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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F13%3A00070435%21RIV14-MSM-14330___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
When trying to uncover the nature of gene regulatory and signaling networks, the modelers currently have a zoo of inference algorithms at their disposal. These algorithms are very useful for converting raw experimental data into mathematical models of various nature – from pure differential equations to very abstract, logical causal networks. However, the algorithms rarely permit refitting of the network based on new data, and further enhancements are commonly done by hand, with quality of the process dependent on the experience of the modeler. We are therefore focusing on development of an environment that works with high level causal models and allows for automated comparison of the properties of the model and the behavior measured or observed in the modeled system. Since we expect the models to have wide range of possible kinetic parameters, we also provide means of manipulating with sets of parametrizations of the model, e.g. their ranking w.r.t. When trying to uncover the nature of gene regulatory and signaling networks, the modelers currently have a zoo of inference algorithms at their disposal. These algorithms are very useful for converting raw experimental data into mathematical models of various nature – from pure differential equations to very abstract, logical causal networks. However, the algorithms rarely permit refitting of the network based on new data, and further enhancements are commonly done by hand, with quality of the process dependent on the experience of the modeler. We are therefore focusing on development of an environment that works with high level causal models and allows for automated comparison of the properties of the model and the behavior measured or observed in the modeled system. Since we expect the models to have wide range of possible kinetic parameters, we also provide means of manipulating with sets of parametrizations of the model, e.g. their ranking w.r.t.
dcterms:title
Esther: Introducing an Online Platform for Parameter Identification of Boolean Networks Esther: Introducing an Online Platform for Parameter Identification of Boolean Networks
skos:prefLabel
Esther: Introducing an Online Platform for Parameter Identification of Boolean Networks Esther: Introducing an Online Platform for Parameter Identification of Boolean Networks
skos:notation
RIV/00216224:14330/13:00070435!RIV14-MSM-14330___
n7:predkladatel
n20:orjk%3A14330
n3:aktivita
n8:S n8:P
n3:aktivity
P(EE2.3.20.0256), S
n3:dodaniDat
n19:2014
n3:domaciTvurceVysledku
n4:6717020 n4:8225400 n4:1045709
n3:druhVysledku
n16:D
n3:duvernostUdaju
n22:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
73234
n3:idVysledku
RIV/00216224:14330/13:00070435
n3:jazykVysledku
n12:eng
n3:klicovaSlova
systems biology; boolean networks; model checking
n3:klicoveSlovo
n9:boolean%20networks n9:systems%20biology n9:model%20checking
n3:kontrolniKodProRIV
[850FDB6BFBE2]
n3:mistoKonaniAkce
Vienna
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Computational Methods in Systems Biology 11th International Conference, CMSB 2013, Klosterneuburg, Austria, September 22-24, 2013, Proceedings
n3:obor
n14:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n13:EE2.3.20.0256
n3:rokUplatneniVysledku
n19:2013
n3:tvurceVysledku
Streck, Adam Kolčák, Juraj Šafránek, David Siebert, Heike
n3:typAkce
n10:WRD
n3:zahajeniAkce
2013-01-01+01:00
s:issn
0302-9743
s:numberOfPages
2
n17:hasPublisher
Springer-Verlag
n6:isbn
9783642407079
n18:organizacniJednotka
14330