Comprehensive Evaluation of Two Genome-Scale Metabolic Network Models for Scheffersomyces Stipitis

被引:17
作者
Damiani, Andrew L. [1 ]
He, Q. Peter [2 ]
Jeffries, Thomas W. [3 ]
Wang, Jin [1 ]
机构
[1] Auburn Univ, Dept Chem Engn, Auburn, AL 36849 USA
[2] Tuskegee Univ, Dept Chem Engn, Auburn, AL USA
[3] Univ Wisconsin, Dept Bacteriol, Madison, WI 53706 USA
基金
美国国家科学基金会;
关键词
Scheffersomyces stipitis; metabolic network models; genome-scale; flux balance analysis; system identification; principal component analysis; YEAST PICHIA-STIPITIS; PHASE PLANE ANALYSIS; SACCHAROMYCES-CEREVISIAE; XYLOSE FERMENTATION; ETHANOL-PRODUCTION; PHENOTYPE; RECONSTRUCTION; RESPIRATION; CULTURES; GROWTH;
D O I
10.1002/bit.25535
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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 presenta 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. (C) 2015 Wiley Periodicals, Inc.
引用
收藏
页码:1250 / 1262
页数:13
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