Source:http://linkedlifedata.com/resource/pubmed/id/18629023
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
6
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| pubmed:dateCreated |
2008-7-16
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| pubmed:abstractText |
Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the long-term influence of a gene on another gene or determine the long-term joint probabilistic behaviour of a few selected genes. Because matrix-based methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of two-state Markov chains, we illustrate the approach on a sub-network designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes.
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| pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-10475062,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-10485462,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-10896154,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-10903845,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-11092429,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-11283699,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-11381677,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-11847074,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-11911796,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-12169550,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-12376376,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-12907597,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-8415706,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-8710899,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18629023-8758892
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
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| pubmed:issn |
1531-6912
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
4
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
601-8
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| pubmed:year |
2003
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| pubmed:articleTitle |
Steady-state analysis of genetic regulatory networks modelled by probabilistic boolean networks.
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| pubmed:affiliation |
Cancer Genomics Laboratory, University of Texas, M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA.
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| pubmed:publicationType |
Journal Article
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