Clustering hybrid regression: a novel computational approach to study and model biohydrogen production through dark fermentation.
Clustering hybrid regression (CHR) approach was developed and evaluated using data from H(2)-producing glucose-based, suspended-cell bioreactor operated for 5 months. The aim was to describe the relationship between metabolic end products and H(2)-production rate. Self-organizing maps (SOM) were used to better visualize the dataset and to detect main metabolic patterns in bioprocess data. SOM detected three distinct metabolic patterns with butyrate, acetate and ethanol as dominant metabolites, respectively. Butyrate dominated metabolism was related to high H(2) production, while acetate and ethanol dominated metabolisms resulted in low H(2) production. CHR models performed well [mean square error (MSE) 0.55 and 0.56] in modeling the H(2)-production rate. The results validate the suitability of the CHR approach in describing the bioprocess behavior and in the modeling of H(2) production rate. The developed model can help in discovering key metabolic interactions and suitable process parameters from complex datasets, and increase the understanding of the bioprocesses occurring in engineered and natural environments.
Nikhil
Department of Signal Processing, Tampere University of Technology, Tampere, Finland. nikhil@tut.fi
Koskinen
Perttu E P
PE
Visa
Ari
A
Kaksonen
Anna H
AH
Puhakka
Jaakko A
JA
Yli-Harja
Olli
O
D000085:Acetates / Q000737:chemistry; D000465:Algorithms; D018533:Biomass; D019149:Bioreactors; D002087:Butyrates / Q000737:chemistry; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D000431:Ethanol / Q000737:chemistry; D005285:Fermentation; D005947:Glucose / Q000737:chemistry*; D006859:Hydrogen / Q000737:chemistry*; D015233:Models, Statistical; D011422:Propionates / Q000737:chemistry; D012044:Regression Analysis
D000085:Acetates; D002087:Butyrates; D011422:Propionates; D000431:Ethanol; D006859:Hydrogen; D005947:Glucose