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Rapid Report (Bioinformatics/Computational biology/Molecular modeling)

G-Networks Based Two Layer Stochastic Modeling of Gene Regulatory Networks with Post-Translational Processes
Haseong Kim1 and Erol Gelenbe1,*
1Imperial College, Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Exhibition Road, London SW7 2BT, UK
*Corresponding author
  Received : April 28, 2011
  Accepted : May 06, 2011
  Published : May 06, 2011
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Synopsis

Background: Thanks to the development of the mathematical/statistical reverse engineering and the high-throughput measuring biotechnology, lots of biologically meaningful gene-gene interaction networks have been revealed. Steady-state analysis of these systems provides an important clue to understand and to predict the systematic behaviours of the biological system. However, modeling such a complex and large-scale system is one of the challenging difficulties in systems biology.
Results: We introduce a new stochastic modeling approach that can describe gene regulatory mechanisms by dividing two (DNA and protein) layers. Simple queuing system is employed to explain the DNA layer and the protein layer is modeled using G-networks which enable us to account for the post-translational protein interactions. Our method is applied to a transcription repression system and an active protein degradation system. The steady-state results suggest that the active protein degradation system is more sensitive but the transcription repression system might be more reliable than the transcription repression system.
Conclusions: Our two layer stochastic model successfully describes the long-run behaviour of gene regulatory networks which consist of various mRNA/protein processes. The analytic solution of the G-networks enables us to extend our model to a large-scale system. A more reliable modeling approach could be achieved by cooperating with a real experimental study in synthetic biology.

Keyword: systems biology, gene regulatory networks, G-networks, queueing system, large-scale modeling
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