title: Stoichiometric identification with maximum likelihood principal component analysis.
abstract: This study presents an effective procedure for the determination of a biologically inspired, black-box model of cultures of microorganisms (including yeasts, bacteria, plant and animal cells) in bioreactors. This procedure is based on sets of experimental data measuring the time-evolution of a few extracellular species concentrations, and makes use of maximum likelihood principal component analysis to determine, independently of the kinetics, an appropriate number of macroscopic reactions and their stoichiometry. In addition, this paper provides a discussion of the geometric interpretation of a stoichiometric matrix and the potential equivalent reaction schemes. The procedure is carefully evaluated within the stoichiometric identification framework of the growth of the yeast Kluyveromyces marxianus on cheese whey. Using Monte Carlo studies, it is also compared with two other previously published approaches.
authors: Automatic Control Laboratory, University of Mons, 31 Boulevard Dolez, 7000, Mons, Belgium, johan.mailier@umons.ac.be.
authors: Johan
authors: J
authors: Mailier
authors: Marcel
authors: M
authors: Remy
authors: Alain
authors: A
authors: Vande Wouwer
mesh_terms: D019149:Bioreactors; D003198:Computer Simulation; D007700:Kinetics; D007716:Kluyveromyces / Q000254:growth & development*; D016013:Likelihood Functions; D008894:Milk Proteins / Q000378:metabolism*; D008954:Models, Biological; D009010:Monte Carlo Method; D025341:Principal Component Analysis / Q000379:methods*; D000067816:Whey Proteins
chemical_list: D008894:Milk Proteins; D000067816:Whey Proteins