Title 



Singlevariable reaction systems: deterministic and stochastic models
 
Author 



 
Abstract 



Biochemical reaction networks are often described by deterministic models based on macroscopic rate equations. However, for small numbers of molecules, intrinsic noise can play a significant role and stochastic methods may thus be required. In this work, we analyze the differences and similarities between a class of macroscopic deterministic models and corresponding mesoscopic stochastic models. We derive expressions that provide a clear and intuitive view upon the behavior of the stochastic model. In particular, these expressions show the dependence of both the dynamics and the stationary distribution of the stochastic model on the number of molecules in the system. As expected, most properties of the stochastic model correspond well with those in the deterministic model if the number of molecules is large enough. However, for some properties, both models are inconsistent, even if the number of molecules in the stochastic model tends to infinity. Throughout this paper, we use a bistable autophosphorylation cycle as a running example. For such a bistable system, we give an explicit proof that the rate of convergence to the stationary distribution (or the second eigenvalue of the transition matrix) depends exponentially on the number of molecules. (C) 2010 Elsevier Inc. All rights reserved.   
Language 



English
 
Source (journal) 



Mathematical biosciences.  New York, N.Y.  
Publication 



New York, N.Y. : 2010
 
ISSN 



00255564
 
Volume/pages 



227:2(2010), p. 105116
 
ISI 



000282727200003
 
Full text (Publishers DOI) 


  
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