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病毒繁殖周期的随机建模:研究病毒和细胞灭绝

Rahnuma Islam¹, David Swigon²

¹Department of Mathematics, University of Pittsburgh, Thackeray Hall, Pittsburgh, 15213, PA, USA. rai31@pitt.edu.

Bulletin of mathematical biology

|October 30, 2025

概括

随机模型提供了比传统微分方程更好的方法来理解病毒持久性和宿主细胞存活率. 一种新的修改后的随机微分方程方法提高了模拟速度,同时保持了病毒灭绝研究的准确性.

科学领域:

病毒学病毒学
数学生物学数学生物学
计算生物学计算生物学

背景情况:

传统的病毒动态建模依赖于普通微分方程,这些方程可能无法完全捕捉病毒的持久性和宿主细胞的存活率.
随机模型为探索病毒灭绝和宿主细胞命运等现象提供了更自然的框架.

研究的目的:

通过两种病毒繁殖的随机模型来研究病毒和基质细胞灭绝.
分析灭绝动态对不同病毒释放机制 (爆发与芽) 病毒生产率的依赖性.
为了比较这些随机模型的不同模拟方法的准确性和效率.

主要方法:

开发了两种简单的病毒繁殖随机模型,区分病毒爆发 (即时释放) 和病毒芽 (逐渐释放).
模拟模型的连续时间马尔科夫链 (CTMC) 版本,以获得准确的结果.
评估了传统的扩散近似方法.
提出并测试了一种修改后的随机微分方程 (SDE) 方法.

主要成果:

对于两种病毒释放机制,CTMC模拟提供了最准确的结果,但在计算上是密集的.
扩散近似方法在灭绝概率和平均灭绝时间方面表现出显著的差异.
修改后的SDE方法显著提高了模拟速度,同时保持了准确性.

结论:

随机模型对于准确建模病毒灭绝和宿主细胞生存至关重要.
修改后的SDE方法为模拟随机病毒动态提供了一个计算效率高,准确的替代方案.
这种改进的模拟技术可以促进我们对病毒持久性的理解,并为治疗策略提供信息.

关键词:

连续时间的马尔科夫链.跳跃扩散过程的过程.随机微分方程随机微分方程随机微分方程随机模型的建模病毒的繁殖病毒的繁殖

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