ML-Force

ML-Force simulates spatial dynamics in cell biological systems by representing particles as hollow spheres and using pair-wise potentials and the Langevin equation to model diffusion, active transport, bimolecular reactions, and compartmental nesting, fusion, and fission across proteins, vesicles, and cells.

Key Features:

• Compartmental Dynamics: Models continuous nesting, fusion, and fission of compartmental structures represented by hollow-sphere particles within and between proteins, vesicles, and cells.
• Hollow-sphere Particle Representation: Represents particles as hollow spheres that can contain other particles and support particle entry and exit.
• Pair-wise Potentials and Langevin Dynamics: Uses pair-wise potentials (forces) together with the Langevin equation to compute particle interactions and motion.
• Particle-based Reaction-Diffusion: Integrates particle-based reaction-diffusion dynamics and supports bimolecular reactions.
• Expressive Attribute-based Modeling: Allows attributes on particles, their attributes, and their contents to determine reaction outcomes and kinetics and to represent non-spatial intra-compartmental dynamics as stochastic events.
• Embedded Domain-Specific Language: Includes a rudimentary rule-based embedded domain-specific modeling language for specifying and continuously executing models.
• Directed Movement via External Forces: Applies forces independent of other particles to produce directed movement and to drive complex behaviors such as compartment fission.

Scientific Applications:

• Vesicle transport: Demonstrated in simulations of vesicle transport capturing multi-compartmental movement and interactions.
• Yeast growth: Applied to yeast growth models to represent spatial and compartmental dynamics.
• Multi-compartmental processes: Used to model processes spanning organizational levels including proteins, vesicles, and cells.

Methodology:

Represents particles as hollow spheres; uses pair-wise potentials (forces) and the Langevin equation; integrates particle-based reaction-diffusion and bimolecular reactions; models continuous nesting, fusion, and fission; supports attribute-based reaction specification and stochastic intra-compartmental events via a rule-based embedded domain-specific language; applies forces independent of other particles for directed movement.