Non Linear Modeling of Cell Survival/Death Using Artificial Neural Network

Signal transduction pathways control cellular responses to stimuli, but it is unclear how molecular information is processed as a network. Large-scale collection and systematization of such data is likely to have a great impact on cell biology as complete genome sequencing has had on genetics. Cell signaling pathways interact with one another to form networks. Such networks are complex in their organization and exhibit emergent properties such as bistability and ultra sensitivity. Analysis of signaling networks requires a combination of experimental and theoretical approaches including the development and analysis of models. This work examines signaling networks that control the survival decision treated with combinations of the pro-death cytokine, tumor necrosis factor-a (TNF), and the pro-survival growth factors, epidermal growth factor (EGF) and insulin. A non linear model using Artificial Neural Network was proposed for cell survival/ death. With this we have made software which is used by doctors in doing chemotherapy without doing any experiments.