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Metabolic Network Reconstruction and Modeling

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

Table of Contents

  1. Altmetric Badge
    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 1: Reconstructing High-Quality Large-Scale Metabolic Models with merlin
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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3 X users

Citations

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Chapter title
Reconstructing High-Quality Large-Scale Metabolic Models with merlin
Chapter number 1
Book title
Metabolic Network Reconstruction and Modeling
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7528-0_1
Pubmed ID
29222747
Book ISBNs
978-1-4939-7527-3, 978-1-4939-7528-0
Authors

Oscar Dias, Miguel Rocha, Eugénio Campos Ferreira, Isabel Rocha

Abstract

Here, the basic principles of reconstructing genome-scale metabolic models with merlin are described. This tool covers the basic stages of this process, providing several tools that allow assembling models, using the sequenced genome as a starting point. merlin has two main modules, separating the process of annotating (enzymes, transporters, and compartments) on the genome from the process of model assembly, though information from the former is integrated in the latter after curation. Moreover, merlin provides several tools to curate the model, including tools for generating reactions' gene rules and placeholder entities for biomass precursors, such as proteins (e-protein) or nucleotides (e-DNA and e-RNA) among others.This tutorial covers each feature of merlin in detail, including the assessment of experimental data for the validation of the model.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 17%
Researcher 7 17%
Student > Doctoral Student 4 10%
Student > Ph. D. Student 4 10%
Student > Postgraduate 3 7%
Other 4 10%
Unknown 13 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 19%
Biochemistry, Genetics and Molecular Biology 7 17%
Engineering 3 7%
Immunology and Microbiology 3 7%
Chemical Engineering 1 2%
Other 5 12%
Unknown 15 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 December 2017.
All research outputs
#13,223,020
of 23,011,300 outputs
Outputs from Methods in molecular biology
#3,437
of 13,157 outputs
Outputs of similar age
#211,011
of 442,319 outputs
Outputs of similar age from Methods in molecular biology
#313
of 1,498 outputs
Altmetric has tracked 23,011,300 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,157 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 73% 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 442,319 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 51% of its contemporaries.
We're also able to compare this research output to 1,498 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.

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