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Bayesian inference of biochemical kinetic parameters using the linear noise approximation

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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

Abstract

Background
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.
Results
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.
Conclusion
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.

ISSN:

1471-2105

Official Date:

19 October 2009

Dates:

Date	Event
19 October 2009	Submitted

Volume:

Vol.10

Number:

No.343

Identification Number:

10.1186/1471-2105-10-343

Status:

Peer Reviewed

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)

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