Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

Stochastic dynamics of genetic networks: modelling and parameter identification

Author(s):

E. Cinquemani, A. Milias, S. Summers, J. Lygeros
Conference/Journal:

Bioinformatics, vol. 24, no. 23, pp. 2748-2754
Abstract:

Motivation: Identification of regulatory networks is typically based on deterministic models of gene expression. Increasing experimental evidence suggests that the gene regulation process is intrinsically random. To ensure accurate and thorough processing of the experimental data, stochasticity must be explicitly accounted for both at the modelling stage and in the design of the identification algorithms. Results: We propose a model of gene expression in prokaryotes where transcription is described as a probabilistic event, whereas protein synthesis and degradation are captured by first order deterministic kinetics. Based on this model and assuming that the network of interactions is known, a method for estimating unknown parameters such as synthesis and binding rates from the outcomes of multiple time course experiments is introduced. The method accounts naturally for sparse, irregularly sampled and noisy data and is applicable to gene networks of arbitrary size. The performance of the method is evaluated on a model of nutrient stress response in Escherichia coli.

Further Information
Year:

2008
Type of Publication:

(01)Article
Supervisor:



File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@Article { CinEtal:2008:IFA_3126,
    author={E. Cinquemani and A. Milias and S. Summers and J. Lygeros},
    title={{Stochastic dynamics of genetic networks: modelling and
	  parameter identification}},
    journal={Bioinformatics},
    year={2008},
    volume={24},
    number={23},
    pages={2748--2754},
    month=dec,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3126}
}
Permanent link