Metabolic Network Reconstruction and Modeling

1. Front Matter

   Pages i-xi

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

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

   Pages 1-36

3. Analyzing and Designing Cell Factories with OptFlux

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

   Pages 37-76

4. The MONGOOSE Rational Arithmetic Toolbox

   • Christopher Le, Leonid Chindelevitch

   Pages 77-99

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

   • Maria Pires Pacheco, Thomas Sauter

   Pages 101-110

6. Reconstruction and Analysis of Central Metabolism in Microbes

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

   Pages 111-129

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

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

   Pages 131-150

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

   • Jonathan Monk, Emanuele Bosi

   Pages 151-175

9. Template-Assisted Metabolic Reconstruction and Assembly of Hybrid Bacterial Models

   • Tiziano Vignolini, Alessio Mengoni, Marco Fondi

   Pages 177-196

10. Integrated Host-Pathogen Metabolic Reconstructions

    • Anu Raghunathan, Neema Jamshidi

    Pages 197-217

11. Metabolic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem

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

    Pages 219-238

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

    • Eva Pinatel, Clelia Peano

    Pages 239-265

13. 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, Andrea Scaloni

    Pages 267-289

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

    • George C. diCenzo, Turlough M. Finan

    Pages 291-314

15. Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL

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

    Pages 315-336

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

    • Paul A. Jensen

    Pages 337-351

17. Dynamic Flux Balance Analysis Using DFBAlab

    • Jose Alberto Gomez, Paul I. Barton

    Pages 353-370

18. Designing Optimized Production Hosts by Metabolic Modeling

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

    Pages 371-387

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

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

    Pages 389-408

20. Back Matter

    Pages 409-410

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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.

• metabolic landscape
• Phenotype Microarray
• Transcriptomics
• Proteomics
• Metabolomics

• Department of Biology, University of Florence, Sesto Fiorentino, Florence, Italy

  Marco Fondi

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

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