Modeling and Simulation of Batch Sugarcane Alcoholic Fermentation Using the Metabolic Model

The present work sought to implement a model different from the more traditional ones for the fermentation process of ethanol production by the action of the fungus Saccharomyces cerevisiae, using a relevant metabolic network based on the glycolytic Embden-Meyerhof-Parnas route, also called EMP. We developed two models to represent this phenomenon. In the first model, we used the simple and unbranched EMP route, with a constant concentration of microorganisms throughout the process and glucose as the whole substrate. We called this first model SR, regarding the Portuguese name sem ramificacoes, which means no branches. We developed the second model by simply adding some branches to the SR model. We called this model CR, regarding the Portuguese name com ramificacoes, which means with branches. Both models were implemented in MATLAB(TM) software considering a constant temperature equal to 32 degrees C, similar to that practiced in sugar and ethanol plants, and a wide range of substrate concentrations, ranging from 30 to 100 g/L, and all the enzymes necessary for fermentation were already expressed in the cells so all the enzymes showed a constant concentration throughout the fermentation. The addition of common branches to the EMP route resulted in a considerable improvement in the results, especially predicting ethanol production closer to what we saw experimentally. Therefore, the results obtained are promising, making adjustments consistent with experimental data, meaning that all the models proposed here are a good basis for the development of future metabolic models of discontinuous fermentative processes.

Automated summary

This study developed two models to simulate the fermentation process of ethanol production using the glycolytic Embden-Meyerhof-Parnas route in Saccharomyces cerevisiae. The addition of branches to the metabolic network improved the prediction of ethanol production, bringing it closer to experimental results.