Novel predictive model of cell survival/death related effects of Extracellular Signal-Regulated kinase protein

Computational modelling is a technique for modelling and solving real-world problems by utilising computing to provide solutions. This paper presents a novel predictive model of cell survival/death-related effects of Extracellular Signal-Regulated Kinase Protein. The computational model was designed using Neural Networks and fuzzy system. Three hundred ERK samples were examined using ten different concentrations of three input proteins: EGF, TNF, and insulin. Based on the different concentrations of input proteins and different samples of ERK protein, adjustment Anderson darling (AD) statistics for multiple distribution functions were computed considering different test such as visual test, Pearson correlation coefficient, and uniformity tests. The results reveal that utilising different concentrations and samples, values such as 7.55 AD and 18.4 AD were obtained using the Weibull distribution function for 0 ng/ml of TNF, 100 ng/ml of EGF, and 0 ng/mL of insulin concentrations. The model was validated by predicting the various ERK protein values that fall within the observed range. The proposed model agrees with the deterministic model, which was developed using difference equations.

Automated summary

Computational modelling is used to solve real-world problems by applying computing techniques. This paper introduces a novel predictive model for studying cell survival/death effects of the ERK protein. The model utilizes Neural Networks and fuzzy systems to analyze ERK samples with various concentrations of input proteins. The results demonstrate the accuracy of the model in predicting ERK protein values.