Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study

Handle URI:
http://hdl.handle.net/10754/621726
Title:
Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study
Authors:
Fan, M.; Kuwahara, Hiroyuki; Wang, X.; Wang, S.; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Parameter estimation is a challenging computational problemin the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter esti- mation of gene circuitmodels fromsuch time-series mRNA data has become an importantmethod for quantitatively dissecting the regulation of gene expression. By focusing on themodeling of gene circuits, we examine here the perform- ance of three types of state-of-the-art parameter estimation methods: population-basedmethods, onlinemethods and model-decomposition-basedmethods. Our results show that certain population-basedmethods are able to generate high- quality parameter solutions. The performance of thesemethods, however, is heavily dependent on the size of the param- eter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, onlinemethods andmodel decomposition-basedmethods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fastmethods with local search as a subsequent refinement procedure can substantially increase the qual- ity of their parameter estimates to the level on par with the best solution obtained fromthe population-basedmethods whilemaintaining high computational speed. These suggest that such hybridmethods can be a promising alternative to themore commonly used population-basedmethods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatorymechanismsmakes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Fan M, Kuwahara H, Wang X, Wang S, Gao X (2015) Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study. Briefings in Bioinformatics 16: 987–999. Available: http://dx.doi.org/10.1093/bib/bbv015.
Publisher:
Oxford University Press (OUP)
Journal:
Briefings in Bioinformatics
Issue Date:
29-Mar-2015
DOI:
10.1093/bib/bbv015
Type:
Article
ISSN:
1467-5463; 1477-4054
Sponsors:
The research reported in this publication was supported by competitive research funding from King Abdullah University of Science and Technology (KAUST), the Natural Science Foundation of Zhejiang Province of China (LQ14F010011) and the National Natural Science Foundation of China (Grant No. 61401131).
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorFan, M.en
dc.contributor.authorKuwahara, Hiroyukien
dc.contributor.authorWang, X.en
dc.contributor.authorWang, S.en
dc.contributor.authorGao, Xinen
dc.date.accessioned2016-11-03T13:23:38Z-
dc.date.available2016-11-03T13:23:38Z-
dc.date.issued2015-03-29en
dc.identifier.citationFan M, Kuwahara H, Wang X, Wang S, Gao X (2015) Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study. Briefings in Bioinformatics 16: 987–999. Available: http://dx.doi.org/10.1093/bib/bbv015.en
dc.identifier.issn1467-5463en
dc.identifier.issn1477-4054en
dc.identifier.doi10.1093/bib/bbv015en
dc.identifier.urihttp://hdl.handle.net/10754/621726-
dc.description.abstractParameter estimation is a challenging computational problemin the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter esti- mation of gene circuitmodels fromsuch time-series mRNA data has become an importantmethod for quantitatively dissecting the regulation of gene expression. By focusing on themodeling of gene circuits, we examine here the perform- ance of three types of state-of-the-art parameter estimation methods: population-basedmethods, onlinemethods and model-decomposition-basedmethods. Our results show that certain population-basedmethods are able to generate high- quality parameter solutions. The performance of thesemethods, however, is heavily dependent on the size of the param- eter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, onlinemethods andmodel decomposition-basedmethods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fastmethods with local search as a subsequent refinement procedure can substantially increase the qual- ity of their parameter estimates to the level on par with the best solution obtained fromthe population-basedmethods whilemaintaining high computational speed. These suggest that such hybridmethods can be a promising alternative to themore commonly used population-basedmethods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatorymechanismsmakes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press.en
dc.description.sponsorshipThe research reported in this publication was supported by competitive research funding from King Abdullah University of Science and Technology (KAUST), the Natural Science Foundation of Zhejiang Province of China (LQ14F010011) and the National Natural Science Foundation of China (Grant No. 61401131).en
dc.publisherOxford University Press (OUP)en
dc.subjectComparative studyen
dc.subjectGene circuitsen
dc.subjectParameter estimationen
dc.subjectThermodynamic-based modelingen
dc.titleParameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative studyen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBriefings in Bioinformaticsen
dc.contributor.institutionCollege of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Chinaen
dc.contributor.institutionDepartment of Statistics, Texas A and M University, United Statesen
kaust.authorKuwahara, Hiroyukien
kaust.authorWang, X.en
kaust.authorGao, Xinen
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