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Metabolic Network Reconstruction and Modeling

Overview of attention for book
Cover of 'Metabolic Network Reconstruction and Modeling'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Reconstructing High-Quality Large-Scale Metabolic Models with merlin
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    Chapter 2 Analyzing and Designing Cell Factories with OptFlux
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    Chapter 3 The MONGOOSE Rational Arithmetic Toolbox
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    Chapter 4 The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models
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    Chapter 5 Reconstruction and Analysis of Central Metabolism in Microbes
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    Chapter 6 Using PSAMM for the Curation and Analysis of Genome-Scale Metabolic Models
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    Chapter 7 Integration of Comparative Genomics with Genome-Scale Metabolic Modeling to Investigate Strain-Specific Phenotypical Differences
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    Chapter 8 Template-Assisted Metabolic Reconstruction and Assembly of Hybrid Bacterial Models
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    Chapter 9 Integrated Host-Pathogen Metabolic Reconstructions
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    Chapter 10 Metabolic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem
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    Chapter 11 RNA Sequencing and Analysis in Microorganisms for Metabolic Network Reconstruction
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    Chapter 12 Differential Proteomics Based on 2D-Difference In-Gel Electrophoresis and Tandem Mass Spectrometry for the Elucidation of Biological Processes in Antibiotic-Producer Bacterial Strains
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    Chapter 13 Techniques for Large-Scale Bacterial Genome Manipulation and Characterization of the Mutants with Respect to In Silico Metabolic Reconstructions
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    Chapter 14 Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL
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    Chapter 15 Coupling Fluxes, Enzymes, and Regulation in Genome-Scale Metabolic Models
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    Chapter 16 Dynamic Flux Balance Analysis Using DFBAlab
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    Chapter 17 Designing Optimized Production Hosts by Metabolic Modeling
  19. Altmetric Badge
    Chapter 18 Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives
Attention for Chapter 5: Reconstruction and Analysis of Central Metabolism in Microbes
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Chapter title
Reconstruction and Analysis of Central Metabolism in Microbes
Chapter number 5
Book title
Metabolic Network Reconstruction and Modeling
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7528-0_5
Pubmed ID
Book ISBNs
978-1-4939-7527-3, 978-1-4939-7528-0
Authors

Janaka N. Edirisinghe, José P. Faria, Nomi L. Harris, Benjamin H. Allen, Christopher S. Henry

Abstract

Genome-scale metabolic models (GEMs) generated from automated reconstruction pipelines often lack accuracy due to the need for extensive gapfilling and the inference of periphery metabolic pathways based on lower-confidence annotations. The central carbon pathways and electron transport chains are among the most well-understood regions of microbial metabolism, and these pathways contribute significantly toward defining cellular behavior and growth conditions. Thus, it is often useful to construct a simplified core metabolic model (CMM) that is comprised of only the high-confidence central pathways. In this chapter, we discuss methods for producing core metabolic models (CMM) based on genome annotations. With its reduced scope compared to GEMs, CMM reconstruction focuses on accurate representation of the central metabolic pathways related to energy biosynthesis and accurate energy yield predictions. We demonstrate the reconstruction and analysis of CMMs using the DOE Systems Biology Knowledgebase (KBase). The complete workflow is available at http://kbase.us/core-models/.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 27%
Student > Bachelor 5 19%
Student > Master 3 12%
Student > Ph. D. Student 3 12%
Professor > Associate Professor 1 4%
Other 1 4%
Unknown 6 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 19%
Biochemistry, Genetics and Molecular Biology 4 15%
Environmental Science 2 8%
Chemical Engineering 2 8%
Computer Science 2 8%
Other 4 15%
Unknown 7 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 December 2017.
All research outputs
#6,112,719
of 24,807,923 outputs
Outputs from Methods in molecular biology
#1,645
of 13,915 outputs
Outputs of similar age
#113,293
of 453,686 outputs
Outputs of similar age from Methods in molecular biology
#134
of 1,485 outputs
Altmetric has tracked 24,807,923 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,915 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 453,686 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 1,485 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.