Explaining oscillations and variability in the p53-Mdm2 system.
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.


CONCLUSIONS
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.
Proctor
Carole J
CJ
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
Gray
Douglas A
DA
D064007:Ataxia Telangiectasia Mutated Proteins; D018797:Cell Cycle Proteins / Q000378:metabolism; D045744:Cell Line, Tumor; D004249:DNA Damage; D004268:DNA-Binding Proteins / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D018384:Oxidative Stress; D010766:Phosphorylation; D017346:Protein Serine-Threonine Kinases / Q000378:metabolism; D051736:Proto-Oncogene Proteins c-mdm2 / Q000235:genetics / Q000378:metabolism*; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D013269:Stochastic Processes; D049490:Systems Biology; D025803:Tumor Suppressor Protein p14ARF / Q000378:metabolism; D016159:Tumor Suppressor Protein p53 / Q000235:genetics / Q000378:metabolism*; D025521:Tumor Suppressor Proteins / Q000378:metabolism
D018797:Cell Cycle Proteins; D004268:DNA-Binding Proteins; D012333:RNA, Messenger; D025803:Tumor Suppressor Protein p14ARF; D016159:Tumor Suppressor Protein p53; D025521:Tumor Suppressor Proteins; D051736:Proto-Oncogene Proteins c-mdm2; C576324:ATM protein, human; D064007:Ataxia Telangiectasia Mutated Proteins; D017346:Protein Serine-Threonine Kinases