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

Vijayakumar, S. and Conway, M. and Lió, P. and Angione, C. (2018) Seeing the wood for the trees:a forest of methods for optimization and omic-network integration in metabolic modelling. Briefings in Bioinformatics, 19 (6). 1218–1235. ISSN 1467-5463

Full text not available from this repository.

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.

Item Type:
Journal Article
Journal or Publication Title:
Briefings in Bioinformatics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1710
Subjects:
ID Code:
162350
Deposited By:
Deposited On:
18 Nov 2021 10:26
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Nov 2021 16:46