Algorithm overview:
1) If the DNA sequence is used as input, ORFs are identifed and translated using Prodigal.
2) Proteins of the target organism are aligned with those from UniRef90.
3) To each reaction in the universal model, a score is assigned based on the alignment score and the organism's taxonomy.
4) Reactions with negative scores are potentially adjusted if they belong to pathways where nearly all reactions have positive scores.
5) A subset of the universal model that maximizes the total reaction scores while maintaining network connectivity and biomass formation is identified.
6) Based on experimental gene essentiality data, reactions likely included erroneously are identified and removed from the model.
7) Considering the completeness level of pathways in the model, reactions associated with promiscuous enzymes that don't contribute to nearly complete pathways are removed. At least one reaction from each promiscuous enzyme is retained.
8) An optimization similar to step 5 is performed while making negative the score of the rections identified in steps 6 and 7.
9) When relevant, passive diffusion reactions and spontaneous reactions are integrated into the model.