ArticleIET systems biology2016
Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
Article in IET systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models.Sensors (Basel, Switzerland) · 2020Article
- Estimation of parameters for plasma glucose regulation in type-2 diabetics in presence of meal.IET systems biology · 2018Article
- Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.Bulletin of mathematical biology · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
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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Registered trials
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