Artificial neural network modeling for predicting the growth of the microalga Karlodinium veneficum

A feed-forward back-propagation neural network (FBN) was used as a tool for predicting the growth dynamics of the microalga Karlodinium veneficum in a culture medium with any specified concentrations of the key nutrients. A 3-layered FBN configuration of 27-25-1 nodes was used. This FBN satisfactorily represented the nonlinear interactions among all the nutrients of a culture medium containing up to 25 different components. The FBN model was trained using the growth dynamics data from more than 420 batch culture experiments involving different media compositions. The relative impact of individual nutrients, the initial cell concentration and the culture duration on growth profiles were determined through a systematic analysis of the partitioning of the FBN connection weights. Microelements and vitamins together had a higher relative impact on growth compared to the impact of the macronutrients. The trained FBN successfully predicted the cell concentration dynamics in cultures with previously untested initial conditions. The FBN proved to be an excellent tool for predicting the growth curves in the range of culture conditions that were relevant to this study.