Interdisciplinary Bio Central

한국생명정보학회 (Korean Society for Bioinformatics)
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G-Networks Based Two Layer Stochastic Modeling of Gene Regulatory Networks with Post-Translational Processes

  • Kim, Ha-Seong (Imperial College, Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering) ;
  • Gelenbe, Erol (Imperial College, Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering)
  • 투고 : 2011.04.28
  • 심사 : 2011.04.06
  • 발행 : 2011.06.30
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초록

Background: Thanks to the development of the mathematical/statistical reverse engineering and the high-throughput measuring biotechnology, lots of biologically meaningful genegene 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.

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참고문헌

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피인용 문헌

  1. Stochastic Gene Expression Modeling with Hill Function for Switch-Like Gene Responses vol.9, pp.4, 2012, https://doi.org/10.1109/TCBB.2011.153
  2. Reconstruction of Large-Scale Gene Regulatory Networks Using Bayesian Model Averaging vol.11, pp.3, 2012, https://doi.org/10.1109/TNB.2012.2214233
  3. A queueing approach to multi-site enzyme kinetics vol.4, pp.3, 2011, https://doi.org/10.1098/rsfs.2013.0077
  4. EROL GELENBE: A CAREER IN MULTI-DISCIPLINARY PROBABILITY MODELS vol.30, pp.3, 2011, https://doi.org/10.1017/s0269964816000024
  5. G-NETWORKS AND THEIR APPLICATIONS TO MACHINE LEARNING, ENERGY PACKET NETWORKS AND ROUTING: INTRODUCTION TO THE SPECIAL ISSUE vol.31, pp.4, 2011, https://doi.org/10.1017/s0269964817000171