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Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have rarely aided in the development of novel yeast applications. The compartmentation of eukaryotic cells adds to model complexity. Model constraints are ideally based on biochemical evidence, which is rarely available for non-conventional yeast and eukaryotic cells. A small-scale model for 13C-based metabolic flux analysis of central yeast carbon metabolism was developed that is universally valid and does not depend on localization information regarding amino acid anabolism. The variable compartmental origin of traced metabolites is a feature that allows application of the model to yeasts with uncertain genomic and transcriptional backgrounds. The presented test case includes the baker's yeast Saccharomyces cerevisiae and the methylotrophic yeast Hansenula polymorpha. Highly similar flux solutions were computed using either a model with undefined pathway localization or a model with constraints based on curated (S. cerevisiae) or computationally predicted (H. polymorpha) localization information, while false solutions were found with incorrect localization constraints. These results indicate a potentially adverse effect of universally assuming Saccharomyces-like constraints on amino acid biosynthesis for non-conventional yeasts and verify the validity of neglecting compartmentation constraints using a small-scale metabolic model. The model was specifically designed to investigate the intracellular metabolism of wild-type yeasts under various growth conditions but is also expected to be useful for computing fluxes of other eukaryotic cells.

Microbial carbon dioxide assimilation and conversion to chemical platform molecules has the potential to be developed as economic, sustainable processes. The carbon dioxide assimilation can proceed by a variety of natural pathways and recently even synthetic CO2 fixation routes have been designed. Early assessment of the performance of the different carbon fixation alternatives within biotechnological processes is desirable to evaluate their potential. Here we applied stoichiometric metabolic modeling based on physiological and process data to evaluate different process variants for the conversion of C1 carbon compounds to the industrial relevant platform chemical succinic acid. We computationally analyzed the performance of cyanobacteria, acetogens, methylotrophs, and synthetic CO2 fixation pathways in Saccharomyces cerevisiae in terms of production rates, product yields, and the optimization potential. This analysis provided insight into the economic feasibility and allowed to estimate the future industrial applicability by estimating overall production costs. With reported, or estimated data of engineered or wild type strains, none of the simulated microbial succinate production processes showed a performance allowing competitive production. The main limiting factors were identified as gas and photon transfer and metabolic activities whereas metabolic network structure was not restricting. In simulations with optimized parameters most process alternatives reached economically interesting values, hence, represent promising alternatives to sugar-based fermentations. Keywords: Carbon fixation, Metabolic engineering, Flux Balance Analysis, Succinic acid, Profitability, C1 carbon source