Download Analysis of Deterministic Cyclic Gene Regulatory Network by Mehmet Eren Ahsen, Hitay Özbay, Silviu-Iulian Niculescu PDF

By Mehmet Eren Ahsen, Hitay Özbay, Silviu-Iulian Niculescu

ISBN-10: 3319156055

ISBN-13: 9783319156057

This short examines a deterministic, ODE-based version for gene regulatory networks (GRN) that comes with nonlinearities and time-delayed suggestions. An introductory bankruptcy offers a few insights into molecular biology and GRNs. The mathematical instruments priceless for learning the GRN version are then reviewed, specifically Hill capabilities and Schwarzian derivatives. One bankruptcy is dedicated to the research of GRNs lower than destructive suggestions with time delays and a unique case of a homogenous GRN is taken into account. Asymptotic balance research of GRNs lower than confident suggestions is then thought of in a separate bankruptcy, during which stipulations resulting in bi-stability are derived. Graduate and complicated undergraduate scholars and researchers up to speed engineering, utilized arithmetic, platforms biology and artificial biology will locate this short to be a transparent and concise advent to the modeling and research of GRNs.

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Extra resources for Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays

Example text

Also, the assumptions made in this section are standard in biochemistry. An interested reader may refer to a popular biochemistry book such as [46]. The repressilator is a synthetic genetic regulatory network first suggested in [47], where the authors used three transcriptional repressors in cascade to build an oscillating network in Escherichia coli. The synthetic network periodically induces the synthesis of green fluorescent protein. In this section, we first derive the dynamics of a 1-repressilator from mass action law.

X/ > 0 which is a contradiction. 5) In other words, it was shown that h0 cannot have positive local minima, so f 0 cannot have negative local maxima. u t Let us now calculate Schwarzian derivatives of some functions which are commonly used as nonlinearities in the modeling of physical systems. 3. 1. e ax 5a2 : 2 /D In real-life problems, we commonly encounter Hill function type nonlinearities. 0; 1/. x/. x/ D 2b 2 < 0: As a corollary of the above, we have the following result. 1. Let a, b > 0, c 0 and m 2 N be constants.

X2 / for all x 2 I . x/, 8x 2 I: Hence, by definition, x1 is a positive local minima of the function h0 . x/ < 0, h0 cannot have a positive local minima. 0; 1/. 18) D which implies that the point y is a positive local maxima of the function h0 . x/ is decreasing in some interval Œa; b then it must be decreasing in Œb; 1. x/ is a decreasing function. 1, the result below is obtained. 2. 0; 1/ Then h0 is a function from RC to Y  RC satisfying one of the following properties: 1. h0 is a strictly increasing function on Œ0; 1/.

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