Linked Life Data

17048393

Source:http://linkedlifedata.com/resource/pubmed/id/17048393

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-10-19
pubmed:abstractText
We discuss several issues pertaining to the use of stochastic biochemical systems for modeling transcriptional regulation in single cells. By appropriately choosing the system state, we can model transcriptional regulation by a hidden Markov model (HMM). This opens the possibility of using well-known techniques for the statistical analysis and stochastic control of HMMs to mathematically and computationally study transcriptional regulation in single cells. Unfortunately, in all but a few simple cases, analytical characterization of the statistical behavior of the proposed HMM is not possible. Moreover, analysis by Monte Carlo simulation is computationally cumbersome. We discuss several techniques for approximating the HMM by one that is more tractable. We employ simulations, based on a biologically relevant transcriptional regulatory system, to show the relative merits and limitations of various approximation techniques and provide general guidelines for their use.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1545-5963
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
57-71
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:articleTitle
A hidden Markov model for transcriptional regulation in single cells.
pubmed:affiliation
Whitaker Biomedical Engineering Institute, Clark Hall 308A, The Johns Hopkins University, Baltimore, MD 21218, USA. goutsias@jhu.edu
pubmed:publicationType
Journal Article