Comprehensive evaluation of two genome-scale metabolic network models forScheffersomyces stipitis

2015 ◽  
Vol 112 (6) ◽  
pp. 1250-1262 ◽  
Author(s):  
Andrew L. Damiani ◽  
Q. Peter He ◽  
Thomas W. Jeffries ◽  
Jin Wang
Keyword(s):  
Metabolic Network ◽  
Comprehensive Evaluation ◽  
Network Models ◽  
Genome Scale

scholarly journals Identification of Genome-Scale Metabolic Network Models Using Experimentally Measured Flux Profiles

2006 ◽  
Vol 2 (7) ◽  
pp. e72 ◽  
Author(s):  
Markus J Herrgård ◽  
Stephen S Fong ◽  
Bernhard Ø Palsson
Keyword(s):  
Metabolic Network ◽  
Network Models ◽  
Measured Flux ◽  
Genome Scale

Comparison of different approaches for identifying subnetworks in metabolic networks

2017 ◽  
Vol 15 (06) ◽  
pp. 1750025 ◽  
Author(s):  
Abolfazl Rezvan ◽  
Changiz Eslahchi
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Comprehensive Evaluation ◽  
Network Models ◽  
Evaluation Framework ◽  
System Level ◽  
Computational Framework ◽  
Kegg Pathways ◽  
A Cell ◽  
Single Method

A metabolic network model provides a computational framework for studying the metabolism of a cell at the system level. The organization of metabolic networks has been investigated in different studies. One of the organization aspects considered in these studies is the decomposition of a metabolic network. The decompositions produced by different methods are very different and there is no comprehensive evaluation framework to compare the results with each other. In this study, these methods are reviewed and compared in the first place. Then they are applied to six different metabolic network models and the results are evaluated and compared based on two existing and two newly proposed criteria. Results show that no single method can beat others in all criteria but it seems that the methods introduced by Guimera and Amaral and Verwoerd do better on among metabolite-based methods and the method introduced by Sridharan et al. does better among reaction-based ones. Also, the methods are applied to several artificial networks, each constructed from merging a few KEGG pathways. Then, their capability to recover those pathways are compared. Results show that among metabolite-based methods, the method of Guimera and Amaral does better again, however, no notable difference between the performances of reaction-based methods was detected.

scholarly journals Identification of Genome-Scale Metabolic Network Models Using Experimentally Measured Flux Profiles

2005 ◽  
Vol preprint (2006) ◽  
pp. e72
Author(s):  
Markus J Herrgard ◽  
Stephen S. Fong ◽  
Bernhard O. Palsson
Keyword(s):  
Metabolic Network ◽  
Network Models ◽  
Measured Flux ◽  
Genome Scale

scholarly journals Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

2019 ◽  
Author(s):  
Matthew L. Jenior ◽  
Thomas J. Moutinho ◽  
Bonnie V. Dougherty ◽  
Jason A. Papin
Keyword(s):  
Escherichia Coli ◽  
Metabolic Network ◽  
Network Models ◽  
Growth Conditions ◽  
Flux Analysis ◽  
Bacterial Physiology ◽  
Context Specific ◽  
K 12 ◽  
Genome Scale

AbstractThe metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.Author SummaryTranscriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.

scholarly journals Data-driven integration of genome-scale regulatory and metabolic network models

2015 ◽  
Vol 6 ◽  
Author(s):  
Saheed Imam ◽  
Sascha Schäuble ◽  
Aaron N. Brooks ◽  
Nitin S. Baliga ◽  
Nathan D. Price
Keyword(s):  
Metabolic Network ◽  
Network Models ◽  
Data Driven ◽  
Genome Scale
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