Computational Systems Biology, Network Biology and Hub Gene Discovery

To equip participants with practical skills in systems biology, molecular-network analysis, hub-gene identification, pathway interpretation, and computational biomarker prioritization.

• Understand the fundamental principles of computational systems biology and network biology.
• Retrieve and analyse publicly available gene-expression datasets from GEO.
• Perform gene-expression preprocessing, normalisation, and differential-expression analysis using R and Bioconductor.
• Identify significantly upregulated and downregulated genes.
• Generate and interpret volcano plots, heatmaps, and gene-expression visualisations.
• Construct protein–protein interaction networks using STRING and Cytoscape.
• Analyse network topology using degree, betweenness, closeness, and MCC centrality measures.
• Identify hub genes using cytoHubba and functional molecular modules using MCODE.
• Perform Gene Ontology, KEGG, and Reactome pathway-enrichment analysis.
• Interpret disease-associated pathways and biological functions of candidate genes.
• Integrate gene-expression, network-centrality, and pathway evidence for biomarker prioritisation.

📅 Day 1: Gene-Expression Analysis for Systems Biology

Core Objective: Process gene-expression data and identify significant genes for downstream network and pathway analysis.

• Introduction to systems biology and biological networks
• Understanding gene-expression datasets and public repositories
• Retrieval of gene-expression data from the Gene Expression Omnibus (GEO)
• Gene-expression data preprocessing and normalization
• Differential gene-expression analysis
• Interpretation of fold change, p-values, and adjusted p-values
• Identification of significantly upregulated and downregulated genes
• Visualization of expression patterns using volcano plots and heatmaps

🛠️ Hands-on Lab: Retrieve a public gene-expression dataset, perform differential-expression analysis in R, and generate a volcano plot, heatmap, and filtered list of significant genes.

🧰 Tools Covered: R, RStudio, Bioconductor, GEOquery, limma, DESeq2, ggplot2

📅 Day 2: Network Construction & Hub-Gene Identification

Core Objective: Construct protein–protein interaction networks and identify key genes, hub nodes, and functional molecular modules.

• Fundamentals of protein–protein interaction networks
• Understanding network nodes, edges, connectivity, and topology
• Construction of interaction networks using the STRING database
• Importing and visualizing biological networks in Cytoscape
• Network-topology analysis using NetworkAnalyzer
• Hub-gene identification using centrality algorithms
• Comparison of degree, betweenness, closeness, and MCC centrality measures
• Functional-module and molecular-cluster detection using MCODE
• Biological interpretation of hub genes and network modules

🛠️ Hands-on Lab: Construct a protein–protein interaction network using STRING, import it into Cytoscape, identify hub genes with cytoHubba, and detect significant molecular modules using MCODE.

🧰 Tools Covered: STRING, Cytoscape, cytoHubba, MCODE, NetworkAnalyzer

📅 Day 3: Pathway Analysis & Biomarker Prioritization

Core Objective: Integrate gene-expression, network, and pathway evidence to prioritize potential biomarkers and therapeutic targets.

• Introduction to functional-enrichment analysis
• Gene Ontology enrichment analysis for biological processes, molecular functions, and cellular components
• KEGG and Reactome pathway-enrichment analysis
• Functional interpretation of hub genes and molecular modules
• Identification of disease-associated biological pathways
• Construction of gene–pathway interaction networks
• Comparison and ranking of candidate biomarker genes
• Biomarker and therapeutic-target prioritization
• Integration of differential-expression, centrality, and pathway evidence
• Preparation of publication-quality network figures and reports

🛠️ Hands-on Lab: Perform functional-enrichment analysis of hub genes, construct gene–pathway networks, compare candidate genes, and prepare a publication-ready biomarker-prioritization report.

🧰 Tools Covered: Enrichr, g:Profiler, DAVID, KEGG, Reactome, ClueGO, Cytoscape

• Graduate and postgraduate students in biotechnology, bioinformatics, life sciences, computational biology, genetics, and related disciplines
• Ph.D. scholars and research fellows working in gene expression, systems biology, network biology, biomarker discovery, or disease mechanisms
• Faculty members, academicians, and educators interested in computational and data-driven biological research
• Bioinformaticians and computational biologists seeking practical experience in R, Bioconductor, STRING, and Cytoscape
• Molecular biologists, geneticists, and biomedical researchers working with transcriptomic or gene-expression datasets
• Researchers involved in cancer biology, infectious diseases, neurological disorders, metabolic diseases, and other disease-focused studies
• Biotechnology and pharmaceutical professionals involved in biomarker discovery, drug-target identification, and therapeutic research
• Data analysts and life-science professionals interested in biological network analysis and pathway interpretation
• Clinical research professionals and molecular-diagnostics specialists
• Individuals planning careers in systems biology, network pharmacology, computational genomics, bioinformatics, or precision medicine