Fr. 135.00

Metabolic Network Reconstruction and Modeling - Methods and Protocols

English · Paperback / Softback

Shipping usually within 6 to 7 weeks

Description

Read more

This volume looks at the latest methodologies used to study cellular metabolism with in silico approaches. The chapters in this book are divided into 3 parts: part I discusses tools and methods used for metabolic reconstructions and basic constraint-based metabolic modeling (CBMM); Part II explores protocols for the generation of experimental data for metabolic reconstruction and modeling, including transcriptomics, proteomics, and mutant generations; and Part III cover advanced techniques for quantitative modeling of cellular metabolism, including dynamic Flux Balance Analysis and multi-objective optimization. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and thorough, MetabolicNetwork Reconstruction and Modeling: Methods and Protocols is a valuable resource for qualified investigators studying cellular metabolism, and novice researchers who want to start working with CBMM.

List of contents

Reconstructing High-Quality Large-Scale Metabolic Models with merlin.- Analyzing and Designing Cell Factories with OptFlux.- The MONGOOSE Rational Arithmetic Toolbox.- The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models.- Reconstruction and Analysis of Central Metabolism in Microbes.- Using PSAMM for the Curation and Analysis of Genome-Scale Metabolic Models.- Integration of Comparative Genomics with Genome-Scale Metabolic Modeling to Investigate Strain-Specific Phenotypical Differences.- Temple-Assisted Metabolic Reconstruction and Assembly of Hybrid Bacterial Models.- Integrated Host-Pathogen Metabolic Reconstruction.- Metabloic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem.- RNA Sequencing and Analysis in Microorganisms for Metabolic Network Reconstruction.- Differential Proteomics Based on 2D-Difference In-Gel Electrophoresis and Tandem Mass Spectrometry for the Elucidation of Biological Processes in Antibiotic-Producer Bacterial Strains.- Techniques for Large-Scale Bacterial Genome Manipulation and Characterization of the Mutants with Respect to In Silico Metabolic Reconstructions.- Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models using Fast-SL.- Coupling Fluxes, Enzymes, and Regulation in Genome-Scale Metabolic Models.- Dynamic Flux Balance Analysis using DFBAlab.- Designing Optimized Production Hosts by Metabolic Modeling.- Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-On Tutorial and Perspectives.

About the author


Marco Fondi


Department of Biology


University of Florence


Via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy


Email: marco.fondi@unifi.it

Summary

Includes cutting-edge methods and protocols


Provides step-by-step detail essential for reproducible results


Contains key notes and implementation advice from the experts

Product details

Assisted by Marc Fondi (Editor), Marco Fondi (Editor)
Publisher Springer, Berlin
 
Languages English
Product format Paperback / Softback
Released 01.01.2018
 
EAN 9781493985111
ISBN 978-1-4939-8511-1
No. of pages 410
Dimensions 179 mm x 24 mm x 254 mm
Weight 802 g
Illustrations XI, 410 p. 103 illus., 92 illus. in color.
Series Methods in Molecular Biology
Subjects Natural sciences, medicine, IT, technology > Biology > Microbiology

B, Proteomics, Biomedical and Life Sciences, Cell Biology, Metabolomics

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.