptual synthetic microbial consortium.
- Build a basic Random Forest regression model to investigate relationships between microbiome features and bioprocess performance.
- Evaluate model performance and interpret important predictive variables.
Hands-On Tools: Google Colab | Python | Pandas | NetworkX | Scikit-learn | Matplotlib
Research Tools Introduced: SPIEC-EASI | Cytoscape | MICOM | COBRApy | XGBoost | SHAP
📅 Day 3 – Predictive Bioreactor Modeling, Microbiome Optimization & Virtual Live-Lab Experimentation
- Functional microbial guilds involved in hydrolysis, acidogenesis, acetogenesis, and methanogenesis.
- Microbial syntrophy, interspecies hydrogen transfer, and metabolic dependencies in anaerobic ecosystems.
- Integration of microbiome characteristics with reactor-performance indicators, including VFAs, pH, methane production, and organic loading rate.
- Introduction to Anaerobic Digestion Model No. 1 (ADM1) and its applications in bioreactor modeling.
- Microbial community perturbation, bioaugmentation, and process-stability assessment.
- Predictive modeling of bioreactor performance and identification of potential process bottlenecks.
- Effects of pH, temperature, substrate conditions, and hydraulic retention time on anaerobic digestion.
- Bioreactor scenario analysis, process optimization, and research interpretation.
🛠️ Hands-On Lab 1: Predictive Bioreactor Modeling & Process Analysis
- Explore an anaerobic digestion dataset containing reactor operating parameters and performance measurements.
- Analyze relationships between operating conditions, VFAs, and methane production.
- Apply an introductory predictive model to investigate bioreactor-performance trends.
- Interpret influential process parameters and potential indicators of reactor instability.
Hands-On Tools: Google Colab | Python | Pandas | Scikit-learn | Matplotlib
Research Framework Introduced: ADM1 – Anaerobic Digestion Model No. 1
🧪 Hands-On Lab 2: Microbe Lab – Microbiome Engineering & Virtual Bioreactor Simulation
Participants will use the Microbe Lab from Live-Labs.org to explore microbial community characteristics and investigate simulated anaerobic digester performance.
- Microbial Dataset Assessment: Select a curated sequencing dataset and examine data characteristics, quality indicators, and analysis eligibility.
- Microbial Community Profiling: Explore microbial taxonomic composition, relative abundance, and unclassified microbial reads.
- Functional Guild Interpretation: Investigate inferred metabolic potential and microbial groups associated with anaerobic digestion.
- Virtual Bioreactor Simulation: Run a baseline anaerobic digester simulation and evaluate predicted process responses.
- Process Perturbation & Scenario Analysis: Modify pH, temperature, substrate conditions, and hydraulic retention time to investigate changes in simulated performance.
- Research Interpretation: Compare baseline and alternative scenarios, identify potential process limitations, and propose microbiome-engineering recommendations.
Hands-On Tools: Microbe Lab: Live-Labs.org | Microbial Community Profiling | Functional Guild Analysis | Virtual Anaerobic Digester Simulation
Who Should Enrol?
- Microbiology and Biotechnology Students
- Bioinformatics and Computational Biology Researchers
- Metagenomics and Microbial Ecology Researchers
- Synthetic Biology and Microbial Consortium Researchers
- Environmental Biotechnology Researchers
- Bioprocess and Biochemical Engineers
- Wastewater Treatment and Anaerobic Digestion Researchers
- Bioenergy and Circular Bioeconomy Researchers
- Industrial Microbiologists
- Ph.D. Scholars and Postdoctoral Researchers
- Faculty Members and Academicians
- Industry Professionals working in Microbial and Bioprocess Technologies
Important Dates
Registration Ends
November 3, 2026
IST
Workshop Dates
November 3, 2026 – November 5, 2026
IST 5:30 PM
Workshop Outcomes
After completing the workshop, participants will be able to:
- Analyze microbial taxonomic and functional abundance profiles.
- Interpret microbial diversity and metabolic potential.
- Understand microbial cooperation, syntrophy, and functional relationships.
- Construct and interpret simplified microbial association networks.
- Explore synthetic microbial consortium design and community stability.
- Identify functional microbial guilds associated with anaerobic digestion.
- Integrate microbiome characteristics with reactor-performance datasets.
- Develop introductory machine-learning models for bioprocess prediction.
- Interpret predictive features associated with methane production and process stability.
- Understand anaerobic digestion modeling and microbial perturbation strategies.
- Perform microbial community assessment and virtual bioreactor simulation using Microbe Lab.
- Compare simulated operating scenarios and identify potential process bottlenecks.
- Develop research-informed recommendations for microbiome engineering and bioprocess improvement.
Fee Structure
Student Fee
₹2999 | $75
Ph.D. Scholar / Researcher Fee
₹3999 | $85
Academician / Faculty Fee
₹4999 | $95
Industry Professional Fee
₹5999 | $109
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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