COPASI

COPASI performs simulation and analysis of mathematical models of biochemical networks to study the dynamics of metabolic networks, cell-signaling pathways, regulatory networks, and infectious disease models.

Key Features:

• Steady-state simulations: Computes steady-state solutions of kinetic models to analyze equilibrium behavior.
• Time-course simulations: Simulates temporal evolution of species concentrations using deterministic solvers.
• Stoichiometric analyses: Performs stoichiometric and flux-based examinations of reaction networks.
• Parameter scanning: Systematically varies parameters to explore model behavior across parameter spaces.
• Sensitivity analysis: Quantifies parameter sensitivities, including metabolic control analysis, to assess control coefficients and robustness.
• Global optimization: Applies global optimization algorithms for parameter search and model fitting.
• Parameter estimation: Estimates kinetic parameters from experimental data using optimization routines.
• Stochastic simulation: Executes stochastic simulation algorithms to capture intrinsic noise in reaction systems.
• Deterministic–stochastic switching: Supports switching between deterministic and stochastic simulation methods and hybrid deterministic–stochastic approaches.
• Random number generator resolution: Allows control and consideration of random number generator resolution in stochastic simulations.
• SBML and BioModels compatibility: Imports and uses models in SBML format, including models available from the BioModels database.

Scientific Applications:

• Systems biology: Analysis and simulation of system-level behavior in biochemical networks.
• Metabolic network analysis: Investigation of metabolic pathway dynamics, flux control, and steady states.
• Cell-signaling pathway dynamics: Modeling temporal signaling responses and cascade behaviors.
• Regulatory network modeling: Exploration of gene regulatory and feedback network dynamics.
• Infectious disease modeling: Simulation of pathogen–host interaction networks and disease-related biochemical processes.
• Biotechnology applications: Quantitative modeling to support biotechnological process and pathway design.
• Model reuse and benchmarking: Reanalysis and reuse of published SBML models from the BioModels database for validation and comparison.

Methodology:

Computational methods explicitly include steady-state and time-course simulations, stoichiometric analyses, parameter scanning, sensitivity analysis including metabolic control analysis, global optimization, parameter estimation, stochastic simulation with control of random number generator resolution, deterministic–stochastic switching and hybrid simulation approaches, and SBML model import (e.g., from BioModels).