Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Organoids—3D tissue models derived from patient or stem cell sources—are revolutionizing drug discovery by providing physiologically relevant systems that closely mimic real human organs and tumors. Unlike traditional 2D cultures, organoids preserve cellular heterogeneity, microenvironment interactions, and clinically meaningful drug response patterns. This makes them powerful platforms for screening anticancer therapies, testing drug combinations, and developing patient-specific treatment strategies. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Organoids—3D tissue models derived from patient or stem cell sources—are revolutionizing drug discovery by providing physiologically relevant systems that closely mimic real human organs and tumors. Unlike traditional 2D cultures, organoids preserve cellular heterogeneity, microenvironment interactions, and clinically meaningful drug response patterns. This makes them powerful platforms for screening anticancer therapies, testing drug combinations, and developing patient-specific treatment strategies.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A portfolio-grade biotechnology deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Organoids as a Screening Platform
• Patient-derived and iPSC-derived organoids and what each preserves
• Matrigel batch variability as a dominant and under-reported source of noise
• Where organoids beat 2D culture, and what they still fail to model
Designing an Organoid Screen
• Plate format, seeding density and edge effects in 3D culture
• Viability readouts in 3D: CellTiter-Glo 3D versus imaging-based measures
• Z-prime and replicate structure — a screen that fails QC is not salvageable later
Extracting Data from 3D Images
• Confocal and light-sheet acquisition trade-offs for organoid volumes
• Segmentation with Cellpose or StarDist and the retraining usually required
• Morphological feature extraction and the batch effects hidden in imaging runs
Modelling Drug Response
• Dose-response fitting, IC50 and the distinction from AUC-based metrics
• Handling heterogeneity between organoid lines rather than averaging it away
• Machine learning on morphological profiles and the small-n overfitting trap
From Screen to Clinical Claim
• Retrospective organoid-to-patient concordance studies and their reported accuracy
• Turnaround time as the binding constraint on clinical use
• Reporting standards and reproducibility across organoid laboratories
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | AutoDock Vina |
| Covered Tool / Platform | PyRx |
| Covered Tool / Platform | Schrödinger Suite |
| Covered Tool / Platform | GROMACS |
| Covered Tool / Platform | ChemDraw |
| Covered Tool / Platform | Discovery Studio |
| Covered Tool / Platform | ADMET Predictor |
Frequently Asked Questions
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