mod_gen: Knowledge-based generalization for metabolic models

Model Generalization is a Python library that compresses a metabolic network model using the knowledge-based model generalization method.

Model generalization takes a model in SBML format as input, and produces 2 SBML files as an output:

• SBML containing the generalized model
• SBML file with groups extension containing the initial model plus the groups representing similar metabolites and similar reactions.

Article

Model Generalization is described in `Zhukova A, Sherman DJ. Knowledge-based generalization of metabolic models. J Comput Biol. 2014 Jul;21(7):534-47 doi:10.1089/cmb.2013.0143.

Model Generalization Method

The model generalization method groups similar metabolites and reactions in the network based on its structure and the knowledge, extracted from the ChEBI ontology. The reactions between the same generalized species are factored together into generalized reactions. A generalization is made specifically for a given model and is maximal with respect to the relations in the model; it respects semantic constraints such as reaction stoichiometry, connectivity, and transport between compartments; and it is performed through a heuristic method that is efficient in practice for genome-scale models.

Each metabolite can be generalized up to one of its ancestors in ChEBI. If a ChEBI annotation for a metabolite is not present in the model, the method attempts to automatically deduce it by comparing metabolite’s name to ChEBI terms’ names and synonyms. Reactions that share the same generalized reactants and the same generalized products, are considered equivalent and are factored together into a generalized reaction.

The appropriate level of abstraction for metabolites and reactions is defined by the network itself as the most general one that satisfies two restrictions: 1. Stoichiometry preserving restriction: metabolites that participate in the same reaction cannot be grouped together; 2. Metabolite diversity restriction: metabolites that do not participate in any pair of similar reactions are not grouped together (as there is no evidence of their similarity in the network).

Overall, the generalization method is composed of three modules: 1. Aggressive reaction grouping based on the most general metabolite grouping (defined by ChEBI), in order to generate reaction grouping candidates; 2. Ungrouping of some metabolites and reactions to correct for violation of the stoichiometry preserving restriction; 3. Ungrouping of some metabolites (while keeping the reaction grouping intact) to correct for violation of the metabolite diversity restriction.

For instance, (S)-3-hydroxydecanoyl-CoA, (S)-3-hydroxylauroyl-CoA and (S)-3-hydroxytetradecanoyl-CoA have a common ancestor hydroxy fatty acyl-CoA in ChEBI. They can be grouped and generalized into hydroxy fatty acyl-CoA, if in the network there is no reaction whose stoichiometry would be changed by such a generalization (stoichiometry preserving restriction), and exist similar reactions that consume or produce them (metabolite diversity restriction).

Installation

From the directory where you have extracted this archive, execute:

• python setup.py

Running Model Generalization

Execute:

• python ./main.py --model path_to_your_model.xml --verbose

For example:

• python ./main.py --model ./MODEL1111190000.xml --verbose

The script will produce two SBML files, containing the generalized model:

• path_to_your_model_generalized.xml -- SBML containing the generalized model
• path_to_your_model_with_groups.xml -- SBML file with groups extension containing the initial model plus the groups representing similar metabolites and similar reactions.