In Part I of this work, we carried out a logical analysis of a simple model describing the interplay between protein p53, its main negative regulator Mdm2 and DNA damage, and briefly discussed the corresponding differential model (Abou-Jaoudé et al., 2009). This analysis allowed us to reproduce several qualitative features of the kinetics of the p53 response to damage and provided an interpretation of the short and long characteristic periods of oscillation reported by Geva-Zatorsky et al. (2006) depending on the irradiation dose. Starting from this analysis, we focus here on more quantitative aspects of the dynamics of our network and combine the differential description of our system with stochastic simulations which take molecular fluctuations into account. We find that the amplitude of the p53 and Mdm2 oscillations is highly variable (to a degree that depends, however, on the bifurcation properties of the system). In contrast, peak width and timing remain more regular, consistent with the experimental data. Our simulations also show that noise can induce repeated pulses of p53 and Mdm2 that, at low damage, resemble the slow irregular fluctuations observed experimentally. Adding the stochastic dimension in our modeling further allowed us to account for an increase of the fraction of cells oscillating with a high frequency when the irradiation dose increases, as observed by Geva-Zatorsky et al. (2006).
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http://dx.doi.org/10.1016/j.jtbi.2010.03.031 | DOI Listing |
Comput Math Methods Med
January 2015
Institute of Systems Biology, Shanghai University, 99 Shangda Road, Shanghai 200444, China.
The latest experimental evidence indicates that acetylation of p53 at K164 (lysine 164) and K120 may induce directly cell apoptosis under severe DNA damage. However, previous cell apoptosis models only studied the effects of active and/or inactive p53, that is, phosphorylation/dephosphorylation of p53. In the present paper, based partly on Geva-Zatorsky et al.
View Article and Find Full Text PDFJ Theor Biol
June 2010
Université Libre de Bruxelles (U.L.B.), Faculté des Sciences, Unit of Theoretical and Computational Biology, Campus Plaine C.P. 231, B-1050 Brussels, Belgium.
In Part I of this work, we carried out a logical analysis of a simple model describing the interplay between protein p53, its main negative regulator Mdm2 and DNA damage, and briefly discussed the corresponding differential model (Abou-Jaoudé et al., 2009). This analysis allowed us to reproduce several qualitative features of the kinetics of the p53 response to damage and provided an interpretation of the short and long characteristic periods of oscillation reported by Geva-Zatorsky et al.
View Article and Find Full Text PDFJ Theor Biol
June 2009
Université Libre de Bruxelles (U.L.B.), Faculté des Sciences, Unit of Theoretical and Computational Biology, Campus Plaine C.P. 231, B-1050 Brussels, Belgium.
We investigate the dynamical properties of a simple four-variable model describing the interactions between the tumour suppressor protein p53, its main negative regulator Mdm2 and DNA damage, a model inspired by the work of Ciliberto et al. [2005. Steady states and oscillations in the p53/Mdm2 network.
View Article and Find Full Text PDFJ Theor Biol
November 2007
Laboratoire Matières et Systèmes Complexes, Université Paris 7, CNRS UMR 7057, c.c. 7056, 75205 Paris Cedex 13, France.
Oscillatory behaviours in genetic networks are important examples for studying the principles underlying the dynamics of cellular regulation. Recently the team of Alon has reported a surprisingly rich oscillatory response of the p53 tumor suppressor to irradiation stress et al. [Lahav, G.
View Article and Find Full Text PDFNature
November 2006
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 76100 Israel.
Protein expression is a stochastic process that leads to phenotypic variation among cells. The cell-cell distribution of protein levels in microorganisms has been well characterized but little is known about such variability in human cells. Here, we studied the variability of protein levels in human cells, as well as the temporal dynamics of this variability, and addressed whether cells with higher than average protein levels eventually have lower than average levels, and if so, over what timescale does this mixing occur.
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