dc.contributor.author | Pearce, Philip | |
dc.contributor.author | Woodhouse, Francis G | |
dc.contributor.author | Forrow, Aden | |
dc.contributor.author | Kelly, Ashley | |
dc.contributor.author | Kusumaatmaja, Halim | |
dc.contributor.author | Dunkel, Jörn | |
dc.date.accessioned | 2021-10-27T20:36:13Z | |
dc.date.available | 2021-10-27T20:36:13Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | https://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.iso | en | |
dc.publisher | Springer Science and Business Media LLC | |
dc.relation.isversionof | 10.1038/S41467-019-13307-X | |
dc.rights | Creative Commons Attribution 4.0 International license | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Nature | |
dc.title | Learning dynamical information from static protein and sequencing data | |
dc.type | Article | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mathematics | |
dc.relation.journal | Nature Communications | |
dc.eprint.version | Final published version | |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
eprint.status | http://purl.org/eprint/status/PeerReviewed | |
dc.date.updated | 2021-05-19T12:27:35Z | |
dspace.orderedauthors | Pearce, P; Woodhouse, FG; Forrow, A; Kelly, A; Kusumaatmaja, H; Dunkel, J | |
dspace.date.submission | 2021-05-19T12:27:36Z | |
mit.journal.volume | 10 | |
mit.journal.issue | 1 | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | |