SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology
SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology
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DOI：
10.1093/bioinformatics/btaa786
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发表时间：
2021-05-01
期刊：
影响因子：
5.8
通讯作者：
Kschischo, Maik
中科院分区：
文献类型：
--
作者：
Newmiwaka, Tobias;Engelhardt, Benjamin;Kschischo, Maik
用常微分方程表述的动态模型可以提供生物系统中机制和因果相互作用的信息，以指导有针对性的干预和设计进一步的实验。关于结构、功能形式和相互作用参数的不准确知识是机械建模的主要障碍。另一个挑战是生物系统的开放性，它接受来自环境的未知输入。R-package SEEDS实现了两种最近开发的算法，可以从输出测量中推断结构模型误差和未知输入。在初始模型与数据不匹配的情况下，这些信息可以促进有效的模型重新校准以及实验设计。
Dynamic models formulated as ordinary differential equations can provide information about the mechanistic and causal interactions in biological systems to guide targeted interventions and to design further experiments. Inaccurate knowledge about the structure, functional form and parameters of interactions is a major obstacle to mechanistic modeling. A further challenge is the open nature of biological systems which receive unknown inputs from their environment. The R-package SEEDS implements two recently developed algorithms to infer structural model errors and unknown inputs from output measurements. This information can facilitate efficient model recalibration as well as experimental design in the case of misfits between the initial model and data.