Metabolic Network Reconstruction and Modeling: Methods and Protocols

ISBN-13: 9781493985111 / Angielski / Miękka / 2018 / 410 str.

Marco Fondi

Spis treści

Preface…

Table of Contents…

Contributing Authors…

1. Reconstructing High-Quality Large-Scale Metabolic Models with merlin

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

2. Analyzing and Designing Cell Factories with OptFlux

Paulo Vilaça, Paulo Maia, Hugo Giesteira, Isabel Rocha, and Miguel Rocha

3. The MONGOOSE Rational Arithmetic Toolbox

Christopher Le and Leonid Chindelevitch

4. The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models

Maria Pires Pacheco and Thomas Sauter

5. Reconstruction and Analysis of Central Metabolism in Microbes

Janaka N. Edirisinghe, José P. Faria, Nomi L. Harris, Benjamin H. Allen, and Christopher S. Henry

6. Using PSAMM for the Curation and Analysis of Genome-Scale Metabolic Models

Keith Dufault-Thompson, Jon Lund Steffensen, and Ying Zhang

7. Integration of Comparative Genomics with Genome-Scale Metabolic Modeling to Investigate Strain-Specific Phenotypical Differences

Jonathan Monk and Emanuele Bosi

8. Temple-Assisted Metabolic Reconstruction and Assembly of Hybrid Bacterial Models

Tiziano Vignolini, Alessia Mengoni, and Marco Fondi

9. Integrated Host-Pathogen Metabolic Reconstruction

Anu Raghunathan and Neema Jamshidi

10. Metabloic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem

Chao Ye, Nan Xu, Xiulai Chen, and Liming Liu

11. RNA Sequencing and Analysis in Microorganisms for Metabolic Network Reconstruction

Eva Pinatel and Clelia Peano

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

Giuseppe Gallo and Andrea Scaloni

13. Techniques for Large-Scale Bacterial Genome Manipulation and Characterization of the Mutants with respect to In Silico Metabolic Reconstructions

George C. diCenzo and Turlough M. Finan

14. Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models using Fast-SL

Karthik Raman, Aditya Pratapa, Omkar Mohite, and Shankar Balachandran

15. Coupling Fluxes, Enzymes, and Regulation in Genome-Scale Metabolic Models

Paul A. Jensen

16. Dynamic Flux Balance Analysis using DFBAlab

Jose Alberto Gomez and Paul I. Barton

17. Designing Optimized Production Hosts by Metabolic Modeling

Christian Jungreuthmayer, Matthias P. Gerstl, David A. Peña Navarro, Michael Hanscho, David E. Ruckerbauer, and Jürgen Zanghellini

18. Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-On Tutorial and Perspectives

Supreeta Vijayakumar, Max Conway, Pietro Lió, and Claudio Angione

O autorze

Marco Fondi
Department of Biology
University of Florence
Via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy
Email: marco.fondi@unifi.it

Blurb

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, Metabolic Network 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.