AI-Powered Systems Biology & Nanomedicine: Python-Based Biomarker Discovery for Lifestyle Diseases

Lifestyle diseases such as type 2 diabetes, obesity, and cardiovascular disorders involve complex interactions among genes, proteins, and molecular signaling pathways. This three-day hands-on workshop integrates systems biology, bioinformatics tools, Python programming, artificial intelligence, and nanomedicine to investigate disease-associated molecular mechanisms and potential therapeutic strategies. Participants will explore biological databases, perform functional enrichment analysis, construct protein interaction networks, identify candidate biomarkers using machine learning, and investigate nanoparticle protein interactions through computational analysis. The workshop combines established bioinformatics platforms with Python-based analysis to provide practical research-oriented training.

This workshop aims to develop practical skills in disease genomics, molecular pathway analysis, protein interaction networks, and candidate biomarker discovery using bioinformatics, Python, and machine learning. Through expert-led sessions and hands-on activities, participants will explore therapeutic target identification, nanocarrier properties, and predictive nano–bio interactions, supporting computational research in drug discovery and precision nanomedicine.

• Investigate molecular mechanisms and signaling pathways underlying lifestyle diseases.
• Identify disease-associated genes and potential therapeutic targets using bioinformatics tools.
• Perform functional enrichment and pathway analysis using KEGG, Reactome, and ShinyGO.
• Apply Python for biological data analysis and visualization.
• Construct protein–protein interaction networks and identify key regulatory hub genes.
• Employ machine learning for candidate biomarker identification and prioritization.
• Analyze disease-associated protein structures and their therapeutic relevance.
• Explore nanocarrier properties, protein corona formation, and nano-bio interactions.
• Apply AI-based predictive models to assess nanoparticle-associated biological responses.
• Integrate systems biology and computational nanomedicine to investigate potential therapeutic strategies.

🗓️ Day 1 — Python-Based Disease Genomics, Molecular Pathways & Systems Biology

Molecular mechanisms of type 2 diabetes, obesity, and cardiovascular disorders

Insulin resistance, metabolic dysfunction, oxidative stress, and inflammatory signaling

Disease-associated gene identification using GeneCards, DisGeNET, and NCBI Gene

Protein function and disease association analysis using UniProt

PI3K–AKT, AMPK, insulin signaling, and NF-κB pathway analysis using KEGG and Reactome

Gene Ontology and pathway enrichment analysis using ShinyGO

Python-based processing and visualization of disease-associated molecular data

🛠️ Hands-On: Retrieve type 2 diabetes-associated genes, perform pathway enrichment analysis using ShinyGO, and generate pathway visualization plots in Python.

Bioinformatics Tools: GeneCards | DisGeNET | NCBI Gene | UniProt | KEGG | Reactome | ShinyGO

Python Tools: Google Colab | Pandas | Matplotlib

🗓️ Day 2 — AI-Driven Molecular Network Analysis, Hub Genes & Biomarker Discovery

Protein–protein interaction networks and their biological significance

Construction of disease-associated molecular interaction networks using STRING

Network visualization using Cytoscape and Python NetworkX

Hub gene identification through degree and betweenness centrality analysis

Network cluster and functional module detection using cytoHubba or MCODE

Retrieval and analysis of diabetes-related gene-expression datasets from NCBI GEO

Random Forest-based candidate biomarker prioritization using gene-expression data

Integration of network topology and machine-learning feature rankings for molecular target prioritization

🛠️ Hands-On : Build a diabetes-associated protein interaction network using STRING and Cytoscape, identify hub genes, and apply Random Forest to a prepared gene-expression dataset for candidate biomarker ranking.

Bioinformatics Tools: STRING | Cytoscape | cytoHubba | NCBI GEO | GEO2R

Python Tools: Google Colab | NetworkX | Scikit-learn | Pandas

🗓️ Day 3 — AI-Assisted Nanomedicine, Protein Targets & Predictive Nano–Bio Analysis

Nanomedicine applications in diabetes and metabolic disease research

Lipid nanoparticles, polymeric nanocarriers, and functionalized drug-delivery systems

Retrieval of disease-associated protein structures using RCSB PDB

Three-dimensional visualization of therapeutic protein targets using ChimeraX

Nanoparticle–protein interactions and protein corona formation

Effects of particle size, zeta potential, and surface properties on nano–bio interactions

Machine-learning analysis of nanocarrier properties and experimentally measured biological responses

Predictive modeling of cellular uptake or cytotoxicity and interpretation of model limitations

🛠️ Hands-On: Visualize a selected diabetes-associated protein structure and use Python with a curated nanoparticle dataset to build an exploratory Random Forest model for cellular uptake or cytotoxicity prediction.

Bioinformatics Tools: UniProt | RCSB PDB | ChimeraX

Python Tools: Google Colab | Pandas | NumPy | Scikit-learn | Matplotlib

• Understand the molecular mechanisms of lifestyle diseases.
• Identify disease associated genes using GeneCards and DisGeNET.
• Analyze molecular pathways using KEGG, Reactome, and ShinyGO.
• Process and visualize biological datasets using Python.
• Construct protein interaction networks using STRING and Cytoscape.
• Identify hub genes and potential therapeutic targets.
• Apply machine learning for candidate biomarker discovery.
• Explore protein structures using RCSB PDB and ChimeraX.
• Understand nanocarrier properties and nanoparticle and protein interactions.
• Analyze nano–bio interactions using Python based predictive models.
• Explore AI-assisted nanomedicine strategies for lifestyle diseases.

Graduate and postgraduate students in Biotechnology, Bioinformatics, Nanotechnology, Pharmacy, Biomedical Sciences, and Life Sciences. PhD scholars and researchers working in systems biology, molecular networks, biomarker discovery, and nanomedicine. Academicians and faculty members interested in AI-driven bioinformatics, Python-based analysis, and computational nanomedicine. Industry professionals in pharmaceutical R&D, biotechnology, molecular diagnostics, AI-based drug discovery, and nanomedicine.