Stochastic modeling and simulation of the p53-MDM2/MDMX loop

Xiaodong Cai, Zhi Min Yuan

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


The p53 gene is crucial for effective tumor suppression in humans as supported by its universal inactivation in cancer cells either through mutations affecting the p53 locus directly or through aberration of its normal regulation. The p53 tumor repressor is regulated through a negative feedback loop involving its transcriptional target MDM2. MDMX is also an essential negative regulator of p53. Several computational models have been proposed to simulate the dynamics of the p53-MDM2 loop, but they do not include MDMX, only account for some basic interactions between p53 and MDM2 and cannot capture the intrinsic noise in the loop. In this article, we present a comprehensive model for the p53-MDM2/MDMX loop that accounts for most known interactions among p53, MDM2 and MDMX. Our model is characterized by a set of molecular reactions, which enables us to employ stochastic simulation to investigate the dynamics of the loop. In agreement with experiments, our results show that p53 and MDM2 undergo oscillations after DNA damage in the presence of noise, and the variation in oscillation amplitudes is much higher than that in oscillation periods. Our simulations predict that intrinsic noise contributes to 60%-70% of the total variation in oscillation amplitudes and periods. The protein levels of p53, MDM2, and MDMX after treatment with Nutlin in our simulations are also consistent with experimental results. Our simulation results further predict that p53 levels increase dramatically after MDM2 is knocked out, but increase with a much less amount after MDMX is knocked out. This may partially explain why MDM2-null and MDMX-null mouse embryos die in different developmental stages. Our stochastic model and simulation provide insights into the variability of the behavior of the p53 pathway and can be used to predict the dynamics of the pathway after certain interventions.

Original languageEnglish (US)
Pages (from-to)917-933
Number of pages17
JournalJournal of Computational Biology
Issue number7
StatePublished - Jul 1 2009


  • Algorithms
  • Gene chips
  • Gene networks
  • Genetics
  • Machine learning

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Modeling and Simulation
  • Computational Theory and Mathematics


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