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. 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.
Guziolowski
Carito
C
INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, Rennes 35042 France. carito.guziolowski@bioquant.uni-heidelberg.de
Blachon
Sylvain
S
Baumuratova
Tatiana
T
Stoll
Gautier
G
Radulescu
Ovidiu
O
Siegel
Anne
A
D000465:Algorithms; D002453:Cell Cycle / Q000502:physiology; D045744:Cell Line, Tumor; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D025941:Protein Interaction Mapping / Q000379:methods*; D012512:Sarcoma, Ewing / Q000235:genetics* / Q000378:metabolism*; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*