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Record Nr. |
UNINA9910299699303321 |
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Autore |
Ahsen Mehmet Eren |
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Titolo |
Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays / / by Mehmet Eren Ahsen, Hitay Özbay, Silviu-Iulian Niculescu |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (104 p.) |
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Collana |
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SpringerBriefs in Control, Automation and Robotics, , 2192-6786 |
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Disciplina |
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Soggetti |
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System theory |
Biomathematics |
Gene expression |
Automatic control |
Robotics |
Mechatronics |
Systems Theory, Control |
Mathematical and Computational Biology |
Gene Expression |
Control, Robotics, Mechatronics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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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