Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference

Motivation: Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally pa...

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Bibliographic Details
Published in:Bioinformatics 2007-12, Vol.23 (23), p.3209-3216
Main Authors: Quach, Minh, Brunel, Nicolas, d'Alché-Buc, Florence
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Biological and medical sciences
Cognitive science
Computer science
Computer Simulation
Fundamental and applied biological sciences. Psychology
General aspects
Life Sciences
Machine Learning
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Modeling and Simulation
Models, Biological
Models, Statistical
Nonlinear Dynamics
Proteome - metabolism
Quantitative Methods
Signal Transduction - physiology
Stochastic Processes
Systems Biology - methods
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Summary:Motivation: Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given. Results: Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach. Conclusion: This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models. Availability: Matlab code available on demand. Contact: florence.dalche@ibisc.univ-evry.fr Supplementary information: Supplementary data are available from http://amisbio.ibisc.fr/dm
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btm510