About Workshop
Bioprocess engineering and fermentation are central to industries such as pharmaceuticals, biofuels, food technology, and enzyme production. However, maintaining optimal conditions in bioreactors is complex due to dynamic variables like nutrient availability, oxygen transfer, pH, and microbial growth. Traditional monitoring approaches often lack predictive capabilities, leading to inefficiencies and variability in production.
Digital twins—virtual replicas of physical bioprocess systems—enable real-time simulation and predictive control of fermentation processes. By integrating sensor data, kinetic models, and AI algorithms, digital twins can forecast process behavior, detect anomalies, and optimize parameters for improved yield and consistency. This workshop explores dry-lab workflows for building simplified digital twin models, enabling participants to understand how smart biomanufacturing systems operate in Industry 4.0 environments.
Aim
This workshop aims to introduce participants to the concept of digital twins in bioprocess engineering and fermentation systems. It focuses on using real-time data, mathematical modeling, and AI to simulate, monitor, and optimize bioprocess performance. Participants will learn how digital twins enhance process control, productivity, and scalability in industrial biotechnology. The program bridges bioprocess engineering, data science, and smart manufacturing.
What Participants Will Learn
- Understand the concept and architecture of digital twins in bioprocessing.
- Learn modeling of microbial growth and fermentation kinetics.
- Explore integration of sensor data and real-time monitoring systems.
- Apply AI for predictive control and anomaly detection.
- Study scale-up and optimization strategies using digital twin systems.
Structure
Day 1: Foundations of AMR, HGT & Bioinformatics Tools
- Global burden and clinical significance of Antimicrobial Resistance
- Mechanisms of resistance (enzymatic degradation, efflux pumps, target modification)
- Role of pathogens (e.g., Escherichia coli, Staphylococcus aureus)
- Horizontal Gene Transfer (HGT): Transformation, Transduction, Conjugation
- Genetic elements: plasmids, transposons, integrons
- HGT in AMR dissemination
- NCBI GenBank, CARD, BLAST, genome annotation pipelines
- Hands-on Session: Retrieval of genomic data, Identification of resistance genes using bioinformatics tools
- Genomic & Metagenomic Analysis
- Whole genome sequencing (WGS) workflows
- Metagenomics for AMR surveillance, Resistome and mobilome analysis
- Computational Modeling of HGT, Network biology approaches
- Phylogenetics and gene flow tracking
- Predictive modeling of resistance spread
- Data integration: biological + engineering systems
- Linking Bioinformatics with Digital Twins
- Data pipelines from omics to simulation
- Key parameters: pH, temperature, oxygen transfer
- Microbial growth kinetics
- Digital Twin–Based Bioreactor Modeling
- Simulation platforms and tools
- Integration of AMR data into bioreactor systems
- Optimization & Control Strategies
- AI/ML approaches in bioprocess optimization
- Adaptive control systems, Scaling from lab to industry
- AMR mitigation using optimized bioreactors
- Industrial and clinical applications & Emerging trends: synthetic biology, smart biomanufacturing
Important Dates
Registration Ends
7:00 PM
Workshop Dates
2026-05-05
8:00 PM
8:00 PM
What You Will Gain
Outcomes
Participants will be able to:
- Understand digital twin frameworks for fermentation systems.
- Model bioprocess parameters and predict system behavior.
- Apply AI for process monitoring and optimization.
- Identify inefficiencies and improve yield using simulation tools.
- Design smarter and scalable bioprocess workflows.