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Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects

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dc.abstract.enMotivation Perturbation experiments constitute the central means to study cellular networks. Several confounding factors complicate computational modeling of signaling networks from this data. First, the technique of RNA interference (RNAi), designed and commonly used to knock-down specific genes, suffers from off-target effects. As a result, each experiment is a combinatorial perturbation of multiple genes. Second, the perturbations propagate along unknown connections in the signaling network. Once the signal is blocked by perturbation, proteins downstream of the targeted proteins also become inactivated. Finally, all perturbed network members, either directly targeted by the experiment, or by propagation in the network, contribute to the observed effect, either in a positive or negative manner. One of the key questions of computational inference of signaling networks from such data are, how many and what combinations of perturbations are required to uniquely and accurately infer the model? Results Here, we introduce an enhanced version of linear effects models (LEMs), which extends the original by accounting for both negative and positive contributions of the perturbed network proteins to the observed phenotype. We prove that the enhanced LEMs are identified from data measured under perturbations of all single, pairs and triplets of network proteins. For small networks of up to five nodes, only perturbations of single and pairs of proteins are required for identifiability. Extensive simulations demonstrate that enhanced LEMs achieve excellent accuracy of parameter estimation and network structure learning, outperforming the previous version on realistic data. LEMs applied to Bartonella henselae infection RNAi screening data identified known interactions between eight nodes of the infection network, confirming high specificity of our model and suggested one new interaction. Availability and implementation https://github.com/EwaSzczurek/LEM Supplementary information Supplementary data are available at Bioinformatics online.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorSzczurek, Ewa
dc.contributor.authorTiuryn, Jerzy
dc.date.accessioned2024-01-25T05:08:11Z
dc.date.available2024-01-25T05:08:11Z
dc.date.copyright2019-07-05
dc.date.issued2019
dc.description.accesstimeAT_PUBLICATION
dc.description.financeNie dotyczy
dc.description.number14
dc.description.versionFINAL_AUTHOR
dc.description.volume35
dc.identifier.doi10.1093/BIOINFORMATICS/BTZ334
dc.identifier.issn1367-4803
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/111029
dc.languageeng
dc.pbn.affiliationcomputer and information sciences
dc.relation.ispartofBioinformatics
dc.relation.pagesi605-i614
dc.rightsCC-BY-NC
dc.sciencecloudnosend
dc.titleLearning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects
dc.typeJournalArticle
dspace.entity.typePublication