Reference : Designing logical rules to model the response of biomolecular networks with complex i...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
Designing logical rules to model the response of biomolecular networks with complex interactions: an application to cancer modeling.
Guziolowski, Carito mailto [University Hospital, Heidelberg]
Blachon, Sylvain [Max-Planck Institute, Potsdam]
Baumuratova, Tatiana mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Stoll, Gautier mailto [Institut Curie, Paris]
Radulescu, Ovidiu mailto [Universie de Montpelier 2]
Siegel, Anne mailto [University of Rennes 1, Rennes]
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Yes (verified by ORBilu)
United States
[en] Algorithms ; Cell Cycle/physiology ; Cell Line, Tumor ; Computer Simulation ; Gene Expression Profiling/methods ; Gene Regulatory Networks ; Humans ; Linear Models ; Models, Biological ; Oligonucleotide Array Sequence Analysis ; Phenotype ; Protein Interaction Mapping/methods ; Sarcoma, Ewing/genetics/metabolism ; Signal Transduction ; Systems Biology/methods
[en] 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.

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