Identifiability Results for Several Classes of Linear Compartment Models
Identifiability Results for Several Classes of Linear Compartment Models
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DOI：
10.1007/s11538-015-0098-0
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发表时间：
2015-08-01
影响因子：
3.5
通讯作者：
Eisenberg, Marisa
中科院分区：
文献类型：
--
作者：
Meshkat, Nicolette;Sullivant, Seth;Eisenberg, Marisa
可识别性涉及找到可以根据给定的输入输出数据唯一地或以其他方式估计模型的哪些未知参数。如果给定输入输出数据无法确定模型参数的某些子集，那么我们就说该模型是不可识别的。在这项工作中，我们研究了线性室模型，这是药代动力学、生理学和生态学中常用的一类生物模型。在过去的工作中，我们使用交换代数和图论来识别一类线性隔室模型，我们称之为可识别循环模型，它们是不可识别的，但具有最简单的可能可识别函数（所谓的单项式循环）。在这里，我们展示如何通过添加输入、添加输出或消除泄漏来修改可识别的循环模型，从而获得可识别的模型。我们还证明了如何将可识别模型（每个模型对应于强连接图）组合成更大的可识别模型的建设性结果。我们将这些理论结果应用到生理学、细胞生物学和生态学的几个现实世界的生物模型中。
Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.