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Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays

This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then rev...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Ahsen, Mehmet Eren (Autor), Özbay, Hitay (Autor), Niculescu, Silviu-Iulian (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Birkhäuser, 2015.
Edición:1st ed. 2015.
Colección:SpringerBriefs in Control, Automation and Robotics,
Temas:
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays  |h [electronic resource] /  |c by Mehmet Eren Ahsen, Hitay Özbay, Silviu-Iulian Niculescu. 
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264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Birkhäuser,  |c 2015. 
300 |a XIII, 94 p. 15 illus. in color.  |b online resource. 
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505 0 |a Preface -- Introduction -- Basic Tools from Systems and Control Theory -- Functions with Negative Schwarzian Derivatives -- Deterministic ODE-Based Model with Time Delay -- Gene Regulatory Networks under Negative Feedback -- Gene Regulatory Networks under Positive Feedback -- Summary and Concluding Remarks -- References. 
520 |a This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs. 
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