Microbial production of 3-hydroxypropionic acid by acetic acid bacteria: Modeling including the buffering capacity of the biological medium enables prediction of pH and metabolite concentrations
2024
Arana-Agudelo, Pedro | de Fouchécour, Florence | Moussa, Marwen | Athès, Violaine | Lachin, Kevin | Spinnler, Henry Eric | Saulou-Bérion, Claire | Trelea, Ioan-Cristian | Paris-Saclay Food and Bioproduct Engineering (SayFood) ; AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Génie et Microbiologie des Procédés Alimentaires (GMPA) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | Agence Nationale de la Recherche (ANR) | ABIES Doctoral School
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Show more [+] Less [-]English. A mathematical model of the fed-batch bioconversion of 1,3-propanediol into 3-hydroxypropionic acid using acetic acid bacteria is proposed. The model includes the microbial growth, the oxidation of 1,3-propanediol to 3-hydroxypropionaldehyde followed by a second oxidation reaction to 3-hydroxypropionic acid. The inhibitoryeffect of the total acid concentration upon the biological reactions was considered as well as the effect of pH on bacterial growth. A special attention was paid to make accurate pH predictions as pH is a key parameter that influences the microbial growth and bioconversion and also defines the strategy of downstream processing foracid recovery. The buffering capacity of the complex biological medium was found to change throughout the bioconversion. In addition to describing satisfactorily a set of experiments reported in the literature, the model was successfully used to predict metabolite concentrations and the resulting pH in new operating conditions withfree pH dynamics. A sensitivity analysis was performed to identify the most influential parameters of the model. The proposed model represents a valuable tool for bioprocess design as it describes the detailed kinetics of 1,3- propanediol oxidation to 3-hydroxypropionic acid by acetic acid bacteria in bioreactor. Additionally, the pHprediction is a major feature of this model, which could guide the identification of optimal operating conditions for microbial activity with a simultaneous in-situ recovery process.
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