吸入性炭疽的宿主内机制数学模型。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-24 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013439
Bevelynn Whaler, Grant Lythe, Joseph J Gillard, Thomas R Laws, Jonathan Carruthers, Thomas Finnie, Carmen Molina-París, Martín López-García
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引用次数: 0

摘要

我们提出了炭疽芽孢杆菌在淋巴结和宿主血液中的动力学数学模型,吸入初始剂量的孢子。我们还纳入了保护性抗原的动力学,这是由细菌产生的炭疽毒素的结合成分。该模型提供了吸入性炭疽早期感染动力学的机制描述,而其随机性使我们能够研究不同结果的概率(例如,吸入一定剂量的孢子后,感染被清除的可能性有多大),以便解释吸入性炭疽的剂量-反应数据。该模型通过贝叶斯方法进行校准,使用来自新西兰白兔和豚鼠感染研究的体内数据,从而能够估计宿主内参数。我们还利用1979年斯维尔德洛夫斯克炭疽病爆发的潜伏期数据,表明该模型可以在合理的参数制度下准确描述人类到症状的时间数据。最后,假设吸入孢子的数量具有泊松分布,我们推导出在时间t之前症状发作概率的简单近似公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mechanistic within-host mathematical model of inhalational anthrax.

We present a mathematical model of the dynamics of Bacillus anthracis bacteria within the lymph nodes and blood of a host, following inhalation of an initial dose of spores. We also incorporate the dynamics of protective antigen, which is the binding component of the anthrax toxin produced by the bacteria. The model offers a mechanistic description of the early infection dynamics of inhalational anthrax, while its stochastic nature allows us to study the probabilities of different outcomes (for example, how likely it is that the infection will be cleared for a given inhaled dose of spores) in order to explain dose-response data for inhalational anthrax. The model is calibrated via a Bayesian approach, using in vivo data from New Zealand white rabbit and guinea pig infection studies, enabling within-host parameters to be estimated. We also leverage incubation-period data from the Sverdlovsk 1979 anthrax outbreak to show that the model can accurately describe human time-to-symptoms data under reasonable parameter regimes. Finally, we derive a simple approximate formula for the probability of symptom onset before time t, assuming that the number of inhaled spores has a Poisson distribution.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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