BIMM

BIMM integrates single-cell RNA sequencing (scRNA-seq) and proteomics to infer signaling pathway perturbations and build multiscale models for analyzing Dexamethasone (DEX) treatment responses in lung adenocarcinoma-derived A549 cells.

Key Features:

• Integration of Multi-Omics Data: Combines scRNA-seq and proteomic datasets to identify differentially expressed genes (DEGs) in A549 cells following Dexamethasone (DEX) treatment.
• Identification of Hub Genes: Constructs regulatory networks from DEGs to identify hub genes such as TGFβ, MYC, and SMAD3 that change with DEX treatment.
• Pathway Analysis and Enrichment: Performs gene set enrichment analysis to determine enriched signaling pathways, with TGFβ signaling identified as a top enriched term.
• Network Modeling and Simulation: Builds multiscale models that incorporate TGFβ-induced and ERBB-amplified signaling pathways to simulate dynamic effects of DEX therapy on tumor regulation.
• Predictive Capabilities: Generates dose- and time-dependent predictive models for DEX treatment and validates predictions against protein-level data for SMAD2, FOXO3, TGFβ1, and TGFβR1 over a time course.
• Cross-Disciplinary Application: Applies the multi-omics integration and multiscale modeling framework to study tumorigenesis and oncotherapy across different cancer contexts.

Scientific Applications:

• Drug Response Analysis: Dissects molecular mechanisms of DEX response in lung cancer by linking DEGs, hub genes, and pathway perturbations.
• Therapeutic Target Identification: Prioritizes hub genes and enriched pathways such as TGFβ signaling as candidate targets for therapeutic intervention.
• Modeling Tumor Dynamics: Uses multiscale simulations to explore tumor behavior under varying DEX doses and time courses.

Methodology:

Identification of DEGs in treated cells; construction of regulatory networks to pinpoint hub genes; gene set enrichment/pathway analysis; integration of results into a multiscale model to simulate tumor dynamics with validation against biological data and literature curation.