Personalized Molecular Digital Twin Platform
GPU-Accelerated Computational Oncology for Precision Cancer Medicine — powered by 11 NVIDIA BioNeMo NIM endpoints
Cognisom builds personalized genomic digital twins using 11 NVIDIA BioNeMo NIM microservices for molecular analysis. Simulates treatment response across 7 cancer regimens with real-time RTX 3D visualization.
Personalized molecular digital twin with real-time 3D visualization
Watch real-time cellular simulation showing multi-scale biological processes from molecular interactions to tissue dynamics
11 NVIDIA BioNeMo NIM endpoints for protein folding, molecular docking, and drug interaction
Personalized genomic digital twin from patient VCF/FASTQ data
Treatment simulation across 7 cancer regimens (chemo, immuno, hormonal, targeted, radiation, combination, experimental)
Real-time RTX 3D molecular visualization with ray tracing
Multi-scale cellular simulation with molecular sequence tracking (ATCG/AUCG)
Detailed immune system modeling (T cells, NK cells, macrophages)
Exosome-mediated molecular transfer and cancer transmission
9 integrated biological modules with event-driven architecture
Cognisom is the ONLY platform with all these features integrated into a single, cohesive system.
• 243,000+ events/second - High-performance event bus
• Modular plugin system - Easy extension and customization
• Real-time control - Parameter adjustment on-the-fly
• ~500KB memory - Efficient for 25 cells + 16 immune cells
To simulate and visualize full organ somatic tissue communication and messaging at the cellular level, creating the world's most comprehensive platform for understanding how organs function as integrated systems.
Our initial focus is prostate cancer—modeling initiation, progression, immune evasion, and metastasis to enable precision medicine through digital organ simulation.
Watch cellular simulation in action
Real-time cellular simulation showing multi-scale biological processes from molecular interactions to tissue dynamics
• Port core modules to CUDA
• GPU spatial indexing
• Memory optimization
Target: 10,000 cells
• Distributed computing
• Domain decomposition
• Load balancing
Target: 100,000 cells
• Million-cell simulations
• Real-time organ modeling
• Clinical deployment
Target: 1,000,000+ cells
• Model tumor growth and microenvironment
• Simulate immune surveillance and evasion
• Track metastatic pathways
• Predict treatment resistance mechanisms
• Simulate checkpoint blockade (anti-PD-1/PD-L1)
• Model CAR-T therapy efficacy
• Optimize combination therapies
• Chronotherapy timing optimization
• Virtual compound screening in silico
• Optimize dosing schedules
• Discover predictive biomarkers
• Enable patient stratification
• Build patient-specific models
• Forecast treatment response
• Predict clinical outcomes
• Guide treatment decisions
Prevalence
Most common cancer in men (1 in 8)
Mortality
35,000+ deaths/year in US
Clinical Need
Metastatic disease is incurable
Data Availability
Well-characterized biology and genomics
• Model primary tumor growth and microenvironment
• Simulate immune surveillance and evasion mechanisms
• Track metastatic pathways (lymphatic → bone)
• Predict treatment response and resistance
• Optimize chronotherapy timing for enhanced efficacy
• Enable patient-specific simulations for precision medicine
Working demos prove the platform's ability to model complex biological phenomena
Result: 3/4 normal cells transformed in 5 hours
Mechanism: Oncogenic mRNA transfer (KRAS G12D)
First simulator to model molecular cancer transmission
Result: 5 cancer cells killed by T/NK cells
Mechanism: MHC-I recognition and cytotoxic killing
Detailed immune cell recognition mechanisms
Components: 100 epithelial + 33 immune cells
Systems: 8 capillaries + 4 lymphatic vessels
Size: 200 × 200 × 100 μm tissue volume
Interface: 9-panel dashboard with 3D tissue view
Features: Live statistics and monitoring
Control: Interactive parameter adjustment
• Interactive Tkinter-based interface
• Real-time simulation control
• Parameter adjustment on-the-fly
• Live statistics and monitoring
• Browser-based interface
• REST API backend (Flask)
• Multi-panel visualization
• Data export (CSV, JSON, HTML, LaTeX)
• Interactive menu system
• Scenario library
• Batch processing
• Scripting support
from core import SimulationEngine
engine = SimulationEngine()
engine.register_module('cellular')
engine.run(duration=24.0)| Feature | PhysiCell | VCell | CompuCell3D | cognisom |
|---|---|---|---|---|
| Molecular sequences | ❌ | ❌ | ❌ | ✅ |
| Exosome transfer | ❌ | ❌ | ❌ | ✅ |
| Detailed immune | ❌ | ❌ | ❌ | ✅ |
| Circadian clocks | ❌ | ❌ | ❌ | ✅ |
| Real-time GUI | ❌ | ❌ | ❌ | ✅ |
| Open source | ✅ | ✅ | ✅ | ✅ |