Statistical reconciliation of the elemental and molecular biomass composition of Saccharomyces cerevisiae

• Published in: Biotechnology and bioengineering 2001-11, Vol.75 (3), p.334-344
• Main Authors: Lange, H. C., Heijnen, J. J.
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Biomass; biomass composition; Biotechnology; elemental balances; equality relations; Fundamental and applied biological sciences. Psychology; Methods. Procedures. Technologies; Others; Saccharomyces cerevisiae - chemistry; statistical reconciliation method; Various methods and equipments; Water - analysis
• ISSN: 0006-3592; 1097-0290
• DOI: 10.1002/bit.10054

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. © 2001 John Wiley & Sons Inc. Biotechnol and Bioeng 75: 334–344, 2001.