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Propagating semantic information in biochemical network models

BMC Bioinformatics (2012) 13(1)
DOI: 10.1186/1471-2105-13-18
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Abstract

Background: To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.Results: A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.Conclusions: Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org. © 2012 Schulz et al.; licensee BioMed Central Ltd.

Figures

  • Figure 1 Propagation of colour features. Feature propagation in a small example network (circles: compounds; squares: reactions). A: Network nodes carry feature vectors v = (r, g, b)T shown by RGB colours. Feature vectors for non-annotated elements are unknown and set to zero by definition (nodes shown in grey). B: After feature propagation, all nodes have distinct feature vectors, i.e. colours. Matching nodes by their colours would now allow to self-align the entire graph unambiguously.
  • Figure 2 Similarity propagation. A: Alignment of two models describing the same biochemical reaction (circles: compounds; squares: reactions). The reactants a and p have a known similarity sap (dashed line; accordingly for products b and q), while the similarity between the reactions x and y is initially unknown. To determine how well the reactions match (dotted line), we compute the inferred similarities ψsp. B: Propagation graph. Red arrows show how information is propagated from species to reactions (a potential propagation back is shown in blue). The similarity between the reactions x and y is supported by two paths (x ¬ a ↔ p ® y and x ¬ b ↔ q ® y). The respective terms a2sap and a2sbq yield the inferred similarityψ sp xy = α2(σap + σbq).
  • Figure 3 Alignments of computational models. A: Linear pathway; only the first and the last species are annotated. B: Self-alignment of the linear pathway. Depicted are the pairwise element similarities according to the three different similarity measures. Each table entry contains six numerical values. The entries in the upper line denote the direct similarity s (blue), the similarity ψfp obtained by feature propagation (red), and the similarity ψsp from similarity propagation (green). Values between 0 and 1 are shown by colour intensities. Similarities between different types of elements (e.g. species and reactions) were set to 0, while ψsp values larger than 1 were set to 1. The lower three entries show the element matching obtained from these similarities. Thick boxes indicate the correct matching.
  • Figure 4 Alignment of MAP kinase cascades. Alignment of BioModels 9 [23] and 11 [24] as shown in Figure 4. The two alignments based on inferred similarities contain previously unmatched pairs (E2, RAFPH), (P_KKK, RAFp), and (KKPase, MEKPH) and some previously false matches are swapped. These incorrect matches resulted from the fact that elements represent proteins in different phosphorylation states but carry the same annotations. The inferred similarity measures differ in two details: the matching of RAF and RAFpRAFPH, which is correctly predicted from similarity propagation and the matching of Reaction19 and Reaction25, which is correctly obtained from feature propagation (data not shown).
  • Figure 5 Assessing the quality of model alignments. The quality of model alignments increases with the fraction of annotated model elements. Alignments between BioModel 9 and BioModel 11 (see Figure 4) were compared to a manually chosen, correct alignment and scored by their recall (left boxes) and precision (right boxes). When annotations are randomly removed, recall and precision (y-axis) decrease with the fraction of removed annotations (x-axis). Boxes in different rows show results from alignments with direct similarities, FP similarities, and SP similarities, as well as a comparison of their three mean values. The straight lines in the three topmost boxes show trends and stem from a linear regression.
  • Figure 6 Evaluation of the annotation prediction in BioModel 61. Probability to find a correct annotation within in the first n predicted annotations (y-axis), for varying numbers of annotations present in the model (x-axis). Boxes show the cases n = 1, n = 5, and n = 10. Trends in the data are shown by straight lines.
  • Figure 7 Similarity propagation resembles diffusion of a graph. A: Propagation graph from Figure 2, redrawn with labels for element pairs (diamonds). B: Similarity propagation on a pair propagation graph. Nodes in the graph correspond to element pairs in the original networks. Edges indicate that information can be propagated simultaneously for both elements in a pair. For instance, similarity information can be propagated from the pair (a,p) to the pair (x,y), resulting in the edge 1 ® 9 with a weight a · a. C: Feature propagation entails a similar process with more paths for propagation. For instance, similarity information could now be propagated from the pair (a,y) to the pair (x,y), resulting in the edge 5 ® 9 with weight a · 1.

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CITATION STYLE

APA

Schulz, M., Klipp, E., & Liebermeister, W. (2012). Propagating semantic information in biochemical network models. BMC Bioinformatics, 13(1). https://doi.org/10.1186/1471-2105-13-18

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