BiPSim: a flexible and generic stochastic simulator for polymerization processes.

Area of Science:

• Computational Biology
• Systems Biology
• Biophysics

Background:

• Whole-cell modeling integrates diverse cellular processes for comprehensive simulation.
• Existing methods face challenges in tractably simulating complex, heterogeneous biological systems.

Purpose of the Study:

• Introduce BiPSim, an open-source stochastic simulator for template-based polymerization processes.
• Enable efficient and scalable whole-cell simulations of genome-wide biological phenomena.

Main Methods:

• Developed BiPSim with an abstract reaction representation and a constant-time Gillespie's Stochastic Simulation Algorithm (SSA).
• Implemented multi-level descriptions for polymerization processes to balance speed and granularity.
• Evaluated performance via genome-wide bacterial gene expression simulations.

Main Results:

• BiPSim demonstrates high efficiency for large-scale stochastic polymerization process simulations.
• The simulator supports simultaneous multi-level descriptions, allowing adjustable trade-offs between speed and model detail.
• Models developed with BiPSim are adaptable to different species due to the absence of hard-coded species-specific information.

Conclusions:

• BiPSim provides a powerful tool for simulating stochastic phenomena in biology at the genome-wide scale.
• The simulator's efficiency, flexibility, and adaptability open new avenues for whole-cell modeling research.
• Facilitates the study of complex biological systems across different organisms.