Abstract P5-05-06: Spatio-temporal computational modeling of the p53-Mdm2 pathway

Abstract

Abstract Background: The p53 transcription factor is a well-established regulator of the cell cycle and key to preventing cancer. In response to a variety of cellular stresses (e.g., DNA damage or oncogene activation) p53 is activated and translocates to the nucleus where it activates transcription of target genes which can induce cell cycle arrest, senescence or apoptosis. One of the target genes for p53 is the gene for Mdm2, a negative regulator of p53 function in cells. Mdm2 represses p53 function through two main mechanisms: by promoting p53 ubiquitination and proteasomal degradation and, through direct inhibition of p53 transcriptional activity. Experimental data have revealed that in response to gamma irradiation, p53 and Mdm2 concentrations exhibit spatial and temporal oscillatory dynamics in individual MCF7 breast cancer cells. The precise function of these oscillations is still under investigation. Method: Using in vitro laboratory data, we have developed a computational model to study the spatio-temporal evolution of the p53-Mdm2 pathway. The model comprises a system of coupled nonlinear partial differential equations, including transport terms and reaction kinetics. The transport is assumed to include both random (diffusion) and active mechanisms with proteins convected in the cytoplasm toward the nucleus, modeling transport along microtubules. Internal structures such as ribosomes and the nuclear membrane are also explicitly modeled. Results: Using computational simulations we have found ranges of values for the model parameters such that sustained oscillatory dynamics occur, consistent with available experimental measurements. To bridge the gap between in vitro and in silico experiments realistic cell geometries using imported images of cells have informed our computational domain. The effects of microtubule-disrupting drugs, proteasome inhibitor drugs, and mutations in the DNA binding site of p53, have been modeled yielding data in agreement with published experimental laboratory studies. Conclusions: This mathematical computational model enables us to compare the effects of clinically relevant therapeutic approaches at the single cell level. In particular, the precise temporal and spatial distributions of p53 can be determined and the cellular consequences modeled. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P5-05-06.