Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
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DOI：
10.1049/iet-syb.2015.0034
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发表时间：
2016-02
影响因子：
2.3
通讯作者：
Medvedovic M
中科院分区：
文献类型：
--
作者：
Sun X;Medvedovic M
Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, we take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, we decompose the whole dynamic model into subset network modules, based on different modules, we apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, we apply it to synthetic data generated from Repressilator model and experimental data of the JAK-STAT Pathway, but this method can be easily extended to large scale cases.
