| Attribute | Detail |
|---|---|
| Format | Online, Project-Based Internship |
| Level | Beginner |
| Certification | Internship e-Certificate upon Successful Completion |
| Fee | ₹5499 / $99 |
| Tools | Cancer and Drug Resistance Neurodegenerative Disorders Cardiovascular and Metabolic Diseases Infectious Diseases and Host–Pathogen Interactions Autoimmune and Inflammatory Disorders Oxidative Stress and Cellular Ageing Select a disease and retrieve suitable public transcriptomic datasets. Clean, normalise, and perform quality control of gene-expression data. Identify differentially expressed genes and generate visualisations. Perform Gene Ontology, KEGG, and Reactome enrichment analysis. Construct and analyse protein-interaction networks using STRING and Cytoscape. Integrate genes, pathways, modules, and regulators into a disease model. Google Colab, Python and R GEO and TCGA-Compatible Data DESeq2, edgeR and limma STRING and Cytoscape Enrichr, g:Profiler and clusterProfiler KEGG, Reactome and GitHub Differentially expressed gene list and visualisations Functional enrichment and pathway-analysis report STRING and Cytoscape protein-interaction network Hub-gene, functional-module, and regulator analysis Integrated systems-level disease model Analysis notebook, GitHub repository, report, and presentation Systems Biology Analysis of Breast Cancer Progression Multi-Omics Investigation of Alzheimer’s Disease Pathways Systems-Level Analysis of Drug Resistance in Lung Cancer Host–Pathogen Systems Biology of Viral Infection Systems Biology of Oxidative Stress and Cellular Ageing Integrated Gene and Pathway Analysis of Type 2 Diabetes |
About the Systems Biology of Disease: Multi-Omics Integration and Network Analysis Course
The Systems Biology of Disease internship is a six-week, project-based programme designed to help participants investigate diseases through the integrated analysis of genes, proteins, biological pathways, regulatory mechanisms, and multi-omics data.
Participants will work with publicly available transcriptomic datasets to identify differentially expressed genes, enriched biological processes, disease-associated pathways, protein-interaction networks, hub genes, and transcriptional regulators.
By combining gene-expression results with network and pathway information, participants will develop an integrated systems-level model explaining the molecular mechanisms involved in disease development, progression, treatment response, or drug resistance.
Aim
To develop a systems-level understanding of disease by integrating gene-expression data, protein interactions, biological pathways, transcriptional regulation, and disease-associated molecular processes.
Program Highlights
• Six-week disease-focused research internship
• Gene-expression and transcriptomic data analysis
• GO, KEGG, and Reactome pathway enrichment
• STRING and Cytoscape network analysis
• Hub-gene and transcriptional-regulator identification
• Final report, presentation, and technical viva
Course Curriculum
Week 1: Disease Selection and Dataset Collection
- Define the disease-related biological problem.
- Select a disease or biological condition.
- Review relevant scientific literature.
- Identify suitable public gene-expression datasets.
- Prepare sample and clinical metadata.
- Create and organise the GitHub repository.
Week 2: Gene-Expression Analysis
- Import and clean gene-expression data.
- Perform normalisation and quality-control analysis.
- Conduct principal component analysis.
- Perform differential gene-expression analysis.
- Identify significantly altered genes.
- Generate volcano plots and heatmaps.
Week 3: Functional Enrichment Analysis
- Prepare upregulated and downregulated gene lists.
- Conduct Gene Ontology enrichment analysis.
- Perform KEGG pathway analysis.
- Perform Reactome pathway analysis.
- Interpret disease-associated biological processes.
- Prioritise significant candidate pathways.
Week 4: Protein-Interaction Network Analysis
- Upload significant genes to the STRING database.
- Configure the organism and interaction confidence score.
- Retrieve the protein–protein interaction network.
- Import and visualise the network in Cytoscape.
- Analyse network topology and connectivity.
- Identify important interacting proteins and modules.
Week 5: Systems-Level Integration
- Integrate gene-expression and interaction-network results.
- Identify hub genes and important network nodes.
- Detect disease-associated functional modules.
- Analyse transcription factors and target genes.
- Connect functional modules with enriched pathways.
- Develop a preliminary disease-mechanism model.
Week 6: Final Interpretation and Presentation
- Validate important genes and pathways through literature.
- Prepare the final systems biology diagram.
- Complete the analysis notebook and GitHub repository.
- Prepare the research-style technical report.
- Develop and deliver the final presentation.
- Attend the technical viva.
Tools, Techniques, or Platforms Covered
Cancer and Drug Resistance
Neurodegenerative Disorders
Cardiovascular and Metabolic Diseases
Infectious Diseases and Host–Pathogen Interactions
Autoimmune and Inflammatory Disorders
Oxidative Stress and Cellular Ageing
- Select a disease and retrieve suitable public transcriptomic datasets.
- Clean, normalise, and perform quality control of gene-expression data.
- Identify differentially expressed genes and generate visualisations.
- Perform Gene Ontology, KEGG, and Reactome enrichment analysis.
- Construct and analyse protein-interaction networks using STRING and Cytoscape.
- Integrate genes, pathways, modules, and regulators into a disease model.
Google Colab, Python and R GEO and TCGA-Compatible Data DESeq2, edgeR and limma STRING and Cytoscape Enrichr, g:Profiler and clusterProfiler KEGG, Reactome and GitHub
- Differentially expressed gene list and visualisations
- Functional enrichment and pathway-analysis report
- STRING and Cytoscape protein-interaction network
- Hub-gene, functional-module, and regulator analysis
- Integrated systems-level disease model
- Analysis notebook, GitHub repository, report, and presentation
Systems Biology Analysis of Breast Cancer Progression
Multi-Omics Investigation of Alzheimer’s Disease Pathways
Systems-Level Analysis of Drug Resistance in Lung Cancer
Host–Pathogen Systems Biology of Viral Infection
Systems Biology of Oxidative Stress and Cellular Ageing
Integrated Gene and Pathway Analysis of Type 2 Diabetes
Real-World Applications
- Identification of disease biomarkers and therapeutic targets
- Investigation of disease progression and drug resistance
- Understanding inflammatory and oxidative-stress mechanisms
- Analysis of host–pathogen molecular interactions
- Prioritisation of pathways for experimental validation
- Supporting precision medicine and drug-discovery research
Who Should Attend & Prerequisites
- Undergraduate and postgraduate life-science students
- Biotechnology, bioinformatics, genetics, and biomedical learners
- PhD scholars and early-career researchers
- Researchers working in disease and computational biology
- Pharmaceutical and healthcare professionals
- Learners interested in systems biology and multi-omics research
Certification
