Program Structure

Module 1: Systems Vaccinology — The Modern Vaccine Science Lens

• Why people respond differently to the same vaccine: biology, age, genetics, microbiome, prior exposure.
• From single markers to systems signatures: why omics changed vaccine research.
• Key outputs: correlates of protection vs signatures of response.
• What systems vaccinology can and cannot claim (limits and risks).

Module 2: Study Design for Vaccine Omics

• Common timepoints: baseline, early innate response, peak adaptive response.
• Sampling strategy: blood, PBMCs, serum, tissue (conceptual).
• Controls and confounders: batch effects, seasonality, prior immunity.
• Metadata that matters: age, sex, comorbidities, vaccine lot, dosing schedule.

Module 3: Transcriptomics in Vaccine Response (RNA-Seq / Microarray)

• What transcriptomics measures: gene expression as immune activation signals.
• QC and normalization concepts: why preprocessing matters.
• Differential expression and immune gene signatures (interferon, inflammation, B-cell/T-cell programs).
• Pathway enrichment: GO/KEGG/Reactome interpretation discipline.

Module 4: Proteomics, Cytokines & Immune Profiling

• Proteomics overview: proteins as closer-to-function signals than mRNA.
• Multiplex cytokines and chemokines: inflammation patterns and timing.
• Flow cytometry concepts: immune cell frequency and activation markers.
• How to align immune profiling with transcriptomics (biological consistency checks).

Module 5: Metabolomics, Microbiome & Host Factors (Overview)

• Metabolomics basics: metabolic state as an immune modulator.
• Microbiome influence: immune training and vaccine response variability (overview).
• Host genetics and HLA concepts: antigen presentation differences (overview).
• Integrating host factors into prediction models without bias.

Module 6: Computational Pipelines & Data Integration

• Workflow mindset: raw data → QC → normalization → features → models.
• Integration approaches overview: correlation networks, module scoring, multi-omics feature selection.
• Dimensionality reduction concepts: PCA/UMAP (what they show and what they don’t).
• Reproducibility: versioned scripts, documented assumptions, and clean reporting.

Module 7: Machine Learning for Vaccine Response Prediction

• Supervised vs unsupervised learning in immunology contexts (overview).
• Building response prediction models: classification/regression examples.
• Avoiding overfitting: cross-validation, leakage, and small-cohort risks.
• Model interpretation: feature importance and biological plausibility checks.

Module 8: Network Biology & Pathway-Level Insights

• Gene modules and immune programs: moving beyond individual genes.
• Co-expression networks concept and why modules generalize better.
• Linking modules to outcomes: antibody titers, T-cell responses, durability.
• From network insight to hypotheses: adjuvant choice and dosing strategies (concept).

Module 9: Translational Applications (From Signatures to Better Vaccines)

• Early signatures as predictors: identifying responders vs non-responders.
• Adjuvant and platform comparisons using systems readouts.
• Population differences: age, immunocompromised groups, and region-specific immunity.
• Implementation perspective: how systems insights support policy and rollout decisions.

Module 10: Ethics, Privacy & Responsible Omics in Vaccine Studies

• Handling sensitive omics data: privacy, consent, and governance basics.
• Fairness and bias: ensuring models do not disadvantage groups.
• Transparent reporting: limitations, uncertainty, and reproducibility.
• Responsible communication: avoiding hype from computational signatures.

Final Project

• Complete a Systems Vaccinology Mini-Analysis (guided dataset or case).
• Deliverables: QC summary, a response signature (gene/module list), pathway interpretation, and a simple predictive model (concept or lightweight implementation).
• Output: short report explaining what the signature suggests biologically and how it could guide vaccine improvement.
• Example projects: early interferon signature predicting antibody response, comparing adjuvant arms, identifying pathways linked to durability, stratifying responders vs non-responders.