Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives

Supreeta Vijayakumar, Max Conway, Pietro Lió, Claudio Angione

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Genome-scale metabolic models are valuable tools for assessing the metabolic potential of living organisms. Being downstream of gene expression, metabolism is increasingly being used as an indicator of the phenotypic outcome for drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimization using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g., antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages389-408
Number of pages20
ISBN (Print)9781493968572
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

NameMethods in Molecular Biology
Volume1716
ISSN (Print)1064-3745

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    Vijayakumar, S., Conway, M., Lió, P., & Angione, C. (2018). Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives. In Methods in Molecular Biology (pp. 389-408). (Methods in Molecular Biology; Vol. 1716). Humana Press Inc.. https://doi.org/10.1007/978-1-4939-7528-0_18