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Comparison of discrete- and continuous-state stochastic methods to model neuronal signal transduction

Published: 02 August 2010 Publication History

Abstract

Several stochastic methods have been developed for the simulation of biochemical reactions. The best known stochastic reaction method is the Gillespie stochastic simulation algorithm which, in this study, is compared to two types of stochastic differential equation models. As a test case, we use a neuronal signal transduction network of 110 reactions and 63 chemical species. We concentrate on showing when stochastic methods are especially needed and how distributions from different stochastic methods differ. We conclude that stochastic differential equations are not suitable for use in volumes on the order of spines, and that even in dendritic and soma regions a portion of the chemical species will exhibit negative excursions. For this reason, a hybrid deterministic and stochastic method may be most applicable to the larger volumes while the Gillespie stochastic simulation algorithm is needed for spines and subspinal volumes. In addition, a grid computing solution is needed for larger volumes to reduce the computation time to tractable levels.

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cover image ACM Conferences
BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
August 2010
705 pages
ISBN:9781450304382
DOI:10.1145/1854776
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 August 2010

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  1. Gillespie stochastic simulation algorithm
  2. chemical langevin equation
  3. signal transduction
  4. stochastic differential equation

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