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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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Related in: MedlinePlus

Oscillations are triggered when DNA damage accumulates gradually over time and disappear when DNA is repaired (ARF model). DNA damage starts to accumulate at time t = 0, and increases until t = 40 hours, when the rate of DNA repair is increased ten thousand-fold.
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Figure 8: Oscillations are triggered when DNA damage accumulates gradually over time and disappear when DNA is repaired (ARF model). DNA damage starts to accumulate at time t = 0, and increases until t = 40 hours, when the rate of DNA repair is increased ten thousand-fold.

Mentions: During ageing there is a gradual increase in damaged DNA due to either an increase in oxidative stress and/or a decrease in anti-oxidant capacity. We used our model to see if oscillations would occur in this scenario. To simulate the effect of a gradual increase in DNA damage we introduced a species called ROS (reactive oxygen species) into the model and changed the reaction for DNA damage so that it depended on the level of ROS instead of IR. We set the rate law for DNA damage reaction so that it gradually increased over time. We also added an event at time 40 hours to increase the repair capacity to see if the oscillations would die down after DNA damage is repaired. The model predicts that oscillations appear when DNA damage starts to accumulate and then remain even after DNA damage is repaired, although the amplitude decreases over time (Figure 8). The persistence of the oscillations is due to the slow turnover of ARF. If we increase the degradation rate of ARF, then the oscillations die out more quickly (data not shown). Our simulations suggest that it is not necessary to have a sudden increase in damage in order to trigger spontaneous oscillations.


Explaining oscillations and variability in the p53-Mdm2 system.

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

Oscillations are triggered when DNA damage accumulates gradually over time and disappear when DNA is repaired (ARF model). DNA damage starts to accumulate at time t = 0, and increases until t = 40 hours, when the rate of DNA repair is increased ten thousand-fold.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Oscillations are triggered when DNA damage accumulates gradually over time and disappear when DNA is repaired (ARF model). DNA damage starts to accumulate at time t = 0, and increases until t = 40 hours, when the rate of DNA repair is increased ten thousand-fold.
Mentions: During ageing there is a gradual increase in damaged DNA due to either an increase in oxidative stress and/or a decrease in anti-oxidant capacity. We used our model to see if oscillations would occur in this scenario. To simulate the effect of a gradual increase in DNA damage we introduced a species called ROS (reactive oxygen species) into the model and changed the reaction for DNA damage so that it depended on the level of ROS instead of IR. We set the rate law for DNA damage reaction so that it gradually increased over time. We also added an event at time 40 hours to increase the repair capacity to see if the oscillations would die down after DNA damage is repaired. The model predicts that oscillations appear when DNA damage starts to accumulate and then remain even after DNA damage is repaired, although the amplitude decreases over time (Figure 8). The persistence of the oscillations is due to the slow turnover of ARF. If we increase the degradation rate of ARF, then the oscillations die out more quickly (data not shown). Our simulations suggest that it is not necessary to have a sudden increase in damage in order to trigger spontaneous oscillations.

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