LEADER 03230nam 2200421 450 001 9910824200403321 005 20230808210118.0 010 $a3-8325-9142-7 035 $a(CKB)4910000000017362 035 $a(MiAaPQ)EBC5850422 035 $a5a8e86f5-e14c-41aa-ad1a-66c5b0dd2d03 035 $a(EXLCZ)994910000000017362 100 $a20190911d2016 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIdentification, analysis and control of discrete and continuous models of gene regulation networks /$fvorgelegt von Christian Breindl aus Neumarkt i.d.OPf 210 1$aBerlin :$cLogos Verlag,$d[2016] 210 4$dİ2016 215 $a1 online resource (xiii, 125 pages) $cillustrations 300 $a"Von der Fakulta?t Konstruktions-, Produktions-, und Fabrzeugtechnik und dem Stuttgart Research Centre for Simulation Technology der Universita?t Stuttgart zur Erlangung der Wu?rde eines Doktors der Ingenieurwissenschaften (Dr. -Ing.) genehmigte Abhandlung." 300 $a"Institut fu?r Systemtheorie und Regelungstechnik, Universita?t Stuttgart." 311 $a3-8325-4283-3 320 $aIncludes bibliographical references (pages 117-125). 330 $aLong description: A systems biological approach towards cellular networks promises a better understanding of how these systems work. The development of mathematical models is however inherently complicated, as the involved molecules and their interactions are mostly difficult to measure. Focusing on gene regulation networks, this work therefore intends to provide systems theoretic tools that support the process of model development and analysis in presence of such incomplete knowledge. The contributions are threefold. First, the problem of identifying interconnections between genes from noisy data is addressed. Existing solutions formulated in a discrete framework are reviewed and simplified significantly with the help of tools from convex optimization theory. Second, a novel method for model verification and discrimination is introduced. It is based on concepts from robust control theory and allows to quantify the capability of a model to reproduce experimentally observed stationary behaviors. As the proposed formalism only requires a vague knowledge about the interactions between the molecules, the method is intended to test and compare early modeling hypotheses. Third, the problem of controlling gene regulation networks in presence of qualitative information only is studied. Methods from discrete event systems theory are adapted to obtain stimulation strategies that will steer the network toward a desired attractor. The benefits of all contributions are illustrated with examples in the individual chapters. 606 $aGene regulatory networks 615 0$aGene regulatory networks. 676 $a572.865 700 $aBreindl$b Christian$01630927 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824200403321 996 $aIdentification, analysis and control of discrete and continuous models of gene regulation networks$93969482 997 $aUNINA