Optimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation

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dc.contributor.authorWeber, G. -W.
dc.contributor.authorTaylan, P.
dc.contributor.authorAlparslan-Gok, S. Z.
dc.contributor.authorOezoeguer-Akyuz, S.
dc.contributor.authorAkteke-Ozturk, B.
dc.date.accessioned2024-04-24T16:02:33Z
dc.date.available2024-04-24T16:02:33Z
dc.date.issued2008
dc.departmentDicle Üniversitesien_US
dc.description.abstractThis mathematical contribution is addressed towards the wide interface of life and human sciences that exists between biological and environmental information. Like very few other disciplines only, the modeling and prediction of genetical data is requesting mathematics nowadays to deeply understand its foundations. This need is even forced by the rapid changes in a world of globalization. Such a study has to include aspects of stability and tractability; the still existing limitations of modern technology in terms of measurement errors and uncertainty have to be taken into account. In this paper, the important role played by the environment is rigorously introduced into the biological context and connected with employing the theories of optimization and dynamical systems. Especially, a matrix-vector and interval concept and algebra are used; some special attention is paid to splines. From data got by DNA microarray experiments and environmental measurements we extract nonlinear ordinary differential equations. This is done by Chebychev approximation and semi-infinite optimization. Then, time-discretized dynamical systems are studied. By a combinatorial algorithm which constructs and follows polyhedra sequences, the region of parametric stability is detected. This is used for testing and maybe improving the goodness of the achieved model. We analyze the topological landscape of gene-environment networks in terms of structural stability which we characterize. This pioneering practically motivated and theoretically elaborated work is devoted to a contribution to better health care, progress in medicine, better education, and to recommending more healthy living conditions. The present paper mainly bases on the authors' and their coauthors' contributions of the last few years, it critically discusses structural frontiers and future challenges, while respecting related research contributions, giving access and referring to alternative concepts that exist in the literature.en_US
dc.identifier.doi10.1007/s11750-008-0052-5
dc.identifier.endpage318en_US
dc.identifier.issn1134-5764
dc.identifier.issn1863-8279
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-56249086455
dc.identifier.scopusqualityQ1
dc.identifier.startpage284en_US
dc.identifier.urihttps://doi.org/10.1007/s11750-008-0052-5
dc.identifier.urihttps://hdl.handle.net/11468/14833
dc.identifier.volume16en_US
dc.identifier.wosWOS:000260771100010
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofTop
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputational Biologyen_US
dc.subjectChebychev Approximationen_US
dc.subjectGeneralized Semi-Infinite Programmingen_US
dc.subjectDna Microarray Experimenten_US
dc.subjectEnvironmenten_US
dc.subjectMeasurement Errorsen_US
dc.subjectUncertaintyen_US
dc.subjectModelingen_US
dc.subjectDynamical Systemen_US
dc.subjectInterval And Matrix Algebraen_US
dc.subjectStructural Stabilityen_US
dc.subjectRegressionen_US
dc.subjectSplinesen_US
dc.subjectConic (Quadratic) Programmingen_US
dc.subject93a30en_US
dc.subject92d10en_US
dc.subject90c34en_US
dc.titleOptimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximationen_US
dc.titleOptimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation
dc.typeArticleen_US

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