Cell Growth Prediction for Bacillus Licheniformis through Artificial Neural Network at Simultaneous Multiple Variation in Concentration of Nutrients in Media

In the cell growth and metabolite production, the selection of nutrients and determination of its concentrations in the cultivation media is very important step for commercially viable products. Formulation of media requires lot of experiments and so time consuming and tedious. The conventional methods also involves errors. To eliminate the error involved and to reduce the number of experiments a new has been tried in the media formulation and optimization. The application of Artificial Neural Network for the prediction of effect of nutrients in the media on cell growth of Bacillus licheniformis has been presented in this work. Ten different medias used were prepared by simultaneous multiple and randomly variation of the concentration of the components in the selected range. The medias were composed of starch, peptone and various selected salts. The cell concentrations were determined at various media composition. An Artificial Neural Network was prepared to use the nutrient concentrations as signal input and cell concentrations as output. Once the network was trained, the results showed its ability to model biochemical nonlinear processes and could be used for the selection and optimization of media composition.