
AI-Assisted CRISPR and CAR-T Cell Engineering for Precision Medicine
Engineering the Future of Precision Medicine with AI, CRISPR, and CAR-T Innovation.
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?
📅 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
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.
