2.1.2. Refinement of Reconstruction

After the first draft model was obtained, a great deal of work was required until the model represented the phenotypic states of the organism [50]. Gap-find was utilized to identify network pathologies which include root no-consumption, root no-production, downstream no-production, and upstream no-consumption and blocked reactions [28,51].

An exhaustive review of the literature was carried out to identify reactions related to the secondary metabolism of the plant that could fill the gaps and facilitate integrating diverse metabolic pathways taking place in the different cellular compartments [6,11,14,16,52,53,54,55]. Furthermore, KEGG tools were used to complement the metabolic information of the reconstruction through a second functional annotation carried out based on BlastKOALA (KEGG Orthology and Links Annotation) [56]. This was aided by the updated annotation release of the C. sativa reference genome [44].

Regarding the manual curation of models, one of the most complex problems researchers face is the diversity of terminology in reference databases. The present model relies on the Model SEED repository [57], which involves several databases (KEGG, MetaCyc, AraGEM, BiGG, Maize_C4GEM, PlantCyc, and TS_Athaliana, among others) and adds a unique identifier to them [12].

An iterative workflow was carried out to add reactions identified previously to the reconstruction. First, reactions were transformed to the ModelSEED nomenclature taking into account reference ModelSEED database information. Next, renamed reactions were integrated into the reconstruction, considering each compartment of the reconstruction. Finally, a network evaluation was carried out, looking for additional gaps that could be generated for new reactions (Figure 1).

Once the reconstruction was obtained, successive flux balance analyses (FBAs) were carried out. FBA is a mathematical approach that calculates the flow of metabolites through metabolic reconstruction, making it possible to predict the growth rate of an organism [58]. This is done by taking advantage of the constraints imposed by the stoichiometric coefficients of each reaction in the metabolic fluxes. FBAs of C. sativa are based on the biomass composition of the plant cell [49] as the objective function of the model (Supplementary Table S1) and then evaluating the flow distribution within the system.