Source:http://linkedlifedata.com/resource/pubmed/id/16090013
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
1 Pt 1
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| pubmed:dateCreated |
2005-8-10
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| pubmed:abstractText |
Nonlinear control techniques by means of a software sensor that are commonly used in chemical engineering could be also applied to genetic regulation processes. We provide here a realistic formulation of this procedure by introducing an additive white Gaussian noise, which is usually found in experimental data. Besides, we include model errors, meaning that we assume we do not know the nonlinear regulation function of the process. In order to illustrate this procedure, we employ the Goodwin dynamics of the concentrations [B. C. Goodwin, (Academic, New York, 1963)] in the simple form recently applied to single gene systems and some operon cases [H. De Jong, J. Comput. Biol. 9, 67 (2002)], which involves the dynamics of the mRNA, given protein and metabolite concentrations. Further, we present results for a three gene case in coregulated sets of transcription units as they occur in prokaryotes. However, instead of considering their full dynamics, we use only the data of the metabolites and a designed software sensor. We also show, more generally, that it is possible to rebuild the complete set of nonmeasured concentrations despite the uncertainties in the regulation function or, even more, in the case of not knowing the mRNA dynamics. In addition, the rebuilding of concentrations is not affected by the perturbation due to the additive white Gaussian noise and also we managed to filter the noisy output of the biological system.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:chemical | |
| pubmed:status |
MEDLINE
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| pubmed:month |
Jul
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| pubmed:issn |
1539-3755
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
72
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
011919
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| pubmed:dateRevised |
2008-11-21
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| pubmed:meshHeading |
pubmed-meshheading:16090013-Algorithms,
pubmed-meshheading:16090013-Animals,
pubmed-meshheading:16090013-Biophysical Phenomena,
pubmed-meshheading:16090013-Biophysics,
pubmed-meshheading:16090013-Biosensing Techniques,
pubmed-meshheading:16090013-Computer Simulation,
pubmed-meshheading:16090013-Computers,
pubmed-meshheading:16090013-DNA, Complementary,
pubmed-meshheading:16090013-Gene Expression Regulation,
pubmed-meshheading:16090013-Kinetics,
pubmed-meshheading:16090013-Models, Biological,
pubmed-meshheading:16090013-Models, Statistical,
pubmed-meshheading:16090013-Models, Theoretical,
pubmed-meshheading:16090013-Nonlinear Dynamics,
pubmed-meshheading:16090013-Normal Distribution,
pubmed-meshheading:16090013-Oscillometry,
pubmed-meshheading:16090013-RNA, Messenger,
pubmed-meshheading:16090013-Software,
pubmed-meshheading:16090013-Statistics as Topic,
pubmed-meshheading:16090013-Stochastic Processes,
pubmed-meshheading:16090013-Time,
pubmed-meshheading:16090013-Time Factors,
pubmed-meshheading:16090013-Transducers
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| pubmed:year |
2005
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| pubmed:articleTitle |
Nonlinear software sensor for monitoring genetic regulation processes with noise and modeling errors.
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| pubmed:affiliation |
Potosinian Institute of Science and Technology, San Luis Potosí, Mexico. vrani@ipicyt.edu.mx
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| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
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