An improved constraint filtering technique for inferring hidden states and parameters of a biological model

Murtuza Baker, Syed; Poskar, C. Hart; Schreiber, Falk; Junker, Björn H.

An improved constraint filtering technique for inferring hidden states and parameters of a biological model

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2013

Autor:innen

Murtuza Baker, Syed

Poskar, C. Hart

Schreiber, Falk

Junker, Björn H.

DOI (zitierfähiger Link)

10.1093/bioinformatics/btt097

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Bioinformatics ; 29 (2013), 8. - S. 1052-1059. - ISSN 1367-4803. - eISSN 1460-2059

Zusammenfassung

In systems biology, kinetic models represent the biological system using a set of ordinary differential equations (ODEs). The correct values of the parameters within these ODEs are critical for a reliable study of the dynamic behaviour of such systems. Typically, it is only possible to experimentally measure a fraction of these parameter values. The rest must be indirectly determined from measurements of other quantities. In this article, we propose a novel statistical inference technique to computationally estimate these unknown parameter values. By characterizing the ODEs with non-linear state-space equations, this inference technique models the unknown parameters as hidden states, which can then be estimated from noisy measurement data.

Fachgebiet (DDC)

004 Informatik

Zitieren

ISO 690

MURTUZA BAKER, Syed, C. Hart POSKAR, Falk SCHREIBER, Björn H. JUNKER, 2013. An improved constraint filtering technique for inferring hidden states and parameters of a biological model. In: Bioinformatics. 29(8), pp. 1052-1059. ISSN 1367-4803. eISSN 1460-2059. Available under: doi: 10.1093/bioinformatics/btt097

BibTex

@article{MurtuzaBaker2013-04-15impro-38241,
  year={2013},
  doi={10.1093/bioinformatics/btt097},
  title={An improved constraint filtering technique for inferring hidden states and parameters of a biological model},
  number={8},
  volume={29},
  issn={1367-4803},
  journal={Bioinformatics},
  pages={1052--1059},
  author={Murtuza Baker, Syed and Poskar, C. Hart and Schreiber, Falk and Junker, Björn H.}
}

RDF

<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/38241">
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-03-31T09:22:07Z</dcterms:available>
    <dc:language>eng</dc:language>
    <dcterms:abstract xml:lang="eng">In systems biology, kinetic models represent the biological system using a set of ordinary differential equations (ODEs). The correct values of the parameters within these ODEs are critical for a reliable study of the dynamic behaviour of such systems. Typically, it is only possible to experimentally measure a fraction of these parameter values. The rest must be indirectly determined from measurements of other quantities. In this article, we propose a novel statistical inference technique to computationally estimate these unknown parameter values. By characterizing the ODEs with non-linear state-space equations, this inference technique models the unknown parameters as hidden states, which can then be estimated from noisy measurement data.</dcterms:abstract>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-03-31T09:22:07Z</dc:date>
    <dc:contributor>Poskar, C. Hart</dc:contributor>
    <dc:creator>Schreiber, Falk</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Murtuza Baker, Syed</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/38241"/>
    <dc:creator>Murtuza Baker, Syed</dc:creator>
    <dc:creator>Poskar, C. Hart</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Schreiber, Falk</dc:contributor>
    <dcterms:title>An improved constraint filtering technique for inferring hidden states and parameters of a biological model</dcterms:title>
    <dc:creator>Junker, Björn H.</dc:creator>
    <dcterms:issued>2013-04-15</dcterms:issued>
    <dc:contributor>Junker, Björn H.</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Nein