The Experts below are selected from a list of 54051 Experts worldwide ranked by ideXlab platform
Jason A. Papin - One of the best experts on this subject based on the ideXlab platform.
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Medusa: Software to build and analyze ensembles of genome-scale Metabolic Network reconstructions.
PLoS computational biology, 2020Co-Authors: Gregory L. Medlock, Thomas J. Moutinho, Jason A. PapinAbstract:Uncertainty in the structure and parameters of Networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of Metabolic Networks, this uncertainty is present both during the reconstruction of Networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale Metabolic Network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale Metabolic Network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org) and from github (https://github.com/opencobra/Medusa), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest.
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Medusa: software to build and analyze ensembles of genome-scale Metabolic Network reconstructions
2019Co-Authors: Gregory L. Medlock, Jason A. PapinAbstract:Abstract Uncertainty in the structure and parameters of Networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of Metabolic Networks, this uncertainty is present both during the reconstruction of Networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale Metabolic Network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles, perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale Metabolic Network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org/) and from github (https://github.com/gregmedlock/Medusa/), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest/.
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Biomedical applications of genome-scale Metabolic Network reconstructions of human pathogens.
Current Opinion in Biotechnology, 2018Co-Authors: Laura J. Dunphy, Jason A. PapinAbstract:The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale Metabolic Network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale Metabolic Network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.
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Metabolic Network modeling of microbial communities
Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2015Co-Authors: Matthew B. Biggs, Gregory L. Medlock, Glynis L. Kolling, Jason A. PapinAbstract:Microbial communities represent a gargantuan force of nature that exerts influence on global geochemical cycles1, agriculture2, human health3, food preparation4, and a host of relevant aspects of life on earth5,6. Traditional microbiology has made great strides over the last century in describing and categorizing these microscopic neighbors. More recently, advances in sequencing technologies have provided the first glimpses at the composition of natural microbial communities, including insights into the physiology of non-culturable microbes7. Databases are filling with mountains of genomic fragments, gene and protein expression data, and other such large-scale “–omics” information, all describing the content of diverse microbial communities8,9. Despite the plethora of data, we yet lack true understanding of the mechanisms that cause communities to function and interact with their environments10. Considering the importance of microbial communities to many global ecosystems, health, and various industries, there is a great need to move beyond a descriptive ‘parts list’ approach of the field, and transition to more functional, predictive models of microbial community structure and function. Predictive community models have the potential to engender many beneficial technologies including: rational probiotic design for restoring a diseased intestinal microbiota11, efficient chemical-producing consortia12, or optimal bioremediation communities13. Furthermore, predictive models will allow novel exploration of basic questions in microbial ecology14,15, leading to new insights into the development and evolution of microbial communities10 (Figure 1). All of these potential applications will require improvements in the mathematical toolbox used to represent biochemical Networks and their interactions. Figure 1 There are many aspects of life in a microbial community that would be useful to capture using mathematical models Genome-scale Metabolic Network reconstructions (GENREs) have been successfully applied to the representation, study, and engineering of single microbes16 (Figure 2). The last decade has seen extensive tool development for the analysis of models encompassing single strains up to complex microbial communities10,17–22. Since the first published community model in 2007 of a mutualistic microbial community, the accumulating body of work has highlighted many unique challenges related to microbial community modeling23. In this review, we discuss the existing frameworks that have been developed using GENREs for community analysis (Figure 3 and Table 1), the types of questions that can be addressed, and challenges in the field that present opportunities for progress. Figure 2 A simple workflow for genome-scale Metabolic Network reconstruction and accompanying constraint-based analysis Figure 3 Community modeling frameworks that feature GENREs Table 1 A timeline for computational Metabolic systems biology of microbial communities
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Metabolic Network modeling of microbial communities
Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2015Co-Authors: Matthew B. Biggs, Gregory L. Medlock, Glynis L. Kolling, Jason A. PapinAbstract:Microbial communities represent a gargantuan force of nature that exerts influence on global geochemical cycles1, agriculture2, human health3, food preparation4, and a host of relevant aspects of life on earth5,6. Traditional microbiology has made great strides over the last century in describing and categorizing these microscopic neighbors. More recently, advances in sequencing technologies have provided the first glimpses at the composition of natural microbial communities, including insights into the physiology of non-culturable microbes7. Databases are filling with mountains of genomic fragments, gene and protein expression data, and other such large-scale “–omics” information, all describing the content of diverse microbial communities8,9. Despite the plethora of data, we yet lack true understanding of the mechanisms that cause communities to function and interact with their environments10. Considering the importance of microbial communities to many global ecosystems, health, and various industries, there is a great need to move beyond a descriptive ‘parts list’ approach of the field, and transition to more functional, predictive models of microbial community structure and function. Predictive community models have the potential to engender many beneficial technologies including: rational probiotic design for restoring a diseased intestinal microbiota11, efficient chemical-producing consortia12, or optimal bioremediation communities13. Furthermore, predictive models will allow novel exploration of basic questions in microbial ecology14,15, leading to new insights into the development and evolution of microbial communities10 (Figure 1). All of these potential applications will require improvements in the mathematical toolbox used to represent biochemical Networks and their interactions. Figure 1 There are many aspects of life in a microbial community that would be useful to capture using mathematical models Genome-scale Metabolic Network reconstructions (GENREs) have been successfully applied to the representation, study, and engineering of single microbes16 (Figure 2). The last decade has seen extensive tool development for the analysis of models encompassing single strains up to complex microbial communities10,17–22. Since the first published community model in 2007 of a mutualistic microbial community, the accumulating body of work has highlighted many unique challenges related to microbial community modeling23. In this review, we discuss the existing frameworks that have been developed using GENREs for community analysis (Figure 3 and Table 1), the types of questions that can be addressed, and challenges in the field that present opportunities for progress. Figure 2 A simple workflow for genome-scale Metabolic Network reconstruction and accompanying constraint-based analysis Figure 3 Community modeling frameworks that feature GENREs Table 1 A timeline for computational Metabolic systems biology of microbial communities
Jin Wang - One of the best experts on this subject based on the ideXlab platform.
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comprehensive evaluation of two genome scale Metabolic Network models for scheffersomyces stipitis
Biotechnology and Bioengineering, 2015Co-Authors: Andrew Damiani, Thomas W. Jeffries, Jin WangAbstract: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. Biotechnol. Bioeng. 2015;112: 1250–1262. © 2015 Wiley Periodicals, Inc.
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Comprehensive evaluation of two genome‐scale Metabolic Network models for Scheffersomyces stipitis
Biotechnology and bioengineering, 2015Co-Authors: Andrew Damiani, Thomas W. Jeffries, Jin WangAbstract: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. Biotechnol. Bioeng. 2015;112: 1250–1262. © 2015 Wiley Periodicals, Inc.
Lars K. Nielsen - One of the best experts on this subject based on the ideXlab platform.
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aragem a genome scale reconstruction of the primary Metabolic Network in arabidopsis
Plant Physiology, 2010Co-Authors: Cristiana Gomes De Oliveira Dalmolin, Lake-ee Quek, Robin W. Palfreyman, Stevens M. Brumbley, Lars K. NielsenAbstract:Genome-scale Metabolic Network models have been successfully used to describe metabolism in a variety of microbial organisms as well as specific mammalian cell types and organelles. This systems-based framework enables the exploration of global phenotypic effects of gene knockouts, gene insertion, and up-regulation of gene expression. We have developed a genome-scale Metabolic Network model (AraGEM) covering primary metabolism for a compartmentalized plant cell based on the Arabidopsis (Arabidopsis thaliana) genome. AraGEM is a comprehensive literature-based, genome-scale Metabolic reconstruction that accounts for the functions of 1,419 unique open reading frames, 1,748 metabolites, 5,253 gene-enzyme reaction-association entries, and 1,567 unique reactions compartmentalized into the cytoplasm, mitochondrion, plastid, peroxisome, and vacuole. The curation process identified 75 essential reactions with respective enzyme associations not assigned to any particular gene in the Kyoto Encyclopedia of Genes and Genomes or AraCyc. With the addition of these reactions, AraGEM describes a functional primary metabolism of Arabidopsis. The reconstructed Network was transformed into an in silico Metabolic flux model of plant metabolism and validated through the simulation of plant Metabolic functions inferred from the literature. Using efficient resource utilization as the optimality criterion, AraGEM predicted the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism.
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AraGEM, a Genome-Scale Reconstruction of the Primary Metabolic Network in Arabidopsis
Plant Physiology, 2009Co-Authors: Cristiana Gomes De Oliveira Dal’molin, Lake-ee Quek, Robin W. Palfreyman, Stevens M. Brumbley, Lars K. NielsenAbstract:Genome-scale Metabolic Network models have been successfully used to describe metabolism in a variety of microbial organisms as well as specific mammalian cell types and organelles. This systems-based framework enables the exploration of global phenotypic effects of gene knockouts, gene insertion, and up-regulation of gene expression. We have developed a genome-scale Metabolic Network model (AraGEM) covering primary metabolism for a compartmentalized plant cell based on the Arabidopsis (Arabidopsis thaliana) genome. AraGEM is a comprehensive literature-based, genome-scale Metabolic reconstruction that accounts for the functions of 1,419 unique open reading frames, 1,748 metabolites, 5,253 gene-enzyme reaction-association entries, and 1,567 unique reactions compartmentalized into the cytoplasm, mitochondrion, plastid, peroxisome, and vacuole. The curation process identified 75 essential reactions with respective enzyme associations not assigned to any particular gene in the Kyoto Encyclopedia of Genes and Genomes or AraCyc. With the addition of these reactions, AraGEM describes a functional primary metabolism of Arabidopsis. The reconstructed Network was transformed into an in silico Metabolic flux model of plant metabolism and validated through the simulation of plant Metabolic functions inferred from the literature. Using efficient resource utilization as the optimality criterion, AraGEM predicted the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism.
Jens Nielsen - One of the best experts on this subject based on the ideXlab platform.
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Principles of optimal Metabolic Network operation
Molecular systems biology, 2007Co-Authors: Jens NielsenAbstract:Mol Syst Biol. 3: 126 What is the optimal operation of Metabolic Networks? This question is interesting to answer, as it would provide information on which underlying principles have shaped Metabolic Networks during evolution, and it may allow us to identify some simple rules governing the operation of Metabolic Networks under different growth conditions. Such rules might be important for Metabolic Network engineering—for example when designing better microbes for the production of fuels, chemicals and materials—but also for the identification of the regulatory mechanisms that ensure the different operations of metabolism. Using reconstructed genome‐scale Metabolic models, the group of Palsson has shown on several occasions that a well suited guiding principle—the so‐called ‘objective function’ in the terminology used in Metabolic flux balance analysis—for operation of Metabolic Networks is optimization of growth (Price et al , 2004). In other words, through evolution, microorganisms have evolved in such a way that their Metabolic Networks ensure the most efficient conversion of carbon and energy to produce more cells. This principle seems to be robust as it was elegantly illustrated in a study where the group looked at growth of the bacterium Escherichia coli …
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uncovering transcriptional regulation of metabolism by using Metabolic Network topology
Proceedings of the National Academy of Sciences of the United States of America, 2005Co-Authors: Kiran Raosaheb Patil, Jens NielsenAbstract:Cellular response to genetic and environmental perturbations is often reflected and/or mediated through changes in the metabolism, because the latter plays a key role in providing Gibbs free energy and precursors for biosynthesis. Such Metabolic changes are often exerted through transcriptional changes induced by complex regulatory mechanisms coordinating the activity of different Metabolic pathways. It is difficult to map such global transcriptional responses by using traditional methods, because many genes in the Metabolic Network have relatively small changes at their transcription level. We therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of Metabolic Networks. By using information on the Metabolic Network topology from genome-scale Metabolic reconstruction, we show that it is possible to reveal patterns in the Metabolic Network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic or environmental perturbations. We find that cells respond to perturbations by changing the expression pattern of several genes involved in the specific part(s) of the metabolism in which a perturbation is introduced. These changes then are propagated through the Metabolic Network because of the highly connected nature of metabolism.
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genome scale reconstruction of the saccharomyces cerevisiae Metabolic Network
Genome Research, 2003Co-Authors: Jochen Forster, Bernhard O. Palsson, Iman Famili, Jens NielsenAbstract:Baker's yeast, Saccharomyces cerevisiae, was the first eukaryotic genome that was fully sequenced, annotated, and made publicly available. (Goffeau 1997). Along with its industrial importance, S. cerevisiae serves as a model organism for understanding and engineering eukaryotic cell function (Dujon 1996; Botstein et al. 1997). There have been many studies aiming to unravel the function of orphan genes in the genome (Oliver 1998; Entian et al. 1999; Winzeler et al. 1999; Hughes et al. 2000), and various functional genomics techniques were first implemented in S. cerevisiae. The first genome-wide cDNA array study was designed for S. cerevisiae (DeRisi et al. 1997), which subsequently resulted in a large number of studies on expression profiling (Hughes et al. 2000). Large-scale studies have been conducted to investigate the protein–protein interactions (Uetz et al. 2000), including the use of two-hybrid systems (Ito et al. 2001). These studies and a large body of biochemical literature now enable us to functionally integrate the wealth of available genetic, molecular, and biochemical information for S. cerevisiae. Integration of knowledge at different levels in the cascade from genes to protein and further to Metabolic fluxes in a genome-scale Network will be pivotal for understanding how the individual components in the system interact and influence overall cell function. The approach of analyzing a complex process at different levels was illustrated in a recent study in which expression profiles in different mutants were compared with protein levels in order to unravel the structure of the complex galactose (GAL)–regulon (Ideker et al. 2001). This coordinated and multilevel effort may have significant influence on designing Metabolic engineering strategies (Ostergaard et al. 2000a,b). These interactions must now be quantified through the use of a mathematical framework—something that involves a significant research effort, but which is believed to lead to fundamental new insights into cellular function (Schilling et al. 1999; Endy and Brent 2001). To gain insight into cell synthesis and the Metabolic capability through mathematical modeling, a natural first step is to reconstruct the underlying Metabolic Network, as this is responsible for the synthesis capacity of the cell, and, as well, it allows detailed analysis of the interactions between the individual pathways functioning in the cell. Recently, genome-scale Metabolic Networks were reconstructed for prokaryotic cells (Edwards and Palsson 1999; Covert et al. 2001), and it was demonstrated how such reconstructed Metabolic models allow direct correlation between the genomic information and Metabolic activity at the flux level. In these reconstructed Metabolic Networks, which consist of several hundred reactions and several hundred metabolites, it was possible to simulate the phenotypic behavior under different genetic conditions and physiological environments (Edwards et al. 2001). Here, we present the reconstruction of the Metabolic Network of S. cerevisiae, the first genome-scale in silico Metabolic Network for a eukaryotic cell. Characteristics of eukaryotic cells, such as compartmentation of reactions and involvement of transport steps across cellular membranes, were considered in the Network. The structure and Metabolic capabilities of the Metabolic Network of S. cerevisiae were compared with a genome-scale reconstructed Metabolic Network of Escherichia coli (Edwards and Palsson 2000).
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Metabolic Network analysis. A powerful tool in Metabolic engineering.
Advances in biochemical engineering biotechnology, 2000Co-Authors: Bjarke Bak Christensen, Jens NielsenAbstract:Metabolic Network analysis is a tool for investigating the features that identify the topology of a Metabolic Network and the relative activities of its individual branches. The pillars of Metabolic Network analysis are mathematical modeling, allowing for a quantitative analysis, biochemical knowledge of, for example, reaction stoichiometry, and the experimental techniques, providing input for the modeling part. The modeling part includes metabolite balancing, usually the basis for Metabolic flux analysis, and isotope balancing. Isotope balancing can be used for both identification of active pathways and for estimation of the relative fluxes through two pathways leading to the same metabolite, aspects that are difficult to investigate using metabolite balancing. The combination of metabolite and isotope balancing is very powerful and constitutes the basis of Metabolic Network analysis. With the main focus being on investigating the Metabolic Network structure, this review describes how central Metabolic features, for example, pathway identification, flux distribution, and compartmentation, can be addressed using a combination of metabolite balancing and labeling experiments.
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Metabolic Network Analysis
Bioanalysis and Biosensors for Bioprocess Monitoring, 1999Co-Authors: Bjarke Bak Christensen, Jens NielsenAbstract:Metabolic Network analysis is a tool for investigating the features that identify the topology of a Metabolic Network and the relative activities of its individual branches. The pillars of Metabolic Network analysis are mathematical modeling, allowing for a quantitative analysis, biochemical knowledge of, for example, reaction stoichiometry, and the experimental techniques, providing input for the modeling part. The modeling part includes metabolite balancing, usually the basis for Metabolic flux analysis, and isotope balancing. Isotope balancing can be used for both identification of active pathways and for estimation of the relative fluxes through two pathways leading to the same metabolite, aspects that are difficult to investigate using metabolite balancing. The combination of metabolite and isotope balancing is very powerful and constitutes the basis of Metabolic Network analysis. With the main focus being on investigating the Metabolic Network structure, this review describes how central Metabolic features, for example, pathway identification, flux distribution, and compartmentation, can be addressed using a combination of metabolite balancing and labeling experiments.
Andrew Damiani - One of the best experts on this subject based on the ideXlab platform.
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comprehensive evaluation of two genome scale Metabolic Network models for scheffersomyces stipitis
Biotechnology and Bioengineering, 2015Co-Authors: Andrew Damiani, Thomas W. Jeffries, Jin WangAbstract: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. Biotechnol. Bioeng. 2015;112: 1250–1262. © 2015 Wiley Periodicals, Inc.
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Comprehensive evaluation of two genome‐scale Metabolic Network models for Scheffersomyces stipitis
Biotechnology and bioengineering, 2015Co-Authors: Andrew Damiani, Thomas W. Jeffries, Jin WangAbstract: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. Biotechnol. Bioeng. 2015;112: 1250–1262. © 2015 Wiley Periodicals, Inc.