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Agent based modeling of blood coagulation system: implementation using a GPU based high speed framework.

Wenan Chen1, Kevin Ward, Qi Li

  • 1Department of Biostatistics, Virginia Commonwealth University, USA. chenw6@vcu.edu

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|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an Agent Based Modeling and Simulation (ABMS) approach for simulating complex biological systems like coagulation. A GPU-accelerated framework significantly enhances simulation speed for large-scale agent models.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Systems Biology

Background:

  • Coagulation and fibrinolytic systems are critical physiological processes.
  • Their complex, nonlinear interactions pose challenges for simultaneous microscopic and macroscopic study.

Purpose of the Study:

  • To develop an Agent Based Modeling and Simulation (ABMS) approach for studying coagulation and fibrinolytic systems.
  • To address the computational challenges of large-scale agent-based simulations.
  • To present a high-speed framework for coagulation simulation using graphics processing units (GPUs).

Main Methods:

  • Developed an Agent Based Modeling and Simulation (ABMS) framework.
  • Implemented a high-speed simulation utilizing GPU computing power.
  • Compared GPU implementation with NetLogo, Repast, and C versions.

Main Results:

  • The GPU implementation achieved over 10x speedup compared to C.
  • The GPU version was over 100x faster than Repast and over 300x faster than NetLogo.
  • Demonstrated significant computational efficiency for million-level agent simulations.

Conclusions:

  • GPU acceleration dramatically improves the speed and scalability of agent-based coagulation simulations.
  • The developed framework enables more comprehensive studies of complex biological systems.
  • This approach overcomes limitations of traditional simulation methods for large-scale biological modeling.