Duan Shaojin, Wan Lin, Fu Wenjiang J, Pan Hong, Ding Qi, Chen Chang, Han Peiwei, Zhu Xiaoyan, Du Liying, Liu Hongxiao, Chen Yuxia, Liu Ximing, Yan Xiting, Deng Minghua, Qian Minping
Guang An Men Hospital, China Academy of Chinese Medicine Sciences, 100053 Beijing, People's Republic of China.
Apoptosis. 2009 Feb;14(2):236-45. doi: 10.1007/s10495-008-0288-4.
Increasing evidence has been gathered for p53-dependent apoptosis, but it is still unclear how p53 initiates apoptosis by employing its transcriptional program. Pair-wise interactions of p53 with expression of other genes fail to predict p53 levels or rate of apoptosis. A more sophisticated approach, using neural networks, permits prediction of interaction among three or more genes (p53, bax, and ING1). These interactions are decidedly nonlinear. Careful measurements and advanced mathematical treatments will permit us not only to understand how expression of pro- and anti-apoptotic genes is regulated, but also to integrate cross-platform and cross-experimental data for the validation of predicted interactions.
越来越多的证据支持p53依赖性凋亡，但p53如何通过其转录程序启动凋亡仍不清楚。p53与其他基因表达的成对相互作用无法预测p53水平或凋亡率。一种更复杂的方法，即使用神经网络，可以预测三个或更多基因（p53、bax和ING1）之间的相互作用。这些相互作用显然是非线性的。仔细的测量和先进的数学处理将使我们不仅能够理解促凋亡和抗凋亡基因的表达是如何调控的，还能够整合跨平台和跨实验数据以验证预测的相互作用。
