Designing logical rules to model the response of biomolecular networks with complex interactions: an Application to Cancer Modeling
We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational interactions, we consider two types of nodes in the network, corresponding to proteins and to RNA. Microar...
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| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2011
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| In: |
IEEE ACM transactions on computational biology and bioinformatics
Year: 2011, Jahrgang: 8, Heft: 5, Pages: 1223-1234 |
| ISSN: | 1557-9964 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://ieeexplore.ieee.org/document/5557845
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| Verfasserangaben: | Carito Guziolowski, Sylvain Blachon, Tatiana Baumuratova, Gautier Stoll, Ovidiu Radulescu, and Anne Siegel |
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| 245 | 1 | 0 | |a Designing logical rules to model the response of biomolecular networks with complex interactions |b an Application to Cancer Modeling |c Carito Guziolowski, Sylvain Blachon, Tatiana Baumuratova, Gautier Stoll, Ovidiu Radulescu, and Anne Siegel |
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| 520 | |a We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational interactions, we consider two types of nodes in the network, corresponding to proteins and to RNA. Microarray data provides very lacunar information for such types of networks because protein nodes, although needed in the model, are not observed. Propagation of observations in such networks leads to poor and nonsignificant model predictions, mainly because rules used to propagate information-usually disjunctive constraints-are weak. Here, we propose a new, stronger type of logical constraints that allow us to strengthen the analysis of the relation between microarray and interaction data. We use these rules to identify the nodes which are responsible for a phenotype, in particular for cell cycle progression. As the benchmark, we use an interaction network describing major pathways implied in Ewing's tumor development. The Python library used to obtain our results is publicly available on our supplementary web page. | ||
| 650 | 4 | |a automatic reasoning | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Biological system modeling | |
| 650 | 4 | |a cancer. | |
| 650 | 4 | |a Computational biology | |
| 650 | 4 | |a in-silico analysis | |
| 650 | 4 | |a Network topology | |
| 650 | 4 | |a posttranslational effects | |
| 650 | 4 | |a Predictive models | |
| 650 | 4 | |a Proteins | |
| 650 | 4 | |a regulatory networks | |
| 650 | 4 | |a Systems biology | |
| 700 | 1 | |a Blachon, Sylvain |e VerfasserIn |4 aut | |
| 700 | 1 | |a Baumuratova, Tatiana |e VerfasserIn |4 aut | |
| 700 | 1 | |a Stoll, Gautier |e VerfasserIn |4 aut | |
| 700 | 1 | |a Radulescu, Ovidiu |e VerfasserIn |4 aut | |
| 700 | 1 | |a Siegel, Anne |e VerfasserIn |4 aut | |
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