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Recent biotechnology requires implementation of new modelling methods based on knowledge principles and learning structures, comprised in fuzzy knowledgebased systems (FKBS), neural networks (NN) and different hybrid methods. The intelligent modelling approaches solve suf®ciently a very important problem ± processing of scarce, uncertainty and incomplete numerical and linguistic information about multivariate non-linear and non-stationary systems as well as biotechnological processes. The paper deals with prediction of an enzyme oxidizing uric acid to alantoin ± the uricase, produced by Candida utilis 90-12 employing neuro-fuzzy knowledgebased approach. The implemented predictive technique exploits the fact that the fuzzy model can be seen as a network structure, similar to arti®cial NN, which on computational level assure a high model accuracy. The predictors implemented are four different by nature variables. The developed predictive model shows that best predictors of uricase production are biomass and limiting substrate concentrations.

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