Recently, biotechnological opportunities have been found in non-Saccharomyces yeasts because they possess metabolic characteristics that lead to the production of compounds of interest. It has been observed that Kluyveromyces marxianus has a great potential in the production of esters, which are aromatic compounds of industrial importance. The genetic bases that govern the synthesis of esters include a large group of enzymes, among which the most important are alcohol acetyl transferases (AATases) and esterases (AEATases), and it is known that some are present in K. marxianus, because it has genetic characteristics like S. cerevisiae. It also has a physiology suitable for biotechnological use since it is the eukaryotic microorganism with the fastest growth rate and has a wide range of thermotolerance with respect to other yeasts. In this work, the enzymatic background of K. marxianus involved in the synthesis of esters is analyzed, based on the sequences reported in the NCBI database.

This paper presents results concerning mechanistic modeling to describe the dynamics and interactions between biomass growth, glucose consumption and ethanol production in batch culture fermentation by Kluyveromyces marxianus (K. marxianus). The mathematical model was formulated based on the biological assumptions underlying each variable and is given by a set of three coupled nonlinear first-order Ordinary Differential Equations. The model has ten parameters, and their values were fitted from the experimental data of 17 K. marxianus strains by means of a computational algorithm design in Matlab. The latter allowed us to determine that seven of these parameters share the same value among all the strains, while three parameters concerning biomass maximum growth rate, and ethanol production due to biomass and glucose had specific values for each strain. These values are presented with their corresponding standard error and $95\%$ confidence interval. The goodness of fit of our system was evaluated both qualitatively by in silico experimentation and quantitative by means of the coefficient of determination and the Akaike Information Criterion. Results regarding the fitting capabilities were compared with the classic model given by the logistic, Pirt, and Luedeking–Piret Equations. Further, nonlinear theories were applied to investigate local and global dynamics of the system, the Localization of Compact Invariant Sets Method was applied to determine the so-called localizing domain, i.e., lower and upper bounds for each variable; whilst Lyapunov’s stability theories allowed to establish sufficient conditions to ensure asymptotic stability in the nonnegative octant, i.e., ${\mathbf{R}}_{+,0}^3$. Finally, the predictive ability of our mechanistic model was explored through several numerical simulations with expected results according to microbiology literature on batch fermentation.