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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
  8. Altmetric Badge
    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 8: Template-Assisted Metabolic Reconstruction and Assembly of Hybrid Bacterial Models
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Chapter title
Template-Assisted Metabolic Reconstruction and Assembly of Hybrid Bacterial Models
Chapter number 8
Book title
Metabolic Network Reconstruction and Modeling
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7528-0_8
Pubmed ID
Book ISBNs
978-1-4939-7527-3, 978-1-4939-7528-0
Authors

Tiziano Vignolini, Alessio Mengoni, Marco Fondi

Abstract

Intraspecific genomic exchanges happen frequently between bacteria living in the same natural environment and can also be performed artificially in the laboratory for basic research or genetic/metabolic engineering purposes. In silico metabolic reconstruction and simulation of the metabolism of the hybrid strains that result from these processes can be used to predict the phenotypic outcome of such genomic rearrangements; this can be especially helpful as a designing tool in the purview of synthetic biology. However, reconstructing the metabolism of a bacterium with a hybrid genome through in silico approaches is not a trivial task, as it requires taking into account the complex relationships existing between metabolic genes and how they change (or remain unchanged) when new genes are placed in a different genomic context. Furthermore, in order to "mix" the metabolic models of different bacterial strains one needs at least two different metabolic models to begin with, and reconstructing a genome-scale model from the ground up is a challenging task itself, requiring an intensive manual effort and a great deal of information. In this chapter, we propose two general protocols to address the aforementioned issues of: (1) quickly generating strain-specific metabolic models, given the relevant genomic sequence and an already existing, high-quality metabolic model of a different strain belonging to the same species, and (2) reconstructing the metabolic model of a hybrid strain containing genomic elements from two different parental strains.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 23%
Professor > Associate Professor 2 15%
Student > Master 2 15%
Student > Ph. D. Student 1 8%
Professor 1 8%
Other 0 0%
Unknown 4 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 31%
Biochemistry, Genetics and Molecular Biology 3 23%
Unknown 6 46%
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 10 December 2017.
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#20,454,971
of 23,011,300 outputs
Outputs from Methods in molecular biology
#9,941
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Outputs of similar age
#378,156
of 442,319 outputs
Outputs of similar age from Methods in molecular biology
#1,193
of 1,498 outputs
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