TISON

TISON performs multi-scale modeling and simulation in systems oncology by integrating biomolecular data across genetic, transcriptional, and translational levels with extracellular microenvironment information to investigate cancer system-level mechanisms.

Key Features:

• Multi-scale modeling and simulation: Represents and simulates interactions across molecular, cellular, and tissue scales in systems oncology.
• Integration of biomolecular data: Integrates biomolecular data across genetic, transcriptional, and translational levels.
• Microenvironment incorporation: Incorporates extracellular microenvironment information alongside molecular data.
• Coupling of scale-specific systems: Couples biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries.
• Simulation engine: Computes the temporal evolution of multi-scale models to capture dynamic disease progression and treatment response.
• Data analysis for model evaluation: Provides analysis features to evaluate and interpret model behavior and outputs.
• Patient-specific data integration: Integrates patient-specific gene expression data to enable personalized model instantiation.
• Validation through case studies: Validated using literature-based case studies demonstrating applications in drug target discovery, repositioning, and personalized therapeutics.

Scientific Applications:

• Systems oncology research: Modeling cancer as a system to study multiscale interactions and mechanisms.
• Emergent property analysis: Investigating emergent behaviors arising from coupled molecular, cellular, and microenvironmental interactions.
• Clinical translation and precision medicine: Developing patient-centric models using gene expression data to inform personalized therapeutic strategies.
• Drug target discovery and repositioning: Supporting identification and evaluation of drug targets and repurposing opportunities via in silico models.
• Treatment response and progression modeling: Simulating temporal disease progression and treatment responses to inform hypothesis generation.

Methodology:

Integrates biomolecular data across genetic, transcriptional, and translational levels and extracellular microenvironment information; couples biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries; uses a simulation engine to compute temporal evolution of multi-scale models; incorporates patient-specific gene expression data; validated with literature-based case studies.