The conversion of pentose to ethanol is one of the major barriers of industrializing the lignocellulosic ethanol processes. As one of the most promising native strains for pentose fermentation, Scheffersomyces stipitis (formerly known as Pichia stipitis) has been widely studied for its xylose fermentation. In spite of the abundant experimental evidence regarding ethanol and byproducts production under various aeration conditions, the mathematical descriptions of the processes are rare. In this work, a constraint-based metabolic network model for the central carbon metabolism of S. stipitis was reconstructed by integrating genomic (S. stipitis v2.0, KEGG), biochemical (ChEBI, PubChem), and physiological information available for this microorganism and other related yeast. The model consists of the stoichiometry of metabolic reactions, biosynthetic requirements for growth, and other constraints. Flux balance analysis is applied to characterize the phenotypic behavior of S. stipitis grown on xylose. The model predictions are in good agreement with published experimental results. To understand the effect of redox balance on xylose fermentation, we propose a system identification-based metabolic analysis framework to extract biological knowledge embedded in a series of designed in silico experiments. In the proposed framework, we first design in silico experiments to perturb the metabolic network in order to investigate the interested properties and then perform system identification, whereby applying principal component analysis (PCA) to the data generated by the designed in silico experiments. By combining the in silico perturbation experiments with system identification tools, biologically meaningful information contained in the complex network structure can be decomposed and translated into easily interpretable information that is useful for biologist. The PCA analysis identifies the phenotypic changes caused by oxygen supply and reveals key metabolic reactions related to redox homeostasis in different phenotypes. In addition, the influence of the cofactor preference of key enzyme (xylose reductase) in xylose metabolism is investigated using the proposed approach, and the results provide important insights on cofactor engineering of xylose metabolism.
Genome-scale metabolic network models represent the link between the genotype and phenotype of the organism, which are usually reconstructed based on the genome sequence annotation and relevant biochemical and physiological information. These models provide a holistic view of the organism's metabolism, and constraint-based metabolic flux analysis methods have been used extensively to study genome-scale cellular metabolic networks. It is clear that the quality of the metabolic network model determines the outcome of the application. Therefore, it is critically important to determine the accuracy of a genome-scale model in describing the cellular metabolism of the modeled strain. However, because of the model complexity, which results in a system with very high degree of freedom, a good agreement between measured and computed substrate uptake rates and product secretion rates is not sufficient to guarantee the predictive capability of the model. To address this challenge, in this work we present a novel system identification based framework to extract the qualitative biological knowledge embedded in the quantitative simulation results from the metabolic network models. The extracted knowledge can serve two purposes: model validation during model development phase, which is the focus of this work, and knowledge discovery once the model is validated. This framework bridges the gap between the large amount of numerical results generated from genome-scale models and the knowledge that can be easily understood by biologists. The effectiveness of the proposed framework is demonstrated by its application to the analysis of two recently published genome-scale models of Scheffersomyces stipitis.
BackgroundScheffersomyces stipitis is an important yeast species in the field of biorenewables due to its desired capacity for xylose utilization. It has been recognized that redox balance plays a critical role in S. stipitis due to the different cofactor preferences in xylose assimilation pathway. However, there has not been any systems level understanding on how the shift in redox balance contributes to the overall metabolic shift in S. stipitis to cope with reduced oxygen uptake. Genome-scale metabolic network models (GEMs) offer the opportunity to gain such systems level understanding; however, currently the two published GEMs for S. stipitis cannot be used for this purpose, as neither of them is able to capture the strain’s fermentative metabolism reasonably well due to their poor prediction of xylitol production, a key by-product under oxygen limited conditions.ResultsA system identification-based (SID-based) framework that we previously developed for GEM validation is expanded and applied to refine a published GEM for S. stipitis, iBB814. After the modified GEM, named iDH814, was validated using literature data, it is used to obtain genome-scale understanding on how redox cofactor shifts when cells respond to reduced oxygen supply. The SID-based framework for GEM analysis was applied to examine how the environmental perturbation (i.e., reduced oxygen supply) propagates through the metabolic network, and key reactions that contribute to the shifts of redox and metabolic state were identified. Finally, the findings obtained through GEM analysis were validated using transcriptomic data.ConclusionsiDH814, the modified model, was shown to offer significantly improved performance in terms of matching available experimental results and better capturing available knowledge on the organism. More importantly, our analysis based on iDH814 provides the first genome-scale understanding on how redox balance in S. stipitis was shifted as a result of reduced oxygen supply. The systems level analysis identified the key contributors to the overall metabolic state shift, which were validated using transcriptomic data. The analysis confirmed that S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that directs the overall shift in metabolic states.Electronic supplementary materialThe online version of this article (10.1186/s12934-018-0983-y) contains supplementary material, which is available to authorized users.
An accurate measurement or estimation of the volumetric mass transfer coefficient kL a is crucial for the design, operation, and scale up of bioreactors. Among different physical and chemical methods, the classical dynamic method is the most widely applied method to simultaneously estimate both kL a and cell's oxygen utilization rate. Despite several important follow-up articles to improve the original dynamic method, some limitations exist that make the classical dynamic method less effective under certain conditions. For example, for the case of high cell density with moderate agitation, the dissolved oxygen concentration barely increases during the re-gassing step of the classical dynamic method, which makes kL a estimation impossible. To address these limitations, in this work we present an improved dynamic method that consists of both an improved model and an improved procedure. The improved model takes into account the mass transfer between the headspace and the broth; in addition, nitrogen is bubbled through the broth when air is shut off. The improved method not only enables a faster and more accurate estimation of kL a, but also allows the measurement of kL a for high cell density with medium/low agitation that is impossible with the classical dynamic method. Scheffersomyces stipitis was used as the model system to demonstrate the effectiveness of the improved method; in addition, experiments were conducted to examine the effect of cell density and agitation speed on kL a.
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