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Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

Guziolowski C, Videla S, Eduati F, Thiele S, Cokelaer T, Siegel A, Saez-Rodriguez J - Bioinformatics (2013)

Bottom Line: We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data.Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion.We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them.

View Article: PubMed Central - PubMed

Affiliation: École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.

ABSTRACT

Motivation: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions.

Results: We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design.

Availability: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/.

Supplementary information: Supplementary materials are available at Bioinformatics online.

Contact: santiago.videla@irisa.fr.

Show MeSH
Frequencies of hyperedges over 11 700 suboptimal models within 10% tolerance. Among the 130 possible hyperedges, 14 were always present, 59 were always absent and 57 were present in some but not all models
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btt393-F3: Frequencies of hyperedges over 11 700 suboptimal models within 10% tolerance. Among the 130 possible hyperedges, 14 were always present, 59 were always absent and 57 were present in some but not all models

Mentions: The complete computation of suboptimal models allows a precise characterization of the distribution of hyperedges, and, therefore, of logical gates in the potential models. When we evaluated the distribution of the 130 possible hyperedges (i.e. those that are included in the hypergraph derived from the original PKN) across the 11 700 models, we found that 14 hyperedges are present in all suboptimal models, and we thus expect them to be functional in HepG2 cells. Fifty-nine hyperedges are absent from all models, thus suggesting that they are not functional in these cells. Finally, 57 hyperedges are present in only a subset of the models; their frequency ranges from 0.99 to 0.0003, showing a large variability (Fig. 3). Therefore, for the given experimental data, these hyperedges are not identifiable, as it is not possible to determine whether they are functional in HepG2 cells.Fig. 3.


Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

Guziolowski C, Videla S, Eduati F, Thiele S, Cokelaer T, Siegel A, Saez-Rodriguez J - Bioinformatics (2013)

Frequencies of hyperedges over 11 700 suboptimal models within 10% tolerance. Among the 130 possible hyperedges, 14 were always present, 59 were always absent and 57 were present in some but not all models
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3753570&req=5

btt393-F3: Frequencies of hyperedges over 11 700 suboptimal models within 10% tolerance. Among the 130 possible hyperedges, 14 were always present, 59 were always absent and 57 were present in some but not all models
Mentions: The complete computation of suboptimal models allows a precise characterization of the distribution of hyperedges, and, therefore, of logical gates in the potential models. When we evaluated the distribution of the 130 possible hyperedges (i.e. those that are included in the hypergraph derived from the original PKN) across the 11 700 models, we found that 14 hyperedges are present in all suboptimal models, and we thus expect them to be functional in HepG2 cells. Fifty-nine hyperedges are absent from all models, thus suggesting that they are not functional in these cells. Finally, 57 hyperedges are present in only a subset of the models; their frequency ranges from 0.99 to 0.0003, showing a large variability (Fig. 3). Therefore, for the given experimental data, these hyperedges are not identifiable, as it is not possible to determine whether they are functional in HepG2 cells.Fig. 3.

Bottom Line: We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data.Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion.We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them.

View Article: PubMed Central - PubMed

Affiliation: École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.

ABSTRACT

Motivation: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions.

Results: We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design.

Availability: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/.

Supplementary information: Supplementary materials are available at Bioinformatics online.

Contact: santiago.videla@irisa.fr.

Show MeSH