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A Practical Protocol for Integration of Transcriptomics Data into Genome-Scale Metabolic Reconstructions

  • Juan Nogales
  • Lucía Agudo
Protocol
Part of the Springer Protocols Handbooks book series (SPH)

Abstract

In recent years, an avalanche of data in the form of the so-called omics has been generated in biological sciences. Nevertheless, the effective use of this huge volume of data is challenging from a purely mathematical and statistical point of view, and integrative approaches are becoming a necessity. Genome-scale metabolic models offer an unprecedented chance to integrate and contextualise, in the correct biological context, this large amount of omics data being generated. This chapter provides a step-by-step protocol for the integration of transcriptomics data in genome-scale metabolic models by constructing condition-specific bacterial models. Subsequently, they are used to increase the accuracy of the in silico predictions in terms of metabolic flux prediction and for the better contextualisation of the transcriptomics data in the correct biological context. Two models environmental bacterial such as Pseudomonas putida KT2440 and Synechocystis sp. PCC 8063 and their corresponding GEMs are used here for such proposes.

Keywords

Constraint-based reconstruction and analysis Genome-scale model GIMME Omics integration Pseudomonas putida Synechocystis 

Notes

Acknowledgements

The authors would like to thank C. Herencias for testing the protocol and valuable discussion.

The research leading to these results has received funding from the Ministry of Economy and Competitiveness of Spain Grant BIO2012-39501, European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 311815 (SYNPOL project, http://www.synpol.org/) and European Union’s H2020 ERAIB LigBio project (PCIN-2014-113).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Environmental BiologyCentro de Investigaciones Biológicas-CSICMadridSpain

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