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Explaining oscillations and variability in the p53-Mdm2 system.

Proctor CJ, Gray DA - BMC Syst Biol (2008)

Bottom Line: We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop.The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions.Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK. c.j.proctor@ncl.ac.uk

ABSTRACT

Background: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing.

Results: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells.

Conclusion: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

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The p53 autocorrelation function (ACF) for the six simulations of irradiated cells as shown in Figure 6. (The ACF for Mdm2 was very similar and so not shown.)
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Figure 7: The p53 autocorrelation function (ACF) for the six simulations of irradiated cells as shown in Figure 6. (The ACF for Mdm2 was very similar and so not shown.)

Mentions: We started by building the model without any intermediary species so that Mdm2 synthesis only depended on the level of unbound p53. This model did not predict sustained oscillations after induction of DNA damage (data not shown). The addition of one intermediary, Mdm2_mRNA, in the negative feedback loop was sufficient for the appearance of sustained oscillations, in both p53 and Mdm2, confirming that a delay is required (Figure 6). Mdm2_mRNA represents the messenger RNA molecule which is transcribed from the Mdm2 gene and carries the coding information to the site of protein synthesis. The autocorrelation function shows that these oscillations are distinct from those generated by white noise (Figure 7). The model output shows that there is intercellular variability as seen in the data. In particular, although most simulations resulted in 5 or 6 peaks in a 30 hour period, there were occasional simulated "cells" with 4 or less peaks. There is also intracellular variability in the oscillation amplitude but much less variation in the oscillation period which was about 5.5 hours. The model predictions regarding the oscillation period agrees with the experimental data [30] as expected, since the parameters which affect the period are well known (see Table 3). The agreement of the model predictions with the data, regarding intracellular and intercellular variability, confirms that a stochastic approach is appropriate for this system.


Explaining oscillations and variability in the p53-Mdm2 system.

Proctor CJ, Gray DA - BMC Syst Biol (2008)

The p53 autocorrelation function (ACF) for the six simulations of irradiated cells as shown in Figure 6. (The ACF for Mdm2 was very similar and so not shown.)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2553322&req=5

Figure 7: The p53 autocorrelation function (ACF) for the six simulations of irradiated cells as shown in Figure 6. (The ACF for Mdm2 was very similar and so not shown.)
Mentions: We started by building the model without any intermediary species so that Mdm2 synthesis only depended on the level of unbound p53. This model did not predict sustained oscillations after induction of DNA damage (data not shown). The addition of one intermediary, Mdm2_mRNA, in the negative feedback loop was sufficient for the appearance of sustained oscillations, in both p53 and Mdm2, confirming that a delay is required (Figure 6). Mdm2_mRNA represents the messenger RNA molecule which is transcribed from the Mdm2 gene and carries the coding information to the site of protein synthesis. The autocorrelation function shows that these oscillations are distinct from those generated by white noise (Figure 7). The model output shows that there is intercellular variability as seen in the data. In particular, although most simulations resulted in 5 or 6 peaks in a 30 hour period, there were occasional simulated "cells" with 4 or less peaks. There is also intracellular variability in the oscillation amplitude but much less variation in the oscillation period which was about 5.5 hours. The model predictions regarding the oscillation period agrees with the experimental data [30] as expected, since the parameters which affect the period are well known (see Table 3). The agreement of the model predictions with the data, regarding intracellular and intercellular variability, confirms that a stochastic approach is appropriate for this system.

Bottom Line: We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop.The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions.Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK. c.j.proctor@ncl.ac.uk

ABSTRACT

Background: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing.

Results: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells.

Conclusion: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

Show MeSH
Related in: MedlinePlus