具有时间延迟的变量级分数酶动力学模型的分析
K Agilan1, S Naveen1, S Suganya1
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamilnadu, 600127, India.
Scientific reports
|October 1, 2025
概括
这项研究引入了一个新的酶动力学模型,使用可变顺序的卡普托微分衍生物和时间延迟. 这种方法通过捕捉记忆效应来增强对生物催化过程的理解.
科学领域:
- 生物化学和化学动力学
- 生物系统中的数学建模
背景情况:
- 酶动力学对于优化生物技术和制药过程至关重要.
- 传统模型可能无法完全捕捉酶反应中的记忆效应或非局部行为.
- 过去的系统状态可以显著影响反应动态.
研究的目的:
- 开发一种精细的酶动力学模型,其中包含变量级的卡普托微分衍生物和时间延迟.
- 为了准确地捕捉酶反应中的记忆效应和非局部行为.
- 改进生物催化过程的表征.
主要方法:
- 使用变量级卡普托微分衍生物来建模酶动力学.
- 整合恒定时间延迟来表示记忆效应.
- 应用固定点理论来确定解决方案的存在和独特性.
- 使用Ulam-Hyers和一般化的Ulam-Hyers概念分析模型稳定性.
- 采用强大的数值方法来探索模型动态.
主要成果:
- 确定了拟议的分数模型的解决方案的存在和独特性.
- 对模型的稳定性进行了严格的分析.
- 数字模拟证明了变量顺序的卡普托分数导数与时间延迟的意义.
- 该模型有效地捕捉了由内存和非局部属性影响的复杂动态.
结论:
- 拟议的可变顺序分数酶动力学模型与时间延迟提供了更精确的生物催化过程的特征.
- 这种先进的模型通过考虑记忆效应来改进传统的酶动力学.
- 这些发现对优化生物技术,制药和食品行业的流程有意义.
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