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Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

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A massively parallel implementation of gillespie algorithm on FPGAs.

Luca Macchiarulo¹

¹Electrical Engineering Department - University of Hawaii at Manoa, 2640 Dole Street, Honolulu (HI), USA. lucam@hawaii.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 24, 2009

Summary

This study accelerates biochemical simulations using dedicated FPGA hardware. The novel approach achieves 100 million time steps per second, significantly boosting computational performance for complex models.

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Area of Science:

Computational Biology
Biochemical Engineering
Hardware Acceleration

Background:

Complex stochastic simulations of biochemical systems are computationally intensive.
Existing simulation acceleration methods have limitations.
Field-programmable gate arrays (FPGAs) offer potential for specialized hardware acceleration.

Purpose of the Study:

To develop and evaluate a dedicated hardware architecture for accelerating complex stochastic biochemical simulations.
To compare the proposed FPGA-based approach with existing simulation acceleration techniques.
To demonstrate the feasibility and performance of the novel system.

Main Methods:

Design of a dedicated hardware architecture implemented on FPGAs.
Development of a retargetable hardware description generator for simulation problems.
Experimental validation and performance benchmarking of the proposed system.

Main Results:

The proposed FPGA architecture significantly accelerates stochastic biochemical simulations.
Achieved performance of 100 million time steps per second for large models (1000 reactions).
Demonstrated feasibility and high performance compared to existing methods.

Conclusions:

FPGA-based hardware acceleration is a viable and highly effective strategy for complex biochemical simulations.
The retargetable hardware description enables broad applicability of the system.
This approach offers a substantial performance improvement for computational biology research.