Document Detail


Efficient calculation of steady state probability distribution for stochastic biochemical reaction network.
MedLine Citation:
PMID:  23134718     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The Steady State (SS) probability distribution is an important quantity needed to characterize the steady state behavior of many stochastic biochemical networks. In this paper, we propose an efficient and accurate approach to calculating an approximate SS probability distribution from solution of the Chemical Master Equation (CME) under the assumption of the existence of a unique deterministic SS of the system. To find the approximate solution to the CME, a truncated state-space representation is used to reduce the state-space of the system and translate it to a finite dimension. The subsequent ill-posed eigenvalue problem of a linear system for the finite state-space can be converted to a well-posed system of linear equations and solved. The proposed strategy yields efficient and accurate estimation of noise in stochastic biochemical systems. To demonstrate the approach, we applied the method to characterize the noise behavior of a set of biochemical networks of ligand-receptor interactions for Bone Morphogenetic Protein (BMP) signaling. We found that recruitment of type II receptors during the receptor oligomerization by itself doesn't not tend to lower noise in receptor signaling, but regulation by a secreted co-factor may provide a substantial improvement in signaling relative to noise. The steady state probability approximation method shortened the time necessary to calculate the probability distributions compared to earlier approaches, such as Gillespie's Stochastic Simulation Algorithm (SSA) while maintaining high accuracy.
Authors:
Shahriar Karim; Gregery T Buzzard; David M Umulis
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-10-26
Journal Detail:
Title:  BMC genomics     Volume:  13 Suppl 6     ISSN:  1471-2164     ISO Abbreviation:  BMC Genomics     Publication Date:  2012  
Date Detail:
Created Date:  2012-11-08     Completed Date:  2013-04-16     Revised Date:  2013-07-11    
Medline Journal Info:
Nlm Unique ID:  100965258     Medline TA:  BMC Genomics     Country:  England    
Other Details:
Languages:  eng     Pagination:  S10     Citation Subset:  IM    
Affiliation:
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Animals
Bone Morphogenetic Proteins / metabolism
Drosophila melanogaster / metabolism
Kinetics
Models, Biological*
Signal Transduction
Chemical
Reg. No./Substance:
0/Bone Morphogenetic Proteins
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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