DocumentCode :
1535090
Title :
A Computational Tool for Monte Carlo Simulations of Biomolecular Reaction Networks Modeled on Physical Principles
Author :
Li, Isaac T S ; Mills, Evan ; Truong, Kevin
Author_Institution :
Inst. of Biomater. & Biomed. Eng., Univ. of Toronto, Toronto, ON, Canada
Volume :
9
Issue :
1
fYear :
2010
fDate :
3/1/2010 12:00:00 AM
Firstpage :
24
Lastpage :
30
Abstract :
Deciphering and designing complex biomolecular networks in the cell are the goals of systems and synthetic biology, respectively. The effects of localization, spatial heterogeneity, and molecular fluctuations in biomolecular networks are not well understood. We present a theoretical approach based on physical principles to accurately simulate biomolecular networks using the Monte Carlo method. Incorporating this theory, a computational tool named Monte Carlo biomolecular simulator (MBS) was developed, enabling studies of biomolecular kinetics with both spatial and temporal resolutions. The accuracy of MBS was verified by comparison against the classical deterministic approaches. Furthermore, the effects of localization, spatial heterogeneity, and molecular fluctuations were studied in three simulated model systems, showing their impact on the overall reaction kinetics. This work demonstrates the unique insights that can be discovered by considering the subtle effects that can be created by the spatial and temporal kinetics of biomolecular reaction networks.
Keywords :
Monte Carlo methods; biochemistry; biology computing; cellular biophysics; chemistry computing; molecular biophysics; MBS; Monte Carlo biomolecular simulator; Monte Carlo simulations; biomolecular network fluctuations; biomolecular network localisation; biomolecular network spatial heterogeneity; biomolecular reaction networks; cellular biomolecular networks; spatial kinetics; temporal kinetics; Biomolecular reaction networks; Monte Carlo simulation; spatiotemporal resolution; Calcium Signaling; Cells; Computational Biology; DNA; Diffusion; Metabolic Networks and Pathways; Models, Biological; Monte Carlo Method; Proteins; Signal Transduction; Stochastic Processes;
fLanguage :
English
Journal_Title :
NanoBioscience, IEEE Transactions on
Publisher :
ieee
ISSN :
1536-1241
Type :
jour
DOI :
10.1109/TNB.2009.2035114
Filename :
5308210
Link To Document :
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