Machine and deep learning meet genome-scale metabolic modeling
Fig 2
Constraint-based data integration and fluxome generation.
(a) Constraint-based metabolic modeling begins with the construction of a manually curated GSMM recording all reactions taking place in the network. (b) Coded within the structure of a GSMM is the stoichiometric matrix S, denoting the involvement of metabolites in each reaction. Constraints are applied to the model to identify a given metabolic goal, represented as the objective function c, and linear or quadratic optimization is used to maximize or minimize this objective. The steady-state assumption (Sv = 0) sets the product of the stoichiometric matrix S and flux vector v as invariant. (c) To compute a unique flux distribution, the objective function can be regularized by subtracting a concave function from it. In addition to v being restricted between default lower and upper limits (vmin and vmax), external multiomic data θ can be used to further constrain fluxes using the mapping function φ(θ), hence driving the output toward condition-dependent solutions. GSMM, genome-scale metabolic model.