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dc.contributor.authorPearce, Philip
dc.contributor.authorWoodhouse, Francis G
dc.contributor.authorForrow, Aden
dc.contributor.authorKelly, Ashley
dc.contributor.authorKusumaatmaja, Halim
dc.contributor.authorDunkel, Jörn
dc.date.accessioned2021-10-27T20:36:13Z
dc.date.available2021-10-27T20:36:13Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/1721.1/136608
dc.description.abstract© 2019, The Author(s). Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. Although efficient algorithms for cluster detection in high-dimensional spaces have been developed over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein-folding transitions, gene-regulatory network motifs, and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations, and phylogenetic trees, respectively. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein-sequencing datasets, and future cryo-electron microscopy (cryo-EM) data.
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.isversionof10.1038/S41467-019-13307-X
dc.rightsCreative Commons Attribution 4.0 International license
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceNature
dc.titleLearning dynamical information from static protein and sequencing data
dc.typeArticle
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematics
dc.relation.journalNature Communications
dc.eprint.versionFinal published version
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2021-05-19T12:27:35Z
dspace.orderedauthorsPearce, P; Woodhouse, FG; Forrow, A; Kelly, A; Kusumaatmaja, H; Dunkel, J
dspace.date.submission2021-05-19T12:27:36Z
mit.journal.volume10
mit.journal.issue1
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Needed


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