[论文解读] Stochastic model of virus and defective interfering particle spread across mammalian cells with immune response

Neil R. Clark, Karla Tapia+4 more|arXiv (Cornell University)|Aug 24, 2011

Mathematical and Theoretical Epidemiology and Ecology Models被引用 4

一句话总结

本研究开发了一种随机性代理模型，用于模拟在哺乳动物细胞单层中塞尼病毒（Sendai virus）和缺陷干扰颗粒（DIPs）的传播，并通过干扰素介导先天免疫反应。研究发现，DIPs通过随机效应延迟了病灶的生长，而干扰素则使生长速度减缓至类似对数的动能，提示DIPs可能在调节病毒传播以增强传播机会方面具有潜在的进化作用。

ABSTRACT

Much of the work on modeling the spread of viral infections utilized partial differential equa- tions. Traveling-wave solutions to these PDEs are typically concentrated on velocities and their dependence on the various parameters. Most of the investigations into the dynamical interaction of virus and defective interfering particles (DIP), which are incomplete forms of the virus that replicate through co-infection, have followed the same lines. In this work we present an agent based model of viral infection with consideration of DIP and the negative feedback loop introduced by interferon production as part of the host innate immune response. The model is based high resolution microscopic images of plaques of dead cells we took from mammalian cells infected with Sendai virus with low and high DIP. In order to investigate the effects of the discrete stochastic microscopic mechanisms, which are responsible for virus spreading, have on the macroscopic growth of viral plaques, we generate an agent-based model of viral infection. The two main aims of this work are to (i) investigate the effects of discrete microscopic randomness on the macroscopic growth of viral plaques; and (ii) examine the dynamic interactions between the full length virus, DIP and interferon, and interpret what may be the function of DIP. We find that we can explain the qualitative differences between our stochastic model and deterministic models in terms of the fractal geometry of the resulting plaques, and that DIP have a delaying effect while the interaction between interferon and DIP has a slowing effect on the growth of viral plaques, potentially contributing to viral latency.

研究动机与目标

• 研究病毒传播中的离散随机机制如何导致与确定性PDE模型相比的宏观病灶生长差异。
• 考察全长病毒、DIPs与干扰素介导的免疫反应之间的动态相互作用，以揭示其对病灶形态与生长动力学的影响。
• 探讨DIPs是否可能通过其内在生物学特性调节病毒传播，从而在病毒进化中发挥潜在作用。
• 模拟干扰素分泌与衰减速率对病灶发育的影响，特别是其对生长曲线的非线性效应。

提出的方法

• 基于2D网格（代表哺乳动物细胞单层）的代理模型，模拟单个细胞、病毒颗粒、DIPs及干扰素分子的行为。
• 细胞感染、病毒复制、细胞死亡及病毒释放被建模为基于高分辨率显微镜观测塞尼病毒病灶实验参数的离散随机事件。
• 检测到DIPs后触发干扰素产生，通过反馈回路降低邻近未感染细胞的感染概率。
• 模型包含游离病毒与DIP颗粒的空间扩散，感染概率取决于局部颗粒浓度与干扰素水平。
• 通过时间序列分析感染细胞数量、病灶周长与分形维数来量化病灶生长，采用幂律拟合评估生长指数。
• 参数扫描改变DIP浓度、干扰素分泌速率、衰减速率及保护效应，以评估其对生长动力学的影响。

实验结果

研究问题

• RQ1 细胞水平的离散随机相互作用如何导致与确定性PDE模型相比的病毒病灶生长宏观差异？
• RQ2 DIP浓度对病毒病灶发育的时间与形态有何影响？
• RQ3 干扰素介导的免疫反应如何改变病毒病灶的生长动力学？
• RQ4 观察到的病灶延迟与形态变化是否可仅由DIPs与干扰素的已知生物学特性解释？
• RQ5 DIPs在病毒通过调节传播速度而实现的进化策略中可能扮演何种角色？

主要发现

• 代理模型产生的病灶生长速度在时间上快于二次方，与确定性PDE模型通常假设的行波解形成对比。
• DIPs显著延迟病灶生长，30% DIP含量使病灶大小在相同时间段内相比5% DIP减少约两倍。
• 病灶的分形维数随DIP浓度升高而增加，而生长的幂律指数降低，表明其对生长速率的净效应存在复杂相互作用。
• 干扰素反应将生长曲线从幂律形式转变为指数单调递减的形式，类似于对数增长，表明具有强烈的减速效应。
• 在特定干扰素参数下出现双相生长曲线，初始阶段的凹性随后在干扰素饱和后恢复为幂律行为。
• 模型表明，DIPs可能作为病毒传播的动态调节器，通过防止宿主细胞过早死亡，潜在促进病毒潜伏与传播成功率。