Bioinformatics-Driven Insights into Antimicrobial Resistance & HGT with Digital Twin–Based Bioreactor Design

• 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.

• 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

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.