Abstract
BackgroundThe tumor suppressor p53 plays pivotal roles in tumorigenesis suppression. Although oscillations of p53 have been extensively studied, the mechanism of p53 pulses and their physiological roles in DNA damage response remain unclear.ResultsTo address these questions we presented an integrated model in which Ataxia-Telangiectasia Mutated (ATM) activation and p53 oscillation were incorporated with downstream apoptotic events, particularly the interplays between Bcl-2 family proteins. We first reproduced digital oscillation of p53 as the response of normal cells to DNA damage. Subsequent modeling in mutant cells showed that high basal DNA damage is a plausible cause for sustained p53 pulses observed in tumor cells. Further computational analyses indicated that p53-dependent PUMA accumulation and the PUMA-controlled Bax activation switch might play pivotal roles to count p53 pulses and thus decide the cell fate.ConclusionThe high levels of basal DNA damage are responsible for generating sustained pulses of p53 in the tumor cells. Meanwhile, the Bax activation switch can count p53 pulses through PUMA accumulation and transfer it into death signal. Our modeling provides a plausible mechanism about how cells generate and orchestrate p53 pulses to tip the balance between survival and death.
Highlights
The tumor suppressor p53 plays pivotal roles in tumorigenesis suppression
The levels of activated AtaxiaTelangiectasia Mutated (ATM) will not fall until the remaining double strand breaks (DSBs) visits the limit point (DSB = 1.22)
The bifurcation parameter was chosen to be [ATM*] in consideration of connecting the p53-MDM2 module with the upstream ATM module. These results suggest that ATM activation status can prominently influence on dynamics of the p53-MDM2 module
Summary
The tumor suppressor p53 plays pivotal roles in tumorigenesis suppression. Oscillations of p53 have been extensively studied, the mechanism of p53 pulses and their physiological roles in DNA damage response remain unclear. Biological networks are abstract representation of biological systems, which capture many of their essential characteristics [1]. Computational modeling of biological networks predominantly obtains insight into their systems behaviors. Special attention is paid to the dynamical networks of cell cycle transitions, circadian rhythms and apoptosis [2,3,4]. Apoptosis, which evolves in an all-ornone fashion, is a self-defense machinery to eliminate cells that are potentially dangerous [5]. The process of cell death decision concerns an integration of multiple malignant inputs. Once the decision has been made, this event is considered to be a 'point of no return'
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