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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
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    Chapter 18 Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives
Attention for Chapter 6: Using PSAMM for the Curation and Analysis of Genome-Scale Metabolic Models
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Chapter title
Using PSAMM for the Curation and Analysis of Genome-Scale Metabolic Models
Chapter number 6
Book title
Metabolic Network Reconstruction and Modeling
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7528-0_6
Pubmed ID
29222752
Book ISBNs
978-1-4939-7527-3, 978-1-4939-7528-0
Authors

Keith Dufault-Thompson, Jon Lund Steffensen, Ying Zhang

Abstract

PSAMM is an open source software package that supports the iterative curation and analysis of genome-scale models (GEMs). It aims to integrate the annotation and consistency checking of metabolic models with the simulation of metabolic fluxes. The model representation in PSAMM is compatible with version tracking systems like Git, which allows for full documentation of model file changes and enables collaborative curations of large, complex models. This chapter provides a protocol for using PSAMM functions and a detailed description of the various aspects in setting up and using PSAMM for the simulation and analysis of metabolic models. The overall PSAMM workflow outlined in this chapter includes the import and export of model files, the documentation of model modifications using the Git version control system, the application of consistency checking functions for model curations, and the numerical simulation of metabolic models.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 33%
Student > Ph. D. Student 2 11%
Student > Master 2 11%
Other 1 6%
Professor 1 6%
Other 2 11%
Unknown 4 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 22%
Agricultural and Biological Sciences 3 17%
Medicine and Dentistry 2 11%
Computer Science 1 6%
Environmental Science 1 6%
Other 2 11%
Unknown 5 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 13 December 2017.
All research outputs
#15,708,425
of 23,344,526 outputs
Outputs from Methods in molecular biology
#5,497
of 13,338 outputs
Outputs of similar age
#271,760
of 444,166 outputs
Outputs of similar age from Methods in molecular biology
#600
of 1,502 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,338 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 444,166 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,502 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.

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