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

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Metabolic Network Reconstruction and Modeling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1716))

Abstract

PSAMM is an open source software package that supports the iterative curation and analysis of genome-scale models (GEMs). It aims to integrate the annotation and consistency checking of metabolic models with the simulation of metabolic fluxes. The model representation in PSAMM is compatible with version tracking systems like Git, which allows for full documentation of model file changes and enables collaborative curations of large, complex models. This chapter provides a protocol for using PSAMM functions and a detailed description of the various aspects in setting up and using PSAMM for the simulation and analysis of metabolic models. The overall PSAMM workflow outlined in this chapter includes the import and export of model files, the documentation of model modifications using the Git version control system, the application of consistency checking functions for model curations, and the numerical simulation of metabolic models.

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Authors and Affiliations

  1. Department of Cell and Molecular Biology, College of the Environment and Life Sciences, University of Rhode Island, Kingston, RI, USA

    Keith Dufault-Thompson, Jon Lund Steffensen & Ying Zhang

Authors
  1. Keith Dufault-Thompson
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  2. Jon Lund Steffensen
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  3. Ying Zhang
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Corresponding author

Correspondence to Ying Zhang .

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Editors and Affiliations

  1. Department of Biology, University of Florence, Sesto Fiorentino, Florence, Italy

    Marco Fondi

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Dufault-Thompson, K., Steffensen, J.L., Zhang, Y. (2018). Using PSAMM for the Curation and Analysis of Genome-Scale Metabolic Models. In: Fondi, M. (eds) Metabolic Network Reconstruction and Modeling. Methods in Molecular Biology, vol 1716. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7528-0_6

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  • DOI: https://doi.org/10.1007/978-1-4939-7528-0_6

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7527-3

  • Online ISBN: 978-1-4939-7528-0

  • eBook Packages: Springer Protocols

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