卡普托分数阶基因调控网络的正向性和稳定性:系统比较法

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Intelligent Systems Pub Date : 2024-06-17 DOI:10.1155/2024/4790696
Cong Wu
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引用次数: 0

摘要

众所周知,正向性是研究基因调控网络的一个重要课题,因为所涉及的变量(如 mRNA 和蛋白质的浓度)永远不会是负值。然而,由于卡普托分数导数(CFD)的非局部性,卡普托分数阶模型的正向性一直是个老大难问题。本文提出了系统比较法,证明了卡普托分数阶基因调控网络(CFOGRNs)仅在正初始条件下的实在性。此外,我们还发现,正向性结果可使给出适当的 CFOGRNs 比较系统成为可行,其中的上估计值和下估计值可用于保证目标 CFOGRNs 的稳定性。因此,这里也提供了 CFOGRNs 稳定性的系统比较方法。与现有的 Lyapunov 直接法相比,所提出的系统比较法提供了另一种稳定性分析方法,并对稳定性条件提出了不同的见解。最后,这些理论推导将通过一个数值模拟实例进行说明和验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Positivity and Stability of Caputo Fractional Order Gene Regulatory Networks: The System Comparison Method

As well known, the positivity is an essential topic when studying gene regulatory networks since the variables involved, e.g., the concentrations of mRNA and proteins, can never be negative. However, the positivity of Caputo fractional order models has been a longstanding problem due to the nonlocality of Caputo fractional derivatives (CFD). In this paper, we present the system comparison method to prove the positivity of Caputo fractional order gene regulatory networks (CFOGRNs) only under positive initial conditions. Moreover, it is found that the positivity results can make it feasible to give proper comparison systems for CFOGRNs, in which the upper and lower estimations can be used to guarantee the stability of the objective CFOGRNs. Thus, the system comparison method for the stability of CFOGRNs is also provided here. Compared to the existing Lyapunov direct method, the proposed system comparison method affords an alternative method for stability analysis and different insights in stability conditions. Finally, these theoretical derivations are illustrated and validated by an example with numerical simulations.

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来源期刊
International Journal of Intelligent Systems
International Journal of Intelligent Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
14.30%
发文量
304
审稿时长
9 months
期刊介绍: The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.
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