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

TL;DR

This paper reviews and classifies existing methods for constraint-based metabolic modelling, provides a practical tutorial on multi-objective optimisation in R, and discusses machine learning approaches for multi-omic data integration.

Contribution

It offers a comprehensive classification of metabolic modelling methods, an interactive online resource, and introduces a hands-on tutorial for multi-objective optimisation and multi-view machine learning integration.

Findings

Classified existing methods into a hierarchical 'forest' structure

Provided an R tutorial for multi-objective optimisation in metabolic models

Discussed machine learning strategies for multi-omic data integration

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 hybridise and become more complex. We then provide the first hands-on tutorial for multi-objective optimisation 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 optimisation 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.