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An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infections in vivo
Journal of The Royal Society Interface ( IF 3.7 ) Pub Date : 2020-12-01 , DOI: 10.1098/rsif.2020.0717
Myrto Vlazaki 1 , David J Price 2, 3 , Olivier Restif 1
Affiliation  

The management of bacterial diseases calls for a detailed knowledge about the dynamic changes in host–bacteria interactions. Biological insights are gained by integrating experimental data with mechanistic mathematical models to infer experimentally unobservable quantities. This inter-disciplinary field would benefit from experiments with maximal information content yielding high-precision inference. Here, we present a computationally efficient tool for optimizing experimental design in terms of parameter inference in studies using isogenic-tagged strains. We study the effect of three experimental design factors: number of biological replicates, sampling timepoint selection and number of copies per tagged strain. We conduct a simulation study to establish the relationship between our optimality criterion and the size of parameter estimate confidence intervals, and showcase its application in a range of biological scenarios reflecting different dynamics patterns observed in experimental infections. We show that in low-variance systems with low killing and replication rates, predicting high-precision experimental designs is consistently achieved; higher replicate sizes and strategic timepoint selection yield more precise estimates. Finally, we address the question of resource allocation under constraints; given a fixed number of host animals and a constraint on total inoculum size per host, infections with fewer strains at higher copies per strain lead to higher-precision inference.

中文翻译:

一种优化体内细菌感染数据驱动数学模型推理精度的实验设计工具

细菌性疾病的管理需要详细了解宿主-细菌相互作用的动态变化。通过将实验数据与机械数学模型相结合以推断实验上不可观察的量,可以获得生物学见解。这个跨学科领域将受益于最大信息内容的实验,产生高精度推理。在这里,我们提出了一种计算上有效的工具,用于在使用等基因标记菌株的研究中根据参数推断优化实验设计。我们研究了三个实验设计因素的影响:生物学重复数、采样时间点选择和每个标记菌株的拷贝数。我们进行了一项模拟研究,以建立我们的最优标准与参数估计置信区间大小之间的关系,并展示其在一系列反映实验感染中观察到的不同动态模式的生物场景中的应用。我们表明,在具有低杀死率和低复制率的低方差系统中,可以始终如一地实现预测高精度的实验设计;更高的重复大小和战略时间点选择产生更精确的估计。最后,我们解决了约束条件下的资源分配问题;给定固定数量的宿主动物和对每个宿主总接种量的限制,在每株较高拷贝数下感染较少菌株会导致更精确的推断。并展示其在一系列生物场景中的应用,反映了在实验感染中观察到的不同动态模式。我们表明,在具有低杀死率和低复制率的低方差系统中,可以始终如一地实现预测高精度的实验设计;更高的重复大小和战略时间点选择产生更精确的估计。最后,我们解决了约束条件下的资源分配问题;给定固定数量的宿主动物和对每个宿主总接种量的限制,在每株较高拷贝数下感染较少菌株会导致更精确的推断。并展示其在一系列生物场景中的应用,反映了在实验感染中观察到的不同动态模式。我们表明,在具有低杀死率和低复制率的低方差系统中,可以始终如一地实现预测高精度的实验设计;更高的重复大小和战略时间点选择产生更精确的估计。最后,我们解决了约束条件下的资源分配问题;给定固定数量的宿主动物和对每个宿主总接种量的限制,在每株较高拷贝数下感染较少菌株会导致更精确的推断。更高的重复大小和战略时间点选择产生更精确的估计。最后,我们解决了约束条件下的资源分配问题;给定固定数量的宿主动物和对每个宿主总接种量的限制,在每株较高拷贝数下感染较少菌株会导致更精确的推断。更高的重复大小和战略时间点选择产生更精确的估计。最后,我们解决了约束条件下的资源分配问题;给定固定数量的宿主动物和对每个宿主总接种量的限制,在每株较高拷贝数下感染较少菌株会导致更精确的推断。
更新日期:2020-12-01
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