A framework for scalable parameter estimation of gene circuit models using structural information

Handle URI:
http://hdl.handle.net/10754/325439
Title:
A framework for scalable parameter estimation of gene circuit models using structural information
Authors:
Kuwahara, Hiroyuki; Fan, Ming; Wang, Suojin; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Motivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. The Author 2013.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Kuwahara H, Fan M, Wang S, Gao X (2013) A framework for scalable parameter estimation of gene circuit models using structural information. Bioinformatics 29: i98-i107. doi:10.1093/bioinformatics/btt232.
Publisher:
Oxford University Press
Journal:
Bioinformatics
Issue Date:
21-Jun-2013
DOI:
10.1093/bioinformatics/btt232
PubMed ID:
23813015
PubMed Central ID:
PMC3694671
Type:
Article
ISSN:
13674803
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKuwahara, Hiroyukien
dc.contributor.authorFan, Mingen
dc.contributor.authorWang, Suojinen
dc.contributor.authorGao, Xinen
dc.date.accessioned2014-08-27T09:51:23Z-
dc.date.available2014-08-27T09:51:23Z-
dc.date.issued2013-6-21en
dc.identifier.citationKuwahara H, Fan M, Wang S, Gao X (2013) A framework for scalable parameter estimation of gene circuit models using structural information. Bioinformatics 29: i98-i107. doi:10.1093/bioinformatics/btt232.en
dc.identifier.issn13674803en
dc.identifier.pmid23813015en
dc.identifier.doi10.1093/bioinformatics/btt232en
dc.identifier.urihttp://hdl.handle.net/10754/325439en
dc.description.abstractMotivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. The Author 2013.en
dc.language.isoenen
dc.publisherOxford University Pressen
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0en
dc.subjectbiological modelen
dc.subjectgene expression regulationen
dc.subjectgene regulatory networken
dc.subjectgeneticsen
dc.subjectmetabolismen
dc.subjectSaccharomyces cerevisiaeen
dc.subjectsystems biologyen
dc.subjectGene Expression Regulationen
dc.subjectGene Regulatory Networksen
dc.subjectModels, Geneticen
dc.subjectSaccharomyces cerevisiaeen
dc.subjectSystems Biologyen
dc.titleA framework for scalable parameter estimation of gene circuit models using structural informationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC3694671en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Statistics, Texas A and M University, College Station, TX 77843, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorKuwahara, Hiroyukien
kaust.authorFan, Mingen
kaust.authorGao, Xinen

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