Program Structure

Module 1: Disease Biology + Where Biotechnology Fits

• What makes a disease “deadly”: virulence, immune evasion, resistance, late diagnosis.
• Infectious vs non-infectious deadly diseases: differences in strategies.
• From sample to answer: diagnostics pipeline overview.
• Prevention vs cure vs control: biotech’s role in each.

Module 2: Sample Handling, Biosafety & Quality Foundations

• Sample types: blood, saliva, swabs, tissue biopsies (what changes in workflow).
• Biosafety basics: contamination control, PPE, and safe handling discipline.
• Quality mindset: controls, repeatability, and documentation.
• Common failure points: contamination, degradation, mix-ups, and false positives.

Module 3: Molecular Diagnostics (PCR, qPCR, RT-PCR)

• Principle of PCR and why it detects disease agents and mutations.
• RT-PCR for RNA viruses: workflow overview and interpretation.
• qPCR basics: Ct values, standard curves (concept), and reporting discipline.
• Primer/probe design thinking and contamination prevention strategies.

Module 4: Immunotechnology & Serological Testing

• Antigen–antibody basics for diagnostics and therapy.
• ELISA and rapid tests: how they work and what they measure.
• Neutralization concept: why some antibodies protect better than others.
• Limitations: cross-reactivity, timing of immune response, false results.

Module 5: Genomics & Sequencing for Disease Detection and Surveillance

• Sanger vs NGS (overview): what each is used for.
• Whole genome sequencing for pathogen tracking (outbreak surveillance concept).
• Variant detection basics: mutation monitoring and resistance signals.
• RNA-Seq concept for host response and disease signatures.

Module 6: Cell Culture, Assays & Functional Testing (Overview)

• Cell culture basics: why we need in vitro systems.
• Cytotoxicity and efficacy assays: what they indicate.
• Viral assays concept: plaque assay, TCID50 (overview).
• From screening to validation: how lab assays support therapeutic decisions.

Module 7: Therapeutic Biotechnology (Vaccines, Antibodies, RNA Tools)

• Vaccine platforms overview: inactivated, subunit, viral vector, mRNA.
• Monoclonal antibodies: production basics and therapeutic logic.
• RNA therapeutics overview: siRNA, antisense, CRISPR (where each fits).
• Drug resistance and why combination strategies matter.

Module 8: Bioinformatics & AI Support in Disease Control

• How bioinformatics supports diagnostics and drug/vaccine design.
• Basics of sequence alignment, phylogeny, and variant tracking (conceptual).
• AI in outbreak monitoring: early warning signals and pattern detection (overview).
• Responsible interpretation: avoiding overclaiming from computational outputs.

Module 9: Case Labs (Real Disease Scenarios)

• Case A: Viral outbreak workflow (sample → RT-PCR → sequencing → surveillance).
• Case B: Antibiotic resistance detection (PCR/NGS + interpretation).
• Case C: Cancer biomarker workflow (qPCR/ELISA + validation thinking).
• Case D: Rapid diagnostics deployment planning (quality and ethics focus).

Final Project

• Create a Disease Control Biotech Workflow Plan for one deadly disease (infectious or non-infectious).
• Include: diagnostic strategy, key techniques, controls, interpretation plan, biosafety notes, and next-step decisions.
• Example projects: TB resistance detection workflow, dengue early diagnosis strategy, cancer biomarker panel plan, viral outbreak surveillance plan.