Literature DB >> 21745481

Spontaneous noise reduction in a strongly cooperative reaction model.

Zoran Konkoli1.   

Abstract

Noise characteristics of a simple multiparticle reaction model were investigated. The model can describe strongly cooperative reactions and is defined as follows. Particles A react in clusters of size k and each reaction forms a product molecule P. The back reaction is also allowed, and each cluster can dissociate into k reactants A. To describe a situation where the reaction is part of a pathway, the system is made open by assuming that particles A are injected and that particles P decay. This is a continuation study. The model is studied by using the same technique as employed previously, the pair approach reaction noise estimator (PARNES) method. Several new issues have been addressed. (i) In the previous work closeness to the Poisson distribution was used as a noise measure. In this work a more traditional noise measure, the ratio of the variance and the mean, was used to analyze stochastic features of the problem. (ii) The dependence of the new noise measure on k has been analyzed in detail, with an emphasis of investigating reactions with large k values. (iii) The previous study focused on understanding time-dependent issues, while this study focuses on describing the equilibrium state of the system. (iv) An exact solution (published elsewhere), available for closed system only, was used to re-investigate the validity of the PARNES method for describing reactions with large k. It was found that the PARNES method cannot describe fine details of the noise characteristics of such reactions.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21745481     DOI: 10.1016/j.jtbi.2011.06.036

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  1 in total

1.  Mathematical explanation of the predictive power of the X-level approach reaction noise estimator method.

Authors:  Zoran Konkoli
Journal:  Theor Biol Med Model       Date:  2012-04-13       Impact factor: 2.432

  1 in total

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