Bayesian inference of biochemical kinetic parameters using the linear noise approximation
Komorowski, Michal, Finkenstädt, Bärbel, Harper, Claire V. and Rand, D. A. (David A.). (2009) Bayesian inference of biochemical kinetic parameters using the linear noise approximation. BMC Bioinformatics, Vol.10 (No.343). ISSN 1471-2105
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Official URL: http://dx.doi.org/10.1186/1471-2105-10-343
Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data.
We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo.
The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods.
|Item Type:||Journal Article|
|Subjects:||Q Science > QH Natural history > QH426 Genetics|
|Divisions:||Faculty of Science > Centre for Systems Biology
Faculty of Science > Mathematics
Faculty of Science > Statistics
|Library of Congress Subject Headings (LCSH):||Genetics -- Mathematical models, Bayesian statistical decision theory -- Research, Biochemical genetics -- Research, Chemical kinetics -- Research, Biomathematics -- Research|
|Journal or Publication Title:||BMC Bioinformatics|
|Publisher:||BioMed Central Ltd.|
|Official Date:||19 October 2009|
|Access rights to Published version:||Open Access|
|Funder:||Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), European Union (EU), University of Warwick, Wellcome Trust (London, England), Prof. John Glover Memorial Postdoctoral Fellowship|
|Grant number:||BB/F005814/1 (BBSRC), 005137 (EU), EP/C544587/1 (EPSRC) 067252 (Wellcome)|
1. Ehrenberg M, Elf J, Aurell E, Sandberg R, Tegner J: Systems Biology
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