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Identification, analysis and control of discrete and continuous models of gene regulation networks /

Long 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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Breindl, Christian (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Alemán
Publicado: Berlin : Logos Verlag Berlin GmbH, [2016]
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Breindl, Christian,  |e author. 
245 1 0 |a Identification, analysis and control of discrete and continuous models of gene regulation networks /  |c vorgelegt von Christian Breindl aus Neumarkt i.d. OPf. 
264 1 |a Berlin :  |b Logos Verlag Berlin GmbH,  |c [2016] 
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505 0 |a Intro; 1 Introduction; 1.1 Motivation; 1.2 Contributions and outline of the thesis; 2 Background; 2.1 Biological fundamentals of gene regulation; 2.2 Mathematical modeling of gene regulation networks; 2.3 Measurement techniques; 3 Identification of Boolean or discrete models of gene regulation networks; 3.1 Introduction and problem statement; 3.2 Polynomial representation of discrete functions; 3.3 Reformulation of the identification problem as a linear program; 3.4 Reduced order representation and robust estimation of unate Boolean functions; 3.5 Application examples 
505 8 |a 3.6 Summary and discussion4 Analysis of multistability and multistability robustness; 4.1 Introduction and problem statement; 4.2 Modeling framework and preliminaries; 4.3 A combinatorial approach for the validation of multistability in gene regulation networks; 4.4 Steady state robustness analysis and model discrimination; 4.5 Application examples; 4.6 Summary and discussion; 5 Control of Boolean models of gene regulation networks; 5.1 Introduction and problem statement; 5.2 Representation of Boolean networks as a discrete event system; 5.3 Application example; 5.4 Summary and discussion 
505 8 |a 6 Conclusion6.1 Summary; 6.2 Outlook; Appendix 
504 |a Includes bibliographical references (pages 117-125). 
546 |a In English. Some information on title page, title page verso, and part of introduction, in German. 
520 |a Long 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. 
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650 0 |a Gene regulatory networks. 
650 2 |a Gene Regulatory Networks 
650 6 |a Réseaux de régulation génique. 
650 7 |a Gene regulatory networks  |2 fast 
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