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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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Six simulations of the ARF model under normal conditions (no irradiation).
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Figure 4: Six simulations of the ARF model under normal conditions (no irradiation).

Mentions: The full list of species and reactions for the ARF model are listed in Tables 1 and 2 respectively. Figure 3 shows a diagram of the system (full details are given in the methods section). Even under normal conditions, there is synthesis and degradation of both p53 and Mdm2 so that we might expect low level oscillations of both proteins. However, since we have used a stochastic simulator, there is also a large component of white noise due to protein synthesis and degradation being modelled as random processes and this would mask any oscillatory effects (Figure 4). The autocorrelation function (ACF) for p53 was computed and plotted using the R statistical package. A periodic ACF is consistent with oscillations whereas a non-periodic ACF is consistent with noise. The ACF confirms that most of the oscillatory behaviour in the model is due to white noise but there are some regular oscillations in the second simulation (see Figure 5, top row, 2nd column). The autocorrelation function for Mdm2 showed similar behaviour and so the plots are not shown.


Explaining oscillations and variability in the p53-Mdm2 system.

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

Six simulations of the ARF model under normal conditions (no irradiation).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Six simulations of the ARF model under normal conditions (no irradiation).
Mentions: The full list of species and reactions for the ARF model are listed in Tables 1 and 2 respectively. Figure 3 shows a diagram of the system (full details are given in the methods section). Even under normal conditions, there is synthesis and degradation of both p53 and Mdm2 so that we might expect low level oscillations of both proteins. However, since we have used a stochastic simulator, there is also a large component of white noise due to protein synthesis and degradation being modelled as random processes and this would mask any oscillatory effects (Figure 4). The autocorrelation function (ACF) for p53 was computed and plotted using the R statistical package. A periodic ACF is consistent with oscillations whereas a non-periodic ACF is consistent with noise. The ACF confirms that most of the oscillatory behaviour in the model is due to white noise but there are some regular oscillations in the second simulation (see Figure 5, top row, 2nd column). The autocorrelation function for Mdm2 showed similar behaviour and so the plots are not shown.

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