A gap-filling algorithm for prediction of metabolic interactions in microbial communities
Fig 1
Graphical representation of the community gap-filling method.
The metabolic reconstructions of two individual organisms (blue and orange respectively) are allowed to exchange metabolites and interact with the environment through a common metabolite pool (green). (a) The algorithm adds biochemical reactions from a reference database (dark purple) to the community model. (b) The community gap-filling algorithm is an MILP problem with the objective to add the minimum number of database reactions to the community model in order to restore biomass production in the individual metabolic models, while it satisfies some constrains for the reaction fluxes. In each compartment of the community model, the metabolite pools are assumed to be in steady state. The addition of each database reaction is controlled by a binary variable, which takes the value of 1 if the database reaction is added to the community model, and 0 otherwise.