AI-Driven Microbiome Engineering: Synthetic Microbial Consortia, Explainable AI & Predictive Bioreactor Optimization
From Microbial Community Profiling to AI-Powered Consortium Design and Virtual Bioreactor Optimization.
About This Course
Microbial communities play a vital role in environmental sustainability, waste-to-energy technologies, anaerobic digestion and industrial bioprocessing. Understanding their interactions and influence on bioreactor performance is essential for developing efficient and sustainable bioprocesses.
This 3-day hands-on workshop integrates functional metagenomics, synthetic microbial consortia, microbial network analysis, machine learning and explainable AI (XAI) for predictive microbiome engineering. Participants will explore microbial profiling using MetaPhlAn and HUMAnN, investigate microbial interactions and develop predictive models using Random Forest and SHAP.
Through Python and Microbe Lab (Live-Labs.org), participants will gain practical exposure to bioreactor stability prediction, methane-yield forecasting, virtual microbial perturbation and scenario-based optimization, enabling data-driven approaches to improve microbial ecosystem stability, biogas production and sustainable bioprocess performance.
Aim
To equip participants with practical skills in functional metagenomics, synthetic microbial consortium design, explainable AI and predictive bioreactor optimization for sustainable biotechnology and industrial bioprocess applications.
Workshop Objectives
- Understand microbial community profiling, functional metagenomics and ecosystem stability.
- Explore microbial interactions, metabolic cooperation and synthetic consortium design.
- Analyze microbial association networks and functional relationships.
- Develop Random Forest models for bioprocess performance prediction.
- Apply SHAP-based explainable AI to identify key microbial and operational factors.
- Investigate methane-yield prediction and anaerobic bioreactor stability.
- Explore virtual microbial perturbation and scenario-based optimization using Microbe Lab.
Workshop Structure
📅 Day 1: Functional Metagenomics, Methanogenic Pathways & Microbiome Stability
Topics Covered:
- Shotgun metagenomics and microbial community profiling.
- Taxonomic and functional analysis using MetaPhlAn and HUMAnN.
- Microbial diversity, ecological resilience and functional redundancy.
- Methanogenic pathways, microbial guilds and syntrophic interactions.
- Introduction to metagenome-assembled genomes (MAGs).
- Comparative analysis of stable and disturbed anaerobic digester microbiomes.
🧪 Hands-On: Functional Metagenomic Profiling
Analyze curated microbial datasets, visualize community composition, compare functional pathways and identify potential methanogenesis-associated indicators.
🧰 Tools: Python, Google Colab, Pandas, Matplotlib, MetaPhlAn/HUMAnN prepared outputs.
📅 Day 2: Synthetic Microbial Consortia, Network Biology & Explainable AI
Topics Covered:
- Microbial cooperation, competition and metabolic cross-feeding.
- Microbial interaction networks and SPIEC-EASI concepts.
- Synthetic microbial consortium design and functional complementarity.
- Introduction to metabolic modeling using MICOM and COBRApy.
- Random Forest-based bioprocess performance prediction.
- Model validation and explainable AI using SHAP.
- Identification of influential microbial and operational features.
🧪 Hands-On: Microbial Network Analysis & AI Prediction
Construct microbial association networks, propose a synthetic consortium, train a Random Forest model and interpret influential features using SHAP.
🧰 Tools: Python, Google Colab, NetworkX, Scikit-learn, SHAP, Pandas, Matplotlib. Prepared SPIEC-EASI/MICOM outputs for demonstration.
📅 Day 3: AI-Based Bioreactor Stability Prediction & Virtual Optimization
Topics Covered:
- Anaerobic digestion, microbial syntrophy and methane production.
- Effects of pH, temperature, hydraulic retention time and organic loading rate.
- Methane-yield prediction and bioreactor stability indicators.
- VFA accumulation, acidification and process instability.
- Introduction to ADM1 and hybrid AI–mechanistic modeling.
- Virtual microbiome perturbation and bioreactor scenario optimization.
- Emerging applications of AI-based digital bioprocess monitoring.
🧪 Hands-On: Predictive Bioreactor Modeling & Virtual Simulation
Analyze anaerobic digestion datasets, predict methane performance, compare stability scenarios and explore microbial perturbations using Microbe Lab.
🧰 Tools: Python, Google Colab, Scikit-learn, Matplotlib, Microbe Lab (Live-Labs.org), prepared ADM1 comparison results.
Who Should Enrol?
- Biotechnology & Microbiology Students
- Bioinformatics & Computational Biology Researchers
- Synthetic Biology & Metabolic Engineering Researchers
- Environmental Biotechnology & Bioprocess Professionals
- AI & Machine Learning Researchers
- Bioenergy & Industrial Biotechnology Professionals
- Academicians & Research Scholars
Important Dates
Registration Ends
November 3, 2026
IST
Workshop Dates
November 3, 2026 – November 5, 2026
IST 5:30 PM
Workshop Outcomes
By the end of this workshop, participants will be able to:
- Analyze microbial community composition and functional pathways.
- Visualize microbial interaction networks and propose synthetic microbial consortia.
- Develop AI-based models to predict bioprocess performance.
- Interpret machine learning predictions using SHAP.
- Identify critical factors influencing methane yield and bioreactor stability.
- Evaluate virtual bioreactor scenarios and microbial perturbations.
- Apply data-driven approaches to sustainable bioprocess optimization.
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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