Analysis of deterministic cyclic gen...
Ahsen, Mehmet Eren.

 

  • Analysis of deterministic cyclic gene regulatory network models with delays[electronic resource] /
  • 紀錄類型: 書目-語言資料,印刷品 : Monograph/item
    杜威分類號: 572.865015118
    書名/作者: Analysis of deterministic cyclic gene regulatory network models with delays/ by Mehmet Eren Ahsen, Hitay Ozbay, Silviu-Iulian Niculescu.
    作者: Ahsen, Mehmet Eren.
    其他作者: Ozbay, Hitay.
    出版者: Cham : : Springer International Publishing :, 2015.
    面頁冊數: xiii, 94 p. : : ill., digital ;; 24 cm.
    Contained By: Springer eBooks
    標題: Gene regulatory networks - Mathematical models.
    標題: Gene regulatory networks - Computer simulation.
    標題: Mathematics.
    標題: Systems Theory, Control.
    標題: Mathematical and Computational Biology.
    標題: Gene Expression.
    標題: Control, Robotics, Mechatronics.
    ISBN: 9783319156064 (electronic bk.)
    ISBN: 9783319156057 (paper)
    內容註: 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.
    摘要、提要註: 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.
    電子資源: http://dx.doi.org/10.1007/978-3-319-15606-4
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