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Stochastic Models To Study the Impact of Mixing on a Fed‐Batch Culture of Saccharomyces cerevisiae
Author(s) -
Delvigne F.,
Lejeune A.,
Destain J.,
Thonart P.
Publication year - 2006
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp050255m
Subject(s) - bioreactor , biological system , mixing (physics) , saccharomyces cerevisiae , markov chain , yeast , representation (politics) , stochastic modelling , matrix (chemical analysis) , statistical physics , dispersion (optics) , mathematics , mechanics , chemistry , physics , chromatography , biology , statistics , biochemistry , organic chemistry , optics , quantum mechanics , political science , law , politics
The mechanisms of interaction between microorganisms and their environment in a stirred bioreactor can be modeled by a stochastic approach. The procedure comprises two submodels: a classical stochastic model for the microbial cell circulation and a Markov chain model for the concentration gradient calculus. The advantage lies in the fact that the core of each submodel, i.e., the transition matrix (which contains the probabilities to shift from a perfectly mixed compartment to another in the bioreactor representation), is identical for the two cases. That means that both the particle circulation and fluid mixing process can be analyzed by use of the same modeling basis. This assumption has been validated by performing inert tracer (NaCl) and stained yeast cells dispersion experiments that have shown good agreement with simulation results. The stochastic model has been used to define a characteristic concentration profile experienced by the microorganisms during a fermentation test performed in a scale‐down reactor. The concentration profiles obtained in this way can explain the scale‐down effect in the case of a Saccharomyces cerevisiae fed‐batch process. The simulation results are analyzed in order to give some explanations about the effect of the substrate fluctuation dynamics on S. cerevisiae .