  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.
