About Workshop
This 3-day hands-on workshop explores how artificial intelligence, CRISPR-Cas9 genome editing, and CAR-T cell engineering can be integrated to support precision therapeutics. Participants will learn the fundamentals of guide RNA design, gene-editing efficiency, off-target risk assessment, CAR-T target selection, immune-cell engineering, and therapy-response prediction through practical Google Colab exercises using safe, sample-based datasets.
Aim
The workshop aims to provide participants with a practical understanding of AI-assisted genome editing and engineered immune-cell therapies. It focuses on how computational tools, biological data, and machine learning can support safer, more specific, and personalized CRISPR and CAR-T therapeutic strategies.
What Participants Will Learn
The workshop is designed to:
- Explain the principles and therapeutic applications of CRISPR-Cas9.
- Introduce AI-assisted guide RNA scoring and off-target assessment.
- Explain CAR-T cell structure, generations, target selection, and engineering strategies.
- Demonstrate how CRISPR can support next-generation CAR-T development.
- Introduce machine learning workflows for antigen prioritization and therapy-response prediction.
- Discuss safety, ethical, regulatory, and translational challenges in precision therapeutics.
Structure
📅 Day 1: AI-Guided CRISPR-Cas9 Design for Precision Genome Editing
- Focus: Understanding CRISPR-Cas9 genome editing, guide RNA design, AI-assisted scoring, and safety considerations for precision therapeutics.
- Introduction to precision therapeutics, genome editing, and the fundamentals of the CRISPR-Cas9 system.
- Understanding the role of guide RNA, Cas9 nuclease, PAM sequence, and target recognition in genome editing.
- Applications of CRISPR in cancer, rare diseases, immune disorders, and cell therapy.
- Use of AI and machine learning for CRISPR guide RNA design, on-target efficiency prediction, and off-target risk assessment.
- Importance of safety, specificity, validation, ethical considerations, and translational readiness in therapeutic genome editing.
- Overview of base editing, prime editing, and next-generation genome editing platforms.
🛠️ Hands-on:
- Perform an educational CRISPR guide RNA scoring simulation using Google Colab and a sample/synthetic DNA sequence.
- Load a sample DNA sequence, identify possible guide RNA regions, check basic PAM sequence logic, and calculate simple GC content.
- Rank guide RNA candidates using basic scoring criteria and visualize guide RNA scores.
- Discuss off-target risk conceptually for safe computational learning.
- This hands-on is for educational and computational learning only, not for wet-lab or clinical guide RNA design.
📅 Day 2: CAR-T Cell Engineering and Immune-Cell Therapeutic Design
- Focus: Exploring CAR-T cell engineering, immune-cell therapeutic design, tumor target selection, and CRISPR-supported immune-cell modification.
- Introduction to adoptive cell therapy, immune-cell engineering, T-cell biology, and tumor immune recognition.
- Understanding the structure of CAR-T cells, including antigen-binding domain, hinge region, transmembrane domain, and signaling domains.
- Overview of CAR-T therapy generations and major tumor-associated targets such as CD19, BCMA, HER2, EGFR, and MSLN.
- Applications of CAR-T therapy in hematological malignancies and emerging solid tumor research.
- Challenges in CAR-T therapy including antigen escape, tumor microenvironment, exhaustion, cytokine release syndrome, and neurotoxicity.
- Role of CRISPR in CAR-T engineering, including gene knockout strategies for PD-1, TCR, HLA, and immune checkpoint-related targets.
- Introduction to universal and off-the-shelf CAR-T concepts for next-generation engineered cell therapies.
🛠️ Hands-on:
- Perform CAR-T target prioritization using a sample dataset of tumor antigen expression and safety indicators.
- Load a sample antigen-expression dataset and compare tumor versus normal tissue expression.
- Identify potential CAR-T target candidates and create a simple antigen-prioritization score.
- Visualize target expression using bar plots and heatmaps.
- Interpret safety considerations such as on-target/off-tumor risk.
📅 Day 3: AI-Integrated CRISPR and CAR-T Workflows for Precision Therapeutics
- Focus: Integrating CRISPR, CAR-T, AI, and multi-omics workflows for next-generation precision therapeutics and engineered immune-cell therapy.
- Integration of CRISPR, CAR-T, and AI for advanced therapeutic discovery and immune-cell engineering.
- AI-based target discovery, antigen ranking, therapy response prediction, and toxicity risk assessment for immune-cell therapies.
- CRISPR screening approaches for identifying resistance genes, sensitivity genes, and therapeutic vulnerabilities.
- Engineering CAR-T cells for improved persistence, specificity, tumor penetration, and functional durability.
- Use of multi-omics data, single-cell data, gene expression, and tumor microenvironment profiling in precision immunotherapy.
- Regulatory, safety, and translational challenges in engineered cell therapy.
- Future trends including logic-gated CAR-T, armored CAR-T, universal CAR-T, synthetic biology circuits, and personalized immune-cell therapeutics.
🛠️ Hands-on:
- Build a simple AI-based therapy response prediction model using sample immune-cell therapy response data.
- Load a sample therapeutic response dataset and identify important features such as antigen expression, immune markers, and checkpoint genes.
- Prepare data for machine learning and train a basic classification model.
- Predict responder versus non-responder groups and evaluate model performance using accuracy and confusion matrix.
- Interpret results for precision therapeutic decision-making.
Important Dates
Registration Ends
5:00 PM
Workshop Dates
2026-07-30
6:00 PM
6:00 PM
What You Will Gain
Outcomes
After completing the workshop, participants will be able to:
- Understand the fundamentals of CRISPR-Cas9 and CAR-T cell therapy.
- Explain how AI can improve guide RNA design and editing-efficiency prediction.
- Evaluate basic on-target and off-target considerations.
- Identify and prioritize potential CAR-T therapeutic targets.
- Analyze sample biological datasets using Python and Google Colab.
- Build a basic machine learning model for therapy-response prediction.
- Interpret model performance and explainable AI results.
- Understand safety concerns related to genome editing and engineered cell therapies.
- Explore emerging approaches such as universal CAR-T, armored CAR-T, logic-gated therapies, base editing, and prime editing.
Who Should Attend
- Researchers and academicians
- PhD scholars and postgraduate students
- Biotechnology professionals
- Immunology and cancer biology researchers
- Biomedical scientists
- Bioinformatics learners
- Pharmaceutical professionals
- Industry professionals working in life sciences and healthcare
- Individuals interested in genome editing, cancer immunotherapy, immune-cell engineering, synthetic biology, and AI-assisted biomedical research