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Metabolic modelling has entered a mature phase with dozens of methods andsoftware implementations available to the practitioner and the theoretician. Itis not easy for a modeller to be able to see the wood (or the forest) for thetrees. Driven by this analogy, we here present a "forest" of principal methodsused for constraint-based modelling in systems biology. This provides atree-based view of methods available to prospective modellers, also availablein interactive version at http://modellingmetabolism.net, where it will be keptupdated with new methods after the publication of the present manuscript. Ourupdated classification of existing methods and tools highlights the mostpromising in the different branches, with the aim to develop a vision of howexisting methods could hybridise and become more complex. We then provide thefirst hands-on tutorial for multi-objective optimisation of metabolic models inR. We finally discuss the implementation of multi-view machine-learningapproaches in poly-omic integration. Throughout this work, we demonstrate theoptimisation of trade-offs between multiple metabolic objectives, with a focuson omic data integration through machine learning. We anticipate that thecombination of a survey, a perspective on multi-view machine learning, and astep-by-step R tutorial should be of interest for both the beginner and theadvanced user.

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