In this section, experimental analysis was performed on HT carcinoma cells. A heat map was obtained from the analysis of ten different concentrations of the three input proteins. Later, features were extracted and selected using a correlation vector. The selected features were classified using different neural network techniques, namely: multiple-layer perceptron (MLP) and radial based function (RBF). The block diagram of the proposed methodology is shown in Fig. 1. The prediction model for cell death/survival was implemented with the proposed method using Statistica Software. In total, we obtained 300 values for each combination of input proteins.
Studies of signalling pathways are focused on depicting downstream and upstream interactions, and then systemizing these interactions into linear cascades that balance information from cell surface receptors to cellular effectors. A bottom-up approach was used for the hierarchical model as shown in Fig. 2.
The bottom-up hierarchical approach starts with the proteins/genes as biological components analogous to the physical layer which consists of active and passive components from electronic elements. In the hierarchy, the next layer is the device layer which comprises of biochemical reactions that regulate the flow of information and manipulate physical processes. The biochemical reactions are equivalent to logic gates which perform computations in a computer. At the module layer, a synthetic biologist could use quiet a number of biological devices to assemble complex pathways that function like integrated circuits. The connection of these modules to each other and their integration into host cells allows the synthetic biologist to extend or modify the behaviour of cells in a programmatic fashion.
HT carcinoma cells are considered for the analysis of cell survival/death by using AKT as a marker protein. The experimental analysis was performed by considering different concentrations of TNF such as 0, 0.2, 5, and 100 ng/ml. Similarly, different concentrations of EGF, such as 0, 1, and 100 ng/ml, and insulin, such as 0, 1, 5, and 500 ng/ml, for making different cultures were analysed for a period of 24 h by adding 1/20 of diluted stimulus. The 0–24 h time frame was divided into 0, 5, 15, 30, 60, and 90 min, and 2, 4, 8, 12, 16, 20, and 24 h. The cells were exposed to ten cytokine treatments so as to explore systematically the relationship between activation of intracellular signalling cascaded as cytokine receptor interaction and survival death cell fate decisions. All the observations were monitored for a period of 48 h. To explore systematic relationships between the activation of intracellular signalling cascades, cytokine receptor interaction, and apoptosis-survival cell fate decisions cells were exposed to a set of ten different treatments of input proteins. At the 13 time point after cytokine addition, three replicate dishes of cells were harvested to measure kinase activities. Altogether, ten distinct protein signals were examined, namely, (a) assayed in vitro using microtiter-based immuno-complex kinase activity assays: ERK, JNK1, AKT, MK2, and IKK; (b) antibody arrays: phospho-to-total (pt) and phospho total measures of EGFR and AKT; and (c) immunoblotting: five phosphorylation sites on four proteins. Out of the different proteins, AKT signals were examined. Each protein signal was integrated by 12-h, 24-h, and 48-h time frame and then analysed with a set of three input protein treatment. This analysis generated a heat map in which the positions of ten protein signals were defined in comparison to the TNF, EGF, and insulin stimuli. The heat map was prepared for the marker protein of ten different concentrations of input proteins. The ten different concentrations of input proteins (TNF-EGF-insulin) are: 0-0-0, 5-0-0, 100-0-0, 0-100-0, 5-1-0, 100-100-0, 0-0-500, 0.2-0-1, 5-0-5, and 100-0-500. Histograms, standard deviation, and scatter plots were calculated to pre-process the data. Different features like mean, maximum, minimum, and standard deviation for training, testing, validation, and overall data were calculated for all ten different concentrations of the input combinations. Correlation vectors were calculated as feature selection techniques and were used to select the best concentrations of input combinations [11]. The results were validated using Eigenvalues and vector calculations. With the help of Eigenvectors, linear transformation is easy to understand. An eigenvector ν of a matrix A is independent of the linear transformation: Aν = λν ⇒ λ(Bu) = A(Bu). Eigenvectors are a set of basic functions that help in describing data variability. The Eigenvalues of our data were calculated from the best combinations of three input proteins which were used to classify cell death/survival for AKT protein. For classification of the proteins, we have employed artificial neural network (ANN) techniques such as MLP and RBF for cell death and cell survival decisions. ANN is a special nonlinear model for classification, clustering as well as regression. There are at least three layers of nodes for a MLP, namely: input layer, hidden layer, and output layer. The input layer consist of input variables which are numeric. Non-numeric data is converted to numeric before it can be used in an ANN technique. This layer is sometimes called the visible layer. The hidden layers consist of layers of nodes between the input and output layers; there may be one or more of these layers. The output layer is a layer of nodes which produce the output variable. Our proposed ANN model for the detection of cell survival/death for AKT is shown in Fig. 3.
ANN techniques are fast becoming a useful approach for signal-processing technologies. In engineering, neural networks serve two important functions: as nonlinear adaptive filters and as pattern classifiers. They are most often adaptive nonlinear systems that learn to perform a function (an input/output map) from data. Adaptive implies that the system parameters change during operation, normally called the training phase. After the training phase, the ANN parameters are fixed and can be deployed to solve problems.