Dataset Information

Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM

ABSTRACT: Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM This model is described in the article: Explaining oscillations and variability in the p53-Mdm2 system. Proctor CJ, Gray DA. BMC Syst Biol 2008; 2: 75 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. This model is hosted on BioModels Database and identified by: BIOMD0000000188. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Carole Proctor

PROVIDER: BIOMD0000000188 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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Explaining oscillations and variability in the p53-Mdm2 system.

Proctor Carole J CJ Gray Douglas A DA

BMC systems biology 20080818

<h4>Background</h4>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, wi ...[more]

PMID: 18706112

Similar Datasets

Project description:Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by p14ARF This model is described in the article: Explaining oscillations and variability in the p53-Mdm2 system. Proctor CJ, Gray DA. BMC Syst Biol 2008; 2: 75 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. This model is hosted on BioModels Database and identified by: BIOMD0000000188. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:ARF is a tumour suppressor activated by oncogenic stress, which stabilizes p53. Although p53 is a key component of the response to DNA damage, a similar function for ARF has not been ascribed. In this work we show that homologous recombination (HR) -deficient primary mouse and human cells accumulate DNA damage, which triggers checkpoint signalling and ARF activation. We also show that in the absence of ARF p53 is induce in response to DNA damage, but surprisingly fails to trigger senescence. The hypothesis tested in this study was that ARF deficiency alters the spectrum of genes activated by p53 in response to HR deficiency. Using mRNA microarray analysis and expression profiling, we identified genes upregulated in wild type primary MEFs treated with RAD51 shRNA, whose expression was not induced in similarly treated Arf -/- primary MEFs. Clustering analysis of p53 transcriptional targets with increased expression in RAD51-depleted cells revealed three groups: genes requiring functional ARF for their expression, genes transcribed in an ARF-independent manner and genes that are upregulated in both ARF-proficient and ARF-deficient cells. Among the genes in the last group we found Cdkn1a(p21), encoding the p21 inhibitor of cyclindependent kinases and well-characterized effector of the p53 role in promoting senescence. The subset of p53 targets requiring ARF for their transcription was specifically upregulated in response to RAD51 inhibition in wild type, but not Arf -/- MEFs. Among these genes, two encoded the dual specificity phosphatase (DUSP) family members DUSP4 and DUSP7, which act as ERK phosphatases and negative regulators of the MAPK kinase pathway in mammalian cells. Four samples were analysed (WT or ARF negative cells transfected with GFP or RAD51 shRNAs) in duplicate. Samples treated with GFP shRNA are the reference samples and the ones treated with RAD51 shRNA are the experimental ones (RAD51 depletion leads to HR deficiency). The WT cell line is our control cell line to study the effect of HR deficiency on the transcription of p53 target genes

Project description:This a model from the article: Stress-specific response of the p53-Mdm2 feedback loop Alexander Hunziker, Mogens H Jensen and Sandeep Krishna BMC Systems Biology 2010, Jul 12;4(1):94 20624280, Abstract: ABSTRACT: BACKGROUND: The p53 signalling pathway has hundreds of inputs and outputs. It can trigger cellular senescence, cell-cycle arrest and apoptosis in response to diverse stress conditions, including DNA damage, hypoxia and nutrient deprivation. Signals from all these inputs are channeled through a single node, the transcription factor p53. Yet, the pathway is flexible enough to produce different downstream gene expression patterns in response to different stresses. RESULTS: We construct a mathematical model of the negative feedback loop involving p53 and its inhibitor, Mdm2, at the core of this pathway, and use it to examine the effect of different stresses that trigger p53. In response to DNA damage, hypoxia, etc., the model exhibits a wide variety of specific output behaviour -- steady states with low or high levels of p53 and Mdm2, as well as spiky oscillations with low or high average p53 levels. CONCLUSIONS: We show that even a simple negative feedback loop is capable of exhibiting the kind of flexible stress-specific response observed in the p53 system. Further, our model provides a framework for predicting the differences in p53 response to different stresses and single nucleotide polymorphisms. The parameters of the model corresponds to the resting state, with delta = 11hr-1, gamma = 0.2hr-1, kt = 0.03nM-1hr-1 and kf = 5000nM-1hr-1. To simulate different stress conditions as in figure 2A (also look at the curation figure of this model) of the reference publication, the above parameter should be changed. The parameter values corresponding to different stress conditions are shown in the following table. Stress Condition/Parameter delta gamma kt kf Nutlin 11hr-1 0.2hr-1 0.03nM-1hr-1 500nM-1hr-1 Oncogene 2hr-1 0.2hr-1 0.03nM-1hr-1 5000nM-1hr-1 DNA damage 2hr-1 0.5hr-1 0.03nM-1hr-1 2500nM-1hr-1 Hypoxia 2hr-1 0.2hr-1 0.01nM-1hr-1 5000nM-1hr-1 This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.For more information see the terms of use.To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.