- Research Article
- Open Access
A Robust Structural PGN Model for Control of Cell-Cycle Progression Stabilized by Negative Feedbacks
EURASIP Journal on Bioinformatics and Systems Biology volume 2007, Article number: 73109 (2007)
The cell division cycle comprises a sequence of phenomena controlled by a stable and robust genetic network. We applied a probabilistic genetic network (PGN) to construct a hypothetical model with a dynamical behavior displaying the degree of robustness typical of the biological cell cycle. The structure of our PGN model was inspired in well-established biological facts such as the existence of integrator subsystems, negative and positive feedback loops, and redundant signaling pathways. Our model represents genes interactions as stochastic processes and presents strong robustness in the presence of moderate noise and parameters fluctuations. A recently published deterministic yeast cell-cycle model does not perform as well as our PGN model, even upon moderate noise conditions. In addition, self stimulatory mechanisms can give our PGN model the possibility of having a pacemaker activity similar to the observed in the oscillatory embryonic cell cycle.
Murray A, Hunt T: The Cell Cycle. Oxford University Press, New York, NY, USA; 1993.
Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P: Molecular Biology of the Cell. 4th edition. Garland Science, New York, NY, USA; 2002.
Li F, Long T, Lu Y, Ouyang Q, Tang C: The yeast cell-cycle network is robustly designed. Proceedings of the National Academy of Sciences of the United States of America 2004, 101(14):4781-4786. 10.1073/pnas.0305937101
Pomerening JR, Kim SY, Ferrell JE Jr.: Systems-level dissection of the cell-cycle oscillator: bypassing positive feedback produces damped oscillations. Cell 2005, 122(4):565-578. 10.1016/j.cell.2005.06.016
Barrera J, Cesar RM Jr., Martins DC Jr., et al.: A new annotation tool for malaria based on inference of probabilistic genetic networks. Proceedings of the 5th International Conference for the Critical Assessment of Microarray Data Analysis (CAMDA '04), Durham, NC, USA, November 2004 36-40.
Barrera J, Cesar RM Jr., Martins DC Jr., et al.: Constructing probabilistic genetic networks of plasmodium falciparum from dynamical expression signals of the intraerythrocytic developement cycle. In Methods of Microarray Data Analysis V. Springer, New York, NY, USA; 2007. chapter 2
Trepode NW, Armelin HA, Bittner M, Barrera J, Gubitoso MD, Hashimoto RF: Modeling cell-cycle regulation by discrete dynamical systems. Proceedings of IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS '05), Newport, RI, USA, May 2005
Kauffman SA: The Origins of Order. Oxford University Press, New York, NY, USA; 1993.
Friedman N, Linial M, Nachman I, Pe'er D: Using Bayesian networks to analyze expression data. Journal of Computational Biology 2000, 7(3-4):601-620. 10.1089/106652700750050961
De Jong H: Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 2002, 9(1):67-103. 10.1089/10665270252833208
Shmulevich I, Dougherty ER, Kim S, Zhang W: Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 2002, 18(2):261-274. 10.1093/bioinformatics/18.2.261
Goutsias J, Kim S: A nonlinear discrete dynamical model for transcriptional regulation: construction and properties. Biophysical Journal 2004, 86(4):1922-1945. 10.1016/S0006-3495(04)74257-5
Bornholdt S: Less is more in modeling large genetic networks. Science 2005, 310(5747):449-451. 10.1126/science.1119959
Spellman PT, Sherlock G, Zhang MQ, et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 1998, 9(12):3273-3297.
Tyson JJ, Chen K, Novak B: Network dynamics and cell physiology. Nature Reviews Molecular Cell Biology 2001, 2(12):908-916. 10.1038/35103078
Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ: Integrative analysis of cell cycle control in budding yeast. Molecular Biology of the Cell 2004, 15(8):3841-3862. 10.1091/mbc.E03-11-0794
Armelin HA, Barrera J, Dougherty ER, et al.: Simulator for gene expression networks. Proceedings of SPIE Microarrays: Optical Technologies and Informatics, San Jose, Calif, USA January 2001, 4266: 248-259.
About this article
Cite this article
Trepode, N.W., Armelin, H.A., Bittner, M. et al. A Robust Structural PGN Model for Control of Cell-Cycle Progression Stabilized by Negative Feedbacks. J Bioinform Sys Biology 2007, 73109 (2007). https://doi.org/10.1155/2007/73109
- Cell Cycle
- System Biology
- Positive Feedback Loop
- Stimulatory Mechanism
- Gene Interaction