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Metabolic Network Reconstruction and Modeling

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Cover of 'Metabolic Network Reconstruction and Modeling'

Table of Contents

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    Book Overview
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    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 9: Integrated Host-Pathogen Metabolic Reconstructions
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Chapter title
Integrated Host-Pathogen Metabolic Reconstructions
Chapter number 9
Book title
Metabolic Network Reconstruction and Modeling
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7528-0_9
Pubmed ID
Book ISBNs
978-1-4939-7527-3, 978-1-4939-7528-0
Authors

Anu Raghunathan, Neema Jamshidi

Abstract

The science and art of Genome scale metabolic network reconstructions have been explicitly documented in the literature for organisms across all the three kingdoms of life. Constraints-based models derived from such reconstructions have been used to assess metabolic phenotypes of their complex connections to genotype accurately. The problem of infectious disease is complex due to the multifactorial response of the host to the pathogen. Systems biology approaches and modeling allow one to study, understand, and predict emergent properties of such complex responses. The integration of the host and pathogen metabolic networks and the subsequent merger of their stoichiometric matrices is nontrivial and requires understanding of both pathogen and host metabolism and physiologies. The protocol here describes the detailed process of network and stoichiometric matrix merger using a salmonella-mouse macrophage model. The protocol also discusses the interfacial and objective functions required to actually embark on the analysis of host-pathogen interaction models.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 40%
Researcher 3 15%
Student > Ph. D. Student 2 10%
Other 2 10%
Unspecified 1 5%
Other 1 5%
Unknown 3 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 30%
Biochemistry, Genetics and Molecular Biology 4 20%
Chemical Engineering 1 5%
Immunology and Microbiology 1 5%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 6 30%