MCell4 with BioNetGen

MCell4 with BioNetGen simulates rule-based reaction-diffusion systems using a 3D Monte Carlo approach for discrete molecules and macromolecular complexes in solution, on membrane surfaces, and combinations thereof.

Key Features:

• Python Interface: Enables interfacing with external simulators for creation of event-driven multiscale and multiphysics simulations.
• Native BioNetGen Language Support: Allows spatially resolved execution of BNGL models in MCell4 and, with simplifying assumptions, in the BioNetGen simulation environment via the libBNG library.
• 3D Monte Carlo Modeling: Simulates diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof.
• Particle-Based Stochastic Approach: Provides accurate and efficient simulation for systems with small numbers of interacting molecules where well-mixed continuum approximations break down.

Scientific Applications:

• Biochemical Signaling Pathway Modeling: Models spatially organized biological systems including subcellular compartments, membranes, scaffolds, and organelles with stochastic behavior.
• Model Validation and Comparison: Accelerates and simplifies validation by enabling BNGL models to run in both spatially resolved and well-mixed simulation environments.
• Multiscale and Multiphysics Simulations: Supports creation of sophisticated event-driven simulations through interfacing with external simulators.

Methodology:

MCell4 uses generalized 3D Monte Carlo methods to model diffusion and chemical reactions of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. Native BNGL support is implemented through the libBNG library.