Linked Life Data

19673546

Source:http://linkedlifedata.com/resource/pubmed/id/19673546

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2009-8-13
pubmed:abstractText
Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-cell variability even in clonal populations. Stochastic biochemical networks are modeled as continuous time discrete state Markov processes whose probability density functions evolve according to a chemical master equation (CME). The CME is not solvable but for the simplest cases, and one has to resort to kinetic Monte Carlo techniques to simulate the stochastic trajectories of the biochemical network under study. A commonly used such algorithm is the stochastic simulation algorithm (SSA). Because it tracks every biochemical reaction that occurs in a given system, the SSA presents computational difficulties especially when there is a vast disparity in the timescales of the reactions or in the number of molecules involved in these reactions. This is common in cellular networks, and many approximation algorithms have evolved to alleviate the computational burdens of the SSA. Here, we present a rigorously derived modified CME framework based on the partition of a biochemically reacting system into restricted and unrestricted reactions. Although this modified CME decomposition is as analytically difficult as the original CME, it can be naturally used to generate a hierarchy of approximations at different levels of accuracy. Most importantly, some previously derived algorithms are demonstrated to be limiting cases of our formulation. We apply our methods to biologically relevant test systems to demonstrate their accuracy and efficiency.
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-10681449, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-11720979, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-11972055, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-12237400, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-12687005, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-12935899, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-15166317, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-15308767, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-15883588, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-16460146, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-17379809, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-17443391, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-17632061, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-17632062, http://linkedlifedata.com/resource/pubmed/commentcorrection/19673546-9023339
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1089-7690
pubmed:author
pubmed:issnType
Electronic
pubmed:day
7
pubmed:volume
131
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
054102
pubmed:dateRevised
2010-9-27
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A rigorous framework for multiscale simulation of stochastic cellular networks.
pubmed:affiliation
Department of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California San Francisco, 1700, 4th Street, San Francisco, California 94143-2542, USA. michael.chevalier@ucsf.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural