4.6 Article

Metabolic modeling for predicting VFA production from protein-rich substrates by mixed-culture fermentation

Journal

BIOTECHNOLOGY AND BIOENGINEERING
Volume 117, Issue 1, Pages 73-84

Publisher

WILEY
DOI: 10.1002/bit.27177

Keywords

anaerobic protein degradation; metabolic modeling; mixed cultures; process design; volatile fatty acids production

Funding

  1. Ministerio de Economia y Competitividad [PCIN-2016-102]
  2. Ministerio de Educacion y Formacion Profesional [FPU14/05457]

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available