P53 controls the cell cycle arrest and cell apoptosis through interaction with the downstream genes and their signal pathways. To stimulate the investigation into the complicated responses of p53 under the circumstance of ion radiation (IR) in the cellular level, a dynamic model for the p53 stress response networks is proposed. The model can be successfully used to simulate the dynamic processes of generating the double-strand breaks (DSBs) and their repairing, ataxia telangiectasia mutated (ATM) activation, as well as the oscillations occurring in the p53-MDM2 feedback loop.

P53, a vital anticancer gene, controls the transcription and translation of a series of genes, and implement the cell cycle arrest and cell apoptosis by regulating their complicated signal pathways. Under radiotherapy, cell can trigger internal self-defense mechanisms in fighting against genome stresses induced by acute ion radiation (IR). To simulate the investigating of cellular responding acute IR at single cell level further, we propose a model of P53 gene regulatory networks under radiotherapy. Our model can successfully implement the kinetics of double strand breaks (DSBs) generating and their repair, ataxia telangiectasia mutated (ATM) activation, as well as P53-MDM2 feedback regulating. By comparing simulations under different IR dose, we can try to find the optimal strategy in controlling of IR dose and therapy time, and provide some theoretical analysis to obtain much better outcome of radiotherapy further.

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Although Kolmogorov-Smirnov (KS) statistic is a widely used method, some weaknesses exist in investigating abrupt Change Point (CP) problems, e.g. it is time-consuming and invalid sometimes. To detect abrupt change from time series fast, a novel method is proposed based on Haar Wavelet (HW) and KS statistic (HWKS). First, the two Binary Search Trees (BSTs), termed TcA and TcD, are constructed by multi-level HW from a diagnosed time series; the framework of HWKS method is implemented by introducing a modified KS statistic and two search rules based on the two BSTs; and then fast CP detection is implemented by two HWKS-based algorithms. Second, the performance of HWKS is evaluated by simulated time series dataset. The simulations show that HWKS is faster, more sensitive and efficient than KS, HW, and T methods. Last, HWKS is applied to analyze the electrocardiogram (ECG) time series, the experiment results show that the proposed method can find abrupt change from ECG segment with maximal data fluctuation more quickly and efficiently, and it is very helpful to inspect and diagnose the different state of health from a patient's ECG signal.