Potential based, spatial simulation of dynamically nested particles.

Area of Science:

• Computational Biology
• Biophysics
• Cellular Dynamics

Background:

• Cell biological phenomena rely on diffusion, active transport, and species localization, necessitating spatial considerations in modeling.
• Existing particle-based simulators often model cells as solid spheres or points, limiting the representation of multi-level spatial dynamics.
• There is a need for spatial approaches that account for the multi-level nature of cell biology, involving entities like proteins, vesicles, and cells with interrelated dynamics.

Purpose of the Study:

• To develop a particle-based simulation approach that explicitly accounts for multi-level spatial dynamics in cell biology.
• To introduce a method that supports compartmental dynamics and the relationships between different organizational levels.
• To enhance the expressiveness of spatial modeling by incorporating features like dynamic nesting, fusion, and fission of compartments.

Main Methods:

• Developed ML-Force, a particle-based simulation approach using hollow spheres to represent particles.
• Implemented compartmental dynamics and inter-level relationships using pair-wise potentials (forces) and the Langevin equation.
• Integrated arbitrary functions and attributes to define particle behavior, reaction kinetics, and non-spatial intra-compartmental dynamics.

Main Results:

• ML-Force supports compartmental dynamics (e.g., particles entering/leaving others) and bimolecular reactions via potentials and the Langevin equation.
• The approach allows for directed particle movement through independent forces and captures complex spatial dynamics like compartment fission.
• Applications in vesicle transport and yeast growth demonstrate the ability to integrate non-spatial intra-compartmental dynamics as stochastic events.

Conclusions:

• ML-Force seamlessly integrates compartmental dynamics (nesting, fusion, fission) with traditional reaction-diffusion dynamics using potentials and the Langevin equation.
• Attributes and arbitrary functions provide flexibility in describing spatial phenomena and relating dynamics across organizational levels.
• The method effectively models intra-cellular dynamics non-spatially, expanding the scope of multi-compartmental process simulation.