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在患有多发性硬化症的患者中,残疾进展的数学建模

Georgi Bazlyankov¹, Tihomir Ivanov²

¹Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Sofia, Bulgaria. gbazljanko@fmi.uni-sofia.bg.

Acta biotheoretica

|May 21, 2025

概括

数学模型通过模拟疾病动态来增强对多发性硬化症 (MS) 的理解. 本研究引入了确定性和随机模型,以更好地代表多发性硬化症的进展和复发.

科学领域:

计算生物学计算生物学
数学流行病学数学流行病学
神经免疫学神经免疫学

背景情况:

多发性硬化症 (MS) 是一种复杂的神经系统疾病,其机制尚不清楚.
现有的模型可能无法完全捕捉MS的异质性和不规则的疾病过程.

研究的目的:

开发和完善用于模拟多发性硬化症 (MS) 进展的数学模型.
在模型中结合有益和有害的细胞代理,影响神经系统.
创建能够复制观察到的临床数据和疾病变异性的模型.

主要方法:

主要使用微分方程来制定数学模型.
包括修改来模拟不同类型的MS及其行为.
开发了决定性和随机的建模方法.
利用周期函数来表示有益和有害细胞度的动态.

主要成果:

提出的模型模拟了在各种形式的MS中观察到的典型的定性行为.
随机模型为MS中复发和缓解的不规则发生提供了更好的表现.
对模型属性的分析揭示了参数,初始条件和模拟疾病结果之间的联系.
模型能够生成与临床数据一致的定量疾病过程模拟.

结论:

数学建模,特别是随机元素,为了解MS等复杂疾病提供了强大的工具.
开发的模型可以模拟疾病的进展和变异性,有助于研究多发性硬化症的发病因子.
这些模型提供了一个定量框架,用于将模拟结果与MS研究中的真实世界临床观察结果进行比较.

关键词:

计算神经科学是一种计算神经科学.数学的建模数学建模多发性硬化症是多发性硬化症.数字实验数字实验雷米林的产生随机模型随机模型随机模型

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