Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks

Xiaodian Sun; Mario Medvedovic

Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks

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Author(s): Xiaodian Sun ¹¹ and Mario Medvedovic ¹¹
- Affiliations: 1: Department of Environmental Health, Laboratory for Statistical Genomics and Systems Biology, University of Cincinnati College of Medicine, 3223 Eden Ave. ML56, Cincinnati, OH 45267-0056, USA
Source: Volume 10, Issue 1, February 2016, p. 10 – 16
DOI: 10.1049/iet-syb.2015.0034 , Print ISSN 1751-8849, Online ISSN 1751-8857

Received 05/03/2015, Accepted 25/05/2015, Revised 15/05/2015, Published 01/02/2016

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, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors 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.

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Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks

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