About Workshop
Anaerobic microbes play a critical role in energy production, waste treatment, and environmental sustainability through processes like methanogenesis, fermentation, and biodegradation. However, analyzing their complex interactions and metabolic potential requires advanced computational methods.
This workshop bridges microbiology and AI by introducing participants to machine learning and deep learning approaches for studying anaerobic microbial consortia. Through hands-on sessions, attendees will work on datasets related to microbial community structure, metagenomic profiling, and metabolite prediction using Python-based tools and AI algorithms.
Aim
This workshop aims to explore how Artificial Intelligence (AI) and data-driven approaches can be applied to analyze anaerobic microbial systems for environmental, industrial, and biomedical applications. Participants will gain insights into microbial community modeling, metagenomic data analysis, and AI-driven prediction of metabolic pathways for sustainable bioprocessing and waste management.
What Participants Will Learn
- Understand the role and diversity of anaerobic microbes in environmental and industrial processes.
- Learn data-driven techniques for analyzing microbial community data and metagenomics.
- Apply AI/ML algorithms for predicting microbial functions and metabolic pathways.
- Explore real-world applications of AI in biogas production, waste treatment, and bioremediation.
- Gain hands-on experience with tools like Python, Scikit-learn, QIIME2, and TensorFlow for microbial data analysis.
Structure
Day 1: Introduction to Anaerobic Microbes & AI in Microbial Data Analysis
- Characteristics of anaerobic microbes and their role in biogeochemical cycles.
- Key processes: methanogenesis, fermentation, denitrification, and their relevance in biotechnology.
- Applications: Anaerobic microbes in biogas production, bioremediation, and health applications.
- Sequencing techniques used to gather microbial data (e.g., 16S rRNA, shotgun sequencing).
- Data sources and datasets: Accessing public repositories for microbiome data.
- Hands-On Activity: Analyzing a sample metagenomic dataset using Python-based machine learning tools (e.g., scikit-learn, TensorFlow).
Day 2: Advanced AI Applications in Anaerobic Microbial Data and Predictive Modeling
- Data preprocessing techniques: Quality control, filtering, and normalization of sequencing data.
- Feature extraction from microbial genomic and metabolic data.
- Understanding microbial diversity metrics (e.g., alpha diversity, beta diversity).
- AI models for microbial species identification and functional annotation.
- Predicting microbial metabolic capabilities using PICRUSt and Tax4Fun integrated with machine learning.
- Hands-On Activity: Classifying microbial species from a provided metagenomic dataset using machine learning models.
- Modeling microbial performance in biogas production and bioremediation.
- AI for metabolic pathway prediction: Using deep learning to predict the metabolic outcomes of microbial communities in various environmental conditions.
- Case Study: AI-driven analysis of methane production in anaerobic digesters.
- Hands-On Session: Building Predictive Models for Anaerobic Microbial Applications
- Using Python and AI libraries (e.g., Keras, XGBoost) to create predictive models for anaerobic microbial performance.
- Model evaluation: Using metrics like accuracy, precision, recall, and F1-score
Important Dates
Workshop Dates
Coming Soon
What You Will Gain
Outcomes
- Ability to apply AI for analyzing and interpreting microbial community data.
- Skills to use machine learning for predicting microbial functions and environmental roles.
- Understanding of microbial interactions within anaerobic systems.
- Practical experience with data-driven tools for microbial ecology and bioprocessing.
- Awareness of industrial and environmental applications of anaerobic microbial AI.
Who Should Attend
- Undergraduate/Postgraduate degree in Microbiology, Biotechnology, Bioinformatics, Environmental Science, Computational Biology, or related fields.
- Professionals in bioenergy, wastewater treatment, or environmental biotechnology sectors.
- Data scientists and AI/ML engineers interested in applying AI to microbial and environmental data.
- Individuals passionate about microbial ecology, sustainability, and bioprocess optimization.
Prof. Kumud Malhotra
Professor & Dean
Speciality: Metagenomics, ML, Data Visualization, Python, QIIME2, TensorFlow, Bioinformatics