Signalling systems that control cell decisions allow cells to process input signals by apprehending the information of the cell to give one of these two feasible outputs: cell death or cell survival. In this paper, a well-structured control design methodology supported by a hierarchical design system was developed to examine signalling networks that control cell decisions by considering a combinations of three primary signals (input proteins): the pro survival growth factors, epidermal growth factor (EGF), insulin, and the pro death cytokine, tumour necrosis factor-α (TNF), for AKT/protein kinase B. The AKT actions were examined by using the three input proteins for cell survival/apoptosis for a period of 0–24 h in 13 different slices for ten different combinations. Experimental analysis was performed to consider the reactions that were essential to explain the action of AKT. Furthermore, pre-processing and data normalization were performed by using standard deviation, plotting histograms, and scatter plots. Feature extraction and selection were performed using correlation matrix. Radial basis function (RBF) and multiple-layer perceptron (MLP) were used for cell survival/death classification. For all the ten combinations of the three input proteins, 42.85, 347.22, 153.13 were obtained as the minimum value, maximum value, and mean value, respectively, and 126.11 was obtained as the standard deviation for 5-0-5 ng/ml combinations of TNF-EGF-Insulin. The results obtained with MLP 10-8-1 were found to outperform other techniques. The results from the experimental analysis indicate that it is possible to build self-consistent compendia cell-signalling data based on AKT protein which were simulated computationally to yield important insights for the control of cell survival/death.

中文翻译：

---

诊断Akt蛋白的细胞存活/死亡的计算模型和实验分析

控制细胞决策的信号系统允许细胞通过了解细胞信息来处理输入信号，从而提供以下两个可行输出之一：细胞死亡或细胞存活。在本文中，开发了一种由分层设计系统支持的结构合理的控制设计方法，以通过考虑三个主要信号（输入蛋白）的组合来检查控制细胞决策的信号网络：亲存活生长因子，表皮生长因子（ EGF），胰岛素以及AKT /蛋白激酶B的致死细胞因子，肿瘤坏死因子-α（TNF）。通过使用三种输入蛋白在0-24期间内的细胞存活/凋亡检查了AKT的作用在十个不同组合的13个不同切片中的h。进行了实验分析，以考虑对解释AKT的作用至关重要的反应。此外，通过使用标准差，绘制直方图和散点图来执行预处理和数据归一化。使用相关矩阵进行特征提取和选择。径向基函数（RBF）和多层感知器（MLP）用于细胞存活/死亡分类。对于三种输入蛋白质的所有十种组合，分别获得42.85、347.22、153.13作为最小值，最大值和平均值，并获得126.11作为5-0-5 ng / ml组合的标准偏差。 TNF-EGF-胰岛素。发现使用MLP 10-8-1获得的结果优于其他技术。