Model alignment with semantic propagation

To align two SBML models, model elements are individually compared by their semantic annotations. Each possible element pair obtains a similarity value and a greedy algorithm is used to match element pairs with high similarities.
A model alignment based on MIRIAM-compliant element annotations provides good results in many cases. Unfortunately, the alignment is difficult or impossible if one or both of the models

• are missing annotations, which makes it impossible to align some of the elements,
• or contain ambiguous annotations, leaving many different element matches equally probable.

In such cases, model alignment can be improved by using the semantic annotations of other elements that are nearby in the model network. For instance, if two reactions from different models share reactants (e.g., if the reactants have already been matched by their semantic annotations), they can be regarded as similar. In this case, knowledge about similarity is propagated from the reactant elements to the reaction elements.
The model alignment on this page lets semantic information diffuse across the reaction network to infer similarities between non-annotated elements. There are two alternative methods. In the first method ("feature propagation"), information about single-element annotations diffuses on the reaction network. In the second method ("similarity propagation"), similarities of element pairs are propagated on an abstract pair matching graph. Differences between the two approaches and the normal similarity measure can be evaluated with this tool. After uploading two models, the model alignment will be shown in the form of a an element pair matrix:

	S1
species_1	x x x x x x

The first row shows pairwise similarities obtained without propagation, with feature propagation, and with similarity propagation. Color intensities depict the similarity values. The second row shows the matching based on these similarity values (color: matched elements; white: no match). For more details about semantic propagation and model alignment, please read the upcoming article:
Schulz et al.: Propagating semantic information in biochemical network models.