A systematic mathematical procedure capable of detecting the presence of a gross error in the measurements and of reconciling connected data sets by using the maximum likelihood principle is applied to the biomass composition data of yeast. The biomass composition of Saccharomyces cerevisiae grown in a chemostat under glucose limitation was analyzed for its elemental and for its molecular composition. Both descriptions initially resulted in conflicting results concerning the elemental composition, molecular weight, and degrees of reduction. The application of the statistical reconciliation method, based on elemental balances and equality relations, is used to obtain a consistent biomass composition. Simultaneously, the error margins of the data sets are significantly reduced in the reconciliation process. On the basis of statistical analysis it was found that inclusion of about 4% water in the list of biomass constituents is essential to adequately describe the dry biomass and match both set of measurements. The reconciled carbon content of the biomass varied 4% from the ones obtained from the molecular analysis. The proposed method increases the accuracy of biomass composition data of its elements and its molecules by providing a best estimate based on all available data and thus provides an improved and consistent basis for metabolic flux analysis as well as black box modeling approaches.@en

In vivo kinetics of Saccharomyces cerevisiae are studied, in a time window of 150 s, by analyzing the response of O2, and CO2, in the fermentor off-gas after perturbation of chemostat cultures by metabolite pulses. Here, a new mathematical method is presented for the estimation of the in vivo oxygen uptake rate (OUR) and carbon dioxide evolution rate (CER) directly from the off-gas data in such perturbation experiments. The mathematical construction allows effective elimination of delay and distortion in the off-gas measurement signal under highly dynamic conditions. A black box model for the fermentor off-gas system is first obtained by system identification, followed by the construction of an optimal linear filter, based on the identified off-gas model. The method is applied to glucose and ethanol pulses performed on chemostat cultures of S. cerevisiae. The estimated OUR is shown to be consistent with the independent dissolved oxygen measurement. The estimated in vivo OUR and CER provide valuable insights into the complex dynamic behavior of yeast and are essential for the establishment and validation of in vivo kinetic models of primary metabolism.@en