Computational Modeling & Data Analytics

 

 

 

 

 

 

 

 

In silico-based modeling and analysis allows for robust and reproducible quantitative assessment of tumor progression and treatment response. These methodologies integrate (and often simplify) the complex molecular and cellular interactions between cancer cells, host cells, and mechanical properties and forces in the heterogeneous tumor microenvironment. The insights from these high-throughput, low-cost approaches can be applied beyond cancer to other diseases that feature abnormal biological, chemical, electrical, and physical microenvironments (e.g., autoimmune disorders, infectious diseases, fibrotic conditions, and brain injuries). Applications include:

• Mathematical modeling of tumor growth, biotransport, and mechanics
• Biomarker-based predictive models of individual treatment response/resistance
• AI-assisted quantitative image analysis and digital pathology
• Computer-aided molecular design for drug discovery and design
• Bioinformatics and machine learning for “multi-omics” big-data integration