In the present study, neural networks (NN) were developed to investigate the responses (average daily gain [ADG] and feed efficiency [FE]) of broiler chicks to protein and two amino acids (AA), namely methionine (Met) and tryptophan (Trp). Separate NN models were constructed for responses to protein and Met and protein and Trp. Comparisons between NN and response surface models revealed higher accuracy of prediction with the NN models. The relative importance of the input variables (protein and AA) on model output (ADG and FE) was assessed using a sensitivity analysis technique. Results indicated that dietary protein is a more important variable than AA (Met and Trp). Optimal values of the input variables (protein and AA) required to maximize ADG and FE in were obtained by subjecting all constructed NN to an optimization algorithm. The optimization algorithm for the protein and Met response models revealed that diets containing 216 g/kg of protein and 5.45 g/kg of Met lead to maximum ADG, whereas maximum FE is achieved with diets containing 222.6 and 5.85 g/kg of protein and Met, respectively. The optimization algorithm for protein and Trp responses showed that 234 g/kg of protein and 2.6 g/kg of Trp in the diet lead to maximum ADG, while maximum FE is achieved with diets containing 240 g/kg of protein and 3.1 g/kg Trp. The optimization results therefore suggest that protein and AA requirements for maximum FE are higher than for maximum ADG.

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