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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F11%3A10105562%21RIV12-MSM-11320___
rdf:type
n11:Vysledek skos:Concept
rdfs:seeAlso
http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-61779-334-9_22
dcterms:description
We demonstrate the advantage of the simultaneous multicurve nonlinear least-squares analysis over that of the conventional single-curve analysis. Fitting results are subjected to thorough Monte Carlo analysis for rigorous assessment of confidence intervals and parameter correlations. The comparison is performed on a practical example of simulated steady-state reaction kinetics complemented with isothermal calorimetry (ITC) data resembling allosteric behavior of rabbit muscle pyruvate kinase (RMPK). Global analysis improves accuracy and confidence limits of model parameters. Cross-correlation between parameters is also reduced with accompanying enhancement of the model-testing power. This becomes especially important for validation of models with %22difficult%22 highly cross-correlated parameters. We show how proper experimental design and critical evaluation of data can improve the chance of differentiating models. We demonstrate the advantage of the simultaneous multicurve nonlinear least-squares analysis over that of the conventional single-curve analysis. Fitting results are subjected to thorough Monte Carlo analysis for rigorous assessment of confidence intervals and parameter correlations. The comparison is performed on a practical example of simulated steady-state reaction kinetics complemented with isothermal calorimetry (ITC) data resembling allosteric behavior of rabbit muscle pyruvate kinase (RMPK). Global analysis improves accuracy and confidence limits of model parameters. Cross-correlation between parameters is also reduced with accompanying enhancement of the model-testing power. This becomes especially important for validation of models with %22difficult%22 highly cross-correlated parameters. We show how proper experimental design and critical evaluation of data can improve the chance of differentiating models.
dcterms:title
The advantage of global fitting of data involving complex linked reactions The advantage of global fitting of data involving complex linked reactions
skos:prefLabel
The advantage of global fitting of data involving complex linked reactions The advantage of global fitting of data involving complex linked reactions
skos:notation
RIV/00216208:11320/11:10105562!RIV12-MSM-11320___
n11:predkladatel
n15:orjk%3A11320
n5:aktivita
n7:Z
n5:aktivity
Z(MSM0021620835)
n5:dodaniDat
n9:2012
n5:domaciTvurceVysledku
n23:7948395
n5:druhVysledku
n20:C
n5:duvernostUdaju
n10:S
n5:entitaPredkladatele
n16:predkladatel
n5:idSjednocenehoVysledku
184767
n5:idVysledku
RIV/00216208:11320/11:10105562
n5:jazykVysledku
n19:eng
n5:klicovaSlova
Monte Carlo; linked reactions; data analysis; global fitting
n5:klicoveSlovo
n8:data%20analysis n8:linked%20reactions n8:Monte%20Carlo n8:global%20fitting
n5:kontrolniKodProRIV
[81D7E55DC2BD]
n5:mistoVydani
New York
n5:nazevEdiceCisloSvazku
796
n5:nazevZdroje
Allostery: Methods and Protocols
n5:obor
n22:BO
n5:pocetDomacichTvurcuVysledku
1
n5:pocetStranKnihy
457
n5:pocetTvurcuVysledku
2
n5:rokUplatneniVysledku
n9:2011
n5:tvurceVysledku
Heřman, Petr Lee, J., C.
n5:zamer
n21:MSM0021620835
s:numberOfPages
23
n13:doi
10.1007/978-1-61779-334-9_22
n6:hasPublisher
Humana Press
n17:isbn
978-1-61779-333-2
n12:organizacniJednotka
11320