Metabolic modeling for predicting VFA production from protein‐rich substrates by mixed‐culture fermentation. Issue 1 (6th October 2019)

• Record Type: Journal Article
• Authors: Regueira, Alberte; Lema, Juan M.; Carballa, Marta; Mauricio‐Iglesias, Miguel
• Is Part Of: Biotechnology and bioengineering. Volume 117:Issue 1(2020)
• Page Start: 73
• Page End: 84
• Publication Date: 2019-10-06
• Subjects: anaerobic protein degradation -- metabolic modeling -- mixed cultures -- process design -- volatile fatty acids production; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.27177
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: Proteinaceous organic wastes are suitable substrates to produce high added‐value products in anaerobic mixed‐culture fermentations. In these processes, the stoichiometry of the biotransformation depends highly on operational conditions such as pH or feeding characteristics and there are still no tools that allow the process to be directed toward those products of interest. Indeed, the lack of product selectivity strongly limits the potential industrial development of these bioprocesses. In this work, we developed a mathematical metabolic model for the production of volatile fatty acids from protein‐rich wastes. In particular, the effect of pH on the product yields is analyzed and, for the first time, the observed changes are mechanistically explained. The model reproduces experimental results at both neutral and acidic pH and it is also capable of predicting the tendencies in product yields observed with a pH drop. It also offers mechanistic insights into the interaction among the different amino acids (AAs) of a particular protein and how an AA might yield different products depending on the relative abundance of other AAs. Particular emphasis is placed on the utility of this mathematical model as a process design tool and different examples are given on how to use the model for this purpose. Abstract : This work presents a mechanistic model that predicts accurately the volatile fatty acid production from proteins and evaluates correctly the effect of pH, offering at the same time mechanistic insight into the predicted changes in product spectrum. This model provides thus an excellent tool for the early‐stage design of processes with a high selectivity on the desired volatile fatty acids.