Construction of the fermentation kinetics model of L-isoleucine

To construct a fermentation kinetic model of L-isoleucine, generally, three aspects including cell growth, substrate consumption, and product formation need to be considered. The specific steps are as follows:

I. Cell Growth Kinetics

Select an appropriate growth model: The commonly used cell growth model is the Logistic model, which takes into account the lag phase, logarithmic growth phase, and stationary phase during the cell growth process.

Estimate the model parameters: Measure the cell concentration at different fermentation time points through experiments, and then use the nonlinear regression method to estimate the parameters of the Logistic model to obtain the values of μ_max and X_max.

II. Substrate Consumption Kinetics

Establish a substrate consumption equation: Substrate consumption is closely related to cell growth and product formation. The Monod equation is usually used to describe cell growth under substrate limitation, and then a substrate consumption kinetic model is established.

Determine the parameters: Measure the substrate concentration at different fermentation time points through experiments. Combine the cell growth data and use the parameter estimation method to determine the values of parameters such as K_s and Y_X/S.

III. Product Formation Kinetics

Select a product formation model: The formation of L-isoleucine may have different correlations with cell growth. Common product formation models include growth-associated, non-growth-associated, and partially growth-associated types.

Fit the model parameters: Measure the product concentration at different fermentation time points through experiments. Combine the cell growth data and fit the parameters of the product formation kinetic equation to obtain the values of α and β.

IV. Model Validation and Optimization

Model validation: Use independent experimental data to validate the constructed fermentation kinetic model. Compare the model prediction results with the experimental data, and evaluate the accuracy and reliability of the model by calculating indicators such as the root mean square error (RMSE) and the mean relative error (MRE).

Model optimization: According to the results of model validation, optimize and improve the model. It may be necessary to further adjust the model structure or parameters, and consider more influencing factors, such as the effects of environmental factors like pH value, temperature, and dissolved oxygen during the fermentation process on fermentation kinetics, so that the model can more accurately describe the fermentation process of L-isoleucine.

Constructing a fermentation kinetic model of L-isoleucine requires a comprehensive consideration of multiple aspects such as cell growth, substrate consumption, and product formation, and parameter estimation, model validation, and optimization through experimental data, so as to obtain a kinetic model that can accurately describe the fermentation process and provide a theoretical basis for the optimization and control of the fermentation process of L-isoleucine.