Acute or Chronic? Within-Host Models with Immune Dynamics, Infection Outcome, and Parasite Evolution
Acute or Chronic? Within-Host Models with Immune Dynamics, Infection Outcome, and Parasite Evolution
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DOI：
10.1086/592404
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发表时间：
2008-12-01
影响因子：
2.9
通讯作者：
van Baalen, Minus
中科院分区：
文献类型：
--
作者：
Alizon, Samuel;van Baalen, Minus
有充分的理论和实验证据表明，毒力的演变取决于宿主的免疫反应。在这篇文章中，我们回顾了一些最近的研究，试图明确纳入免疫系统的动态（而不是仅仅代表它由一个单一的黑盒参数）的寄生虫毒力的演变模型。一个引人注目的观察是，感染的类型（急性或慢性）总是被认为是一个约束，模型假设必须满足，而不是作为一个潜在的结果的相互作用的寄生虫与宿主的免疫系统。我们认为，避免对感染类型做出假设将有助于更好地了解传染病，尽管仍然存在一些基本和技术问题。免疫系统的动态建模开辟了广泛的视角：了解免疫系统如何根除寄生虫（它对大多数病原体都有效，但不是对所有病原体都有效，HIV是一个臭名昭著的不能完全消除的病毒的例子），通过伴随免疫研究多种感染，了解动物免疫系统的出现和进化，和一般的进化流行病学（e.例如，在一个实施例中，预测新疗法和公共卫生政策的进化后果）。最后，我们讨论了基于嵌入式（或嵌套式）模型的新方法，并确定了传染病建模的未来前景。
There is ample theoretical and experimental evidence that virulence evolution depends on the immune response of the host. In this article, we review a number of recent studies that attempt to explicitly incorporate the dynamics of the immune system (instead of merely representing it by a single black box parameter) in models for the evolution of parasite virulence. A striking observation is that the type of infection (acute or chronic) is invariably considered to be a constraint that model assumptions have to satisfy rather than as a potential outcome of the interaction of the parasite with its host's immune system. We argue that avoiding making assumptions about the type of infection will lead to a better understanding of infectious diseases, even though a number of fundamental and technical problems remain. Dynamical modeling of the immune system opens a wide range of perspectives: for understanding how the immune system eradicates a parasite (which it does for most pathogens but not for all, HIV being a notorious example of a virus that is not completely eliminated), for studying multiple infections through concomitant immunity, for understanding the emergence and evolution of the immune system in animals, and for evolutionary epidemiology in general (e. g., predicting evolutionary consequences of new therapies and public health policies). We conclude by discussing new approaches based on embedded (or nested) models and identify future perspectives for the modeling of infectious diseases.