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Identification of biochemical networks by S-tree based genetic programming

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dc.contributor.authorCho, D. Y.-
dc.contributor.authorCho, K. H.-
dc.contributor.authorZhang, B. T.-
dc.date.accessioned2009-12-24T11:08:31Z-
dc.date.available2009-12-24T11:08:31Z-
dc.date.issued2006-04-06-
dc.identifier.citationBioinformatics. 2006 Jul 1;22(13):1631-40. Epub 2006 Apr 3.en
dc.identifier.issn1367-4803 (Print)-
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16585066-
dc.identifier.urihttps://hdl.handle.net/10371/22595-
dc.description.abstractMOTIVATION: Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. RESULTS: We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are <5% regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within +/-10% noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10(-2). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation.en
dc.language.isoen-
dc.publisherOxford University Pressen
dc.subjectAlgorithmsen
dc.subjectBiochemistry/*methodsen
dc.subjectComputational Biology/*methodsen
dc.subjectComputer Simulationen
dc.subjectDNA Repairen
dc.subjectEscherichia coli/genetics/metabolismen
dc.subjectFermentationen
dc.subjectGene Expression Profilingen
dc.subjectGenes, Bacterialen
dc.subjectGenes, Fungalen
dc.subjectModels, Statisticalen
dc.subjectSystems Biologyen
dc.subjectModels, Genetic-
dc.titleIdentification of biochemical networks by S-tree based genetic programmingen
dc.typeArticleen
dc.contributor.AlternativeAuthor조동연-
dc.contributor.AlternativeAuthor조광현-
dc.contributor.AlternativeAuthor장병탁-
dc.identifier.doi10.1093/bioinformatics/btl122-
Appears in Collections:
College of Medicine/School of Medicine (의과대학/대학원)Immunology (면역학전공)Journal Papers (저널논문_면역학전공)
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