Modeling of lactose enzymatic hydrolysis using Monte Carlo method

Background: Mathematical modeling is useful in the analysis, prediction, and optimization of an enzymatic process. Unlike the conventional modeling methods, Monte Carlo method has special advantages in providing representations of the molecule's spatial distribution. However, thus far, Monte Carlo modeling of enzymatic system is namely based on unimolecular basis, not suitable for practical applications. In this research, Monte Carlo modeling is performed for enzymatic hydrolysis of lactose for the purpose of real-time applications. Results: The enzyme hydrolysis of lactose, which is conformed to Michaelis­Menten kinetics, is modeled using the Monte Carlo modeling method, and the simulation results prove that the model predicts the reaction kinetics very well. Conclusions: Monte Carlo modeling method can be used to model enzymatic reactions in a simple way for real-time applications.

Analysis of batch and repeated fedbatch productions of Candida utilis cell mass using mathematical modeling method

Background: Candida utilis is widely used in bioindustry, and its cell mass needs to be produced in a cost effective way. Process optimization based on the experimental results is the major way to reduce the production cost. However, this process is expensive, time consuming and labor intensive. Mathematical modeling is a useful tool for process analysis and optimization. Furthermore, sufficient information can be obtained with fewer experiments by using the mathematical modeling, and some results can be predicted even without doing experiments. Results: In the present study, we performed the mathematical modeling and simulation for the cell mass production of Candida utilis based on limited batch and repeated fedbatch experiments. The model parameters were optimized using genetic algorithm (GA), and the processes were analyzed. Conclusions: Taken together, this newly developed method is efficient, labor saving and cost effective.