Data-Informed Parameter Synthesis for Population Markov Chains

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2019
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
EU-Projektnummer
DFG-Projektnummer
Forschungsförderung
Projekt
Open Access-Veröffentlichung
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
ČEŠKA, Milan, ed., Nicola PAOLETTI, ed.. Hybrid Systems Biology : 6th International Workshop, HSB 2019, Prague, Czech Republic, April 6-7, 2019, Revised Selected Papers. Cham: Springer, 2019, pp. 147-164. Lecture Notes in Bioinformatics. 11705. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-28041-3. Available under: doi: 10.1007/978-3-030-28042-0_10
Zusammenfassung

Stochastic population models are widely used to model phenomena in different areas such as chemical kinetics or collective animal behaviour. Quantitative analysis of stochastic population models easily becomes challenging, due to the combinatorial propagation of dependencies across the population. The complexity becomes especially prominent when model’s parameters are not known and available measurements are limited. In this paper, we illustrate this challenge in a concrete scenario: we assume a simple communication scheme among identical individuals, inspired by how social honeybees emit the alarm pheromone to protect the colony in case of danger. Together, n individuals induce a population Markov chain with n parameters. In addition, we assume to be able to experimentally observe the states only after the steady-state is reached. In order to obtain the parameters of the individual’s behaviour, by utilising the data measurements for population, we combine two existing techniques. First, we use the tools for parameter synthesis for Markov chains with respect to temporal logic properties, and then we employ CEGAR-like reasoning to find the viable parameter space up to desired coverage. We report the performance on a number of synthetic data sets.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
570 Biowissenschaften, Biologie
Schlagwörter
Konferenz
6th International Workshop, Hybrid Systems Biology (HSB) 2019, 6. Apr. 2019 - 7. Apr. 2019, Prague, Czech Republic
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690HAJNAL, Matej, Morgane NOUVIAN, David SAFRANEK, Tatjana PETROV, 2019. Data-Informed Parameter Synthesis for Population Markov Chains. 6th International Workshop, Hybrid Systems Biology (HSB) 2019. Prague, Czech Republic, 6. Apr. 2019 - 7. Apr. 2019. In: ČEŠKA, Milan, ed., Nicola PAOLETTI, ed.. Hybrid Systems Biology : 6th International Workshop, HSB 2019, Prague, Czech Republic, April 6-7, 2019, Revised Selected Papers. Cham: Springer, 2019, pp. 147-164. Lecture Notes in Bioinformatics. 11705. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-28041-3. Available under: doi: 10.1007/978-3-030-28042-0_10
BibTex
@inproceedings{Hajnal2019-08-01DataI-48772,
  year={2019},
  doi={10.1007/978-3-030-28042-0_10},
  title={Data-Informed Parameter Synthesis for Population Markov Chains},
  number={11705},
  isbn={978-3-030-28041-3},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Bioinformatics},
  booktitle={Hybrid Systems Biology : 6th International Workshop, HSB 2019, Prague, Czech Republic, April 6-7, 2019, Revised Selected Papers},
  pages={147--164},
  editor={Češka, Milan and Paoletti, Nicola},
  author={Hajnal, Matej and Nouvian, Morgane and Safranek, David and Petrov, Tatjana}
}
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/123456789/48772">
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:creator>Nouvian, Morgane</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-02-25T10:36:11Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Petrov, Tatjana</dc:creator>
    <dc:contributor>Hajnal, Matej</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:abstract xml:lang="eng">Stochastic population models are widely used to model phenomena in different areas such as chemical kinetics or collective animal behaviour. Quantitative analysis of stochastic population models easily becomes challenging, due to the combinatorial propagation of dependencies across the population. The complexity becomes especially prominent when model’s parameters are not known and available measurements are limited. In this paper, we illustrate this challenge in a concrete scenario: we assume a simple communication scheme among identical individuals, inspired by how social honeybees emit the alarm pheromone to protect the colony in case of danger. Together, n individuals induce a population Markov chain with n parameters. In addition, we assume to be able to experimentally observe the states only after the steady-state is reached. In order to obtain the parameters of the individual’s behaviour, by utilising the data measurements for population, we combine two existing techniques. First, we use the tools for parameter synthesis for Markov chains with respect to temporal logic properties, and then we employ CEGAR-like reasoning to find the viable parameter space up to desired coverage. We report the performance on a number of synthetic data sets.</dcterms:abstract>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/48772"/>
    <dc:contributor>Safranek, David</dc:contributor>
    <dc:contributor>Petrov, Tatjana</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Safranek, David</dc:creator>
    <dc:creator>Hajnal, Matej</dc:creator>
    <dcterms:issued>2019-08-01</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Nouvian, Morgane</dc:contributor>
    <dc:language>eng</dc:language>
    <dcterms:title>Data-Informed Parameter Synthesis for Population Markov Chains</dcterms:title>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-02-25T10:36:11Z</dc:date>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet