Identifiable reparametrizations of linear compartment models
Identifiable reparametrizations of linear compartment models
复制标题
DOI：
10.1016/j.jsc.2013.11.002
复制
发表时间：
2014-05-01
影响因子：
0.7
通讯作者：
Sullivant, Seth
中科院分区：
文献类型：
--
作者：
Meshkat, Nicolette;Sullivant, Seth
结构可辨识性是指从给定的输入输出数据中找出模型的哪些未知参数可以量化。在系统生物学和药物动力学中使用的许多线性常微分方程模型是不可识别的，这意味着参数可以具有无穷多的值，但仍然产生相同的输入输出数据。我们利用交换代数和图论研究了一类特殊的不可辨识模型，并找到了获得这些模型的可辨识尺度重参数的条件。我们的主要结果是，可识别的标度重参数的存在等价于单项函数的标度重参数的存在。我们提供了一个算法，当它们存在时，寻找这些重参数，并且部分结果开始对具有可识别的比例重参数的图进行分类。(C)2013爱思唯尔B.V.保留所有权利。
Structural identifiability concerns finding which unknown parameters of a model can be quantified from given input output data. Many linear ODE models, used in systems biology and pharmacokinetics, are unidentifiable, which means that parameters can take on an infinite number of values and yet yield the same input output data. We use commutative algebra and graph theory to study a particular class of unidentifiable models and find conditions to obtain identifiable scaling reparametrizations of these models. Our main result is that the existence of an identifiable scaling reparametrization is equivalent to the existence of a scaling reparametrization by monomial functions. We provide an algorithm for finding these reparametrizations when they exist and partial results beginning to classify graphs which possess an identifiable scaling reparametrization. (C) 2013 Elsevier B.V. All rights reserved.