Multi-scale stochastic simulation of diffusion-coupled agents and its application to cell culture simulation

Area of Science:

• Computational Biology
• Systems Biology
• Biophysics

Background:

• Biological systems coordinate responses via cell-to-cell molecular signaling.
• Simulating coupled intracellular and extracellular processes presents significant challenges.

Purpose of the Study:

• To develop an efficient stochastic simulation method for diffusion processes.
• To enable synchronization between intracellular and extracellular cellular conditions.
• To study cell-cell communication in viral infection signaling.

Main Methods:

• Modification of Gillespie's chemical reaction algorithm for stochastic diffusion simulation.
• Simulation of individual cells as independent agents reacting to their local environment.
• Time-scale separation between intracellular and extracellular processes.

Main Results:

• The simulation method accurately models diffusion and cell-environment interactions.
• It successfully simulates interferon-mediated signaling between human monocyte-derived dendritic cells during viral infection.
• Predicted early cell activation is robust and sensitive, independent of the infected cell fraction.

Conclusions:

• The developed method overcomes challenges in simulating multiscale biological signaling.
• It provides a valuable tool for understanding complex cellular communication systems.
• Findings validate the simulation's accuracy and predictive power through experimental concordance.