Discriminating Direct and Indirect Connectivities in Biological Networks

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dc.contributor.ISNI0000 0001 2535 9739 (Bleris, L)en_US
dc.contributor.authorKang, Taeken_US
dc.contributor.authorMoore, Richarden_US
dc.contributor.authorLi, Yien_US
dc.contributor.authorSontag, Eduardoen_US
dc.contributor.authorBleris, Leonidasen_US
dc.contributor.utdAuthorKang, Taek
dc.contributor.utdAuthorMoore, Richard
dc.contributor.utdAuthorLi, Yi
dc.contributor.utdAuthorBleris, Leonidas
dc.date.accessioned2016-07-06T22:34:33Z
dc.date.available2016-07-06T22:34:33Z
dc.date.created2015-09-29
dc.descriptionIncludes supplementary informationen_US
dc.description.abstractReverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks.;en_US
dc.description.sponsorshipThis work was funded by the US National Institutes of Health Grants GM098984, GM096271, CA17001801, National Science Foundation Grant CBNET-1105524, and the University of Texas at Dallas. E.S. partially supported by Air Force Office of Scientific Research Grant FA9550- 14-1-0060.en_US
dc.identifier.bibliographicCitationKang, Taek, Richard Moore, Yi Li, Eduardo Sontag, et al. 2015. "Discriminating direct and indirect connectivities in biological networks." Proceedings of the National Academy of Sciences of the United States of America 112(41), doi: 10.1073/pnas.1507168112.en_US
dc.identifier.issn1091-6490en_US
dc.identifier.issue41en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/4902
dc.identifier.volume112en_US
dc.publisherNational Academy of Sciencesen_US
dc.relation.urihttp://dx.doi.org/10.1073/pnas.1507168112en_US
dc.rights©2015 National Academy of Sciencesen_US
dc.source.journalProceedings of the National Academy of Sciences of the United States of Americaen_US
dc.subjectCellsen_US
dc.subjectResampling (Statistics)en_US
dc.subjectSynthetic biologyen_US
dc.subjectPerturbation (Mathematics)en_US
dc.subjectReverse engineeringen_US
dc.titleDiscriminating Direct and Indirect Connectivities in Biological Networksen_US
dc.type.genrearticleen_US

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