
AI-Assisted CRISPR and CAR-T Cell Engineering for Precision Medicine
Hands-On Computational Approaches for Genome Editing, Immune-Cell Engineering and AI-Driven Therapeutic Design
Skills you will gain:
About Program:
This hands-on workshop explores AI-assisted CRISPR genome editing and CAR-T cell engineering for precision medicine.
Participants will learn computational approaches for guide-RNA design, therapeutic target prioritisation, immune-cell engineering, and AI/ML-based analysis.
The workshop integrates CRISPR, CAR-T, multi-omics, and AI workflows to support next-generation therapeutic research.
Aim: To equip participants with practical skills in therapeutic CRISPR strategy selection, disease-variant analysis, base- and prime-editing design, AI-assisted guide prioritisation, off-target assessment, and sequencing-based evaluation of genome-editing outcomes.
Program Objectives:
- Understand therapeutic CRISPR-Cas9, base editing, prime editing, and gene-regulation approaches.
- Retrieve and interpret disease-associated variants using ClinVar and Ensembl.
- Select suitable genome-editing strategies for specific variants.
- Design base-editing guide RNAs and prime-editing pegRNAs.
- Assess PAM compatibility, editing windows, and bystander mutations.
- Rank guide candidates using efficiency, specificity, and AI-assisted scoring.
- Identify and prioritise potential genome-wide off-target sites.
- Analyse CRISPR amplicon-sequencing data using CRISPResso2.
- Quantify intended edits, indels, frameshifts, and editing purity.
- Generate research-oriented figures, summaries, and computational reports.
What you will learn?
Workshop Structure
📅 Day 1: AI-Guided CRISPR Design for Precision Genome Editing
- CRISPR-Cas9 mechanism, DNA repair, and genome-editing strategies
- Target-gene, transcript, exon, and variant selection
- Guide RNA, PAM, and editing-window principles
- AI/ML-assisted guide-RNA scoring and prioritisation
- Base editing and prime editing overview
- Off-target prediction, specificity, safety, and ethics
🛠️ Hands-on: Target selection, guide-RNA design, computational scoring, and off-target assessment using sample datasets.
🧰 Tools: NCBI Gene, Ensembl, UCSC Genome Browser, UniProt, PubMed, Google Colab, Python, BioPython
📅 Day 2: CRISPR-Supported CAR-T Cell Engineering
- CAR-T architecture, generations, and therapeutic applications
- Tumour-antigen selection and target prioritisation
- Major targets: CD19, BCMA, HER2, EGFR, and MSLN
- CRISPR-based immune-cell engineering and gene knockout strategies
- Universal and next-generation CAR-T concepts
- Antigen escape, T-cell exhaustion, tumour microenvironment, and safety
🛠️ Hands-on: Analyse tumour-antigen expression data, prioritise CAR-T targets, and assess on-target/off-tumour risks.
🧰 Tools: Google Colab, Python, Pandas, NumPy, Matplotlib, Scikit-learn
📅 Day 3: AI-Integrated CRISPR–CAR-T Precision Therapeutics
- AI-assisted therapeutic target discovery and prioritisation
- CRISPR screening for resistance genes and therapeutic vulnerabilities
- Multi-omics and single-cell approaches in precision immunotherapy
- AI-based therapeutic response and toxicity prediction
- Next-generation CAR-T: logic-gated, armoured, and universal platforms
- Translational, regulatory, ethical, and biosafety considerations
🛠️ Hands-on: Build an AI/ML workflow for therapeutic response prediction and interpret key predictive features.
🧰 Tools: Google Colab, Python, Pandas, NumPy, Scikit-learn, Matplotlib, SHAP
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
- UG and PG students in biotechnology, bioinformatics, genetics, genomics, molecular biology, and life sciences
- Ph.D. scholars, research fellows, faculty members, and academicians
- Bioinformaticians, computational biologists, and NGS professionals
- Biotechnology, pharmaceutical, and clinical research professionals
- Researchers interested in CRISPR, gene therapy, precision medicine, and therapeutic genome editing
Basic knowledge of molecular biology is recommended. Prior experience in CRISPR, programming, or NGS analysis is not mandatory.
Career Supporting Skills
Program Outcomes
- Interpret disease-associated variants using ClinVar and Ensembl.
- Select appropriate CRISPR, base-editing, or prime-editing strategies.
- Design and rank guide RNAs and pegRNAs.
- Assess PAM compatibility, bystander edits, and off-target risks.
- Analyse amplicon NGS data using CRISPResso2.
- Quantify editing efficiency, indels, substitutions, and frameshifts.
- Prepare research-oriented figures, summaries, and analysis reports.
