School of Mathematical Sciences

Inference, sensitivity and identifiability in stochastic chemical systems. menu

Inference, sensitivity and identifiability in stochastic chemical systems.

Speaker: 
Michal KomorowskiTheoretical Systems Biology Group, Imperial College London
Date/Time: 
Thu, 04/11/2010 - 16:30
Room: 
M203
Seminar series: 

The aim of the presentation is to present a novel, integrated
theoretical framework for the analysis of stochastic biochemical
reactions models. The framework includes efficient methods for
statistical parameter estimation from experimental data, as well as
tools to study parameter identifiability, sensitivity and robustness.
The methods provide novel conclusions about functionality and
statistical properties of stochastic systems.

I will introduce a general model of chemical reactions described by
the Chemical Master Equation that I approximate using the linear noise
approximation. This allows to write explicit expressions for the
likelihood of experimental data, which lead to an efficient inference
algorithm and a quick method for calculation of the Fisher Information
Matrices.

A number of experimental and theoretical examples will be presented to
show how the techniques can be used to extract information from the
noise structure inherent to experimental data. Examples include
inference of parameters of gene expression using a fluorescent
reporter gene data, a Bayesian hierarchical model for estimation of
transcription rates and a study of the p53 system. Novel insights into
the causes and effects of stochasticity in biochemical systems are
obtained by the analysis of the Fisher Information Matrices.

References:

Komorowski, M. , Finkenstädt , B., Rand, D. A. , (2010); Using single
fluorescent reporter gene to infer half-life of extrinsic noise and
other parameters of gene expression, Biophysical Journal, Vol 98,
Issue 12, 2759-2769,

Komorowski, M. , Finkenstädt , B.,  Harper, C. V., Rand, D. A. ,
(2009); Bayesian inference of biochemical kinetic parameters using the
linear noise approximation,  BMC Bioinformatics, 2009, 10:343
doi:10.1186/1471-2105-10-343, 2009,

B. Finkenstadt; E. A. Heron; M. Komorowski; K. Edwards; S. Tang; C. V.
Harper; J. R. E. Davis; M. R. H. White; A. J. Millar; D. A. Rand,
(2008);
Reconstruction of transcriptional dynamics from gene reporter data
using differential equations,  Bioinformatics 15 December 2008; 24:
2901 - 2907.