GreatSPN

GreatSPN models Relapsing-Remitting Multiple Sclerosis (RRMS) dynamics using colored Petri nets to simulate disease progression and treatment effects.

Key Features:

• Colored Petri Net Formalism: Uses an extended Colored Petri Net (CPN) formalism to provide compact graphical representations and automatically derive ordinary differential equations (ODEs) encoding system dynamics.
• Modeling and Simulation: Constructs and simulates comprehensive ODE models of RRMS to investigate disease progression and treatment effects, including investigation of daclizumab administration despite its withdrawal due to adverse effects such as infections, encephalitis, and liver damage.
• Parameter Calibration: Calibrates ODE parameters via Latin Hypercube Sampling combined with the Partial Rank Correlation Coefficient (PRCC) index to fit and assess model behavior in healthy individuals and people with MS.
• Scenario Analysis: Performs scenario analyses for RRMS, including a daclizumab treatment scenario and a pregnancy scenario addressing the reduced incidence of relapses observed until delivery.

Scientific Applications:

• Disease Modeling: Enables mechanistic study of RRMS pathophysiology through CPN-derived ODE simulations.
• Treatment Evaluation: Supports assessment of therapeutic effects and safety profiles by simulating drug interventions such as daclizumab, including associated adverse effects.
• Research Methodology: Provides a computational framework combining CPNs, ODE derivation, and sensitivity-based calibration for systems-level analysis of immune-mediated inflammatory diseases like RRMS.

Methodology:

Biological processes are encoded as extended Colored Petri Nets from which ordinary differential equations (ODEs) are automatically derived, and ODE parameters are calibrated using Latin Hypercube Sampling combined with the Partial Rank Correlation Coefficient (PRCC) index.