Expressive rule-based modeling and fast simulation for dynamic compartments

Area of Science:

• Computational Biology
• Cellular Dynamics
• Systems Biology

Background:

• Cellular compartmentalization is crucial for biological processes.
• Existing rule-based stochastic simulation tools often lack dynamic compartmentalization capabilities.
• Modeling dynamic changes in cellular compartments presents significant challenges for language design and simulation engines.

Purpose of the Study:

• To develop a rule-based modeling language and efficient simulation engine that supports dynamic compartmentalization in cell biology.
• To adapt the ML-Rules language for flexible modeling of evolving compartmental structures.
• To enable faster and more accessible simulations of cell biological models with dynamic compartments.

Main Methods:

• Adapted the ML-Rules language to support a wide range of compartmental dynamics.
• Developed an efficient simulation engine using specialized data structures and algorithms in Rust.
• Implemented a WebAssembly-based prototype for easy access to the modeling language and simulations.

Main Results:

• The adapted ML-Rules language effectively models diverse compartmental dynamics.
• The new simulation engine demonstrates a two-orders-of-magnitude performance improvement over previous ML-Rules simulations.
• Case studies confirm the accuracy and efficiency of the implemented approach.

Conclusions:

• The developed system provides a powerful and efficient solution for modeling dynamic cellular compartmentalization.
• The WebAssembly implementation lowers the barrier for researchers to explore complex cell models.
• This work advances the field of rule-based stochastic simulation for dynamic biological systems.