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Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.

Accepted version
Peer-reviewed

Type

Article

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Authors

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

Abstract

Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.

Description

Keywords

Machine Learning, Metabolism, Models, Theoretical, Systems Biology

Journal Title

Briefings in Bioinformatics

Conference Name

Journal ISSN

1477-4054
1477-4054

Volume Title

19

Publisher

Oxford University Press

Rights

All rights reserved
Sponsorship
This work was partially funded by a Teesside University doctoral scholarship, EPSRC, and the EU grant MIMOMICS.