Learning dynamical information from static protein and sequencing data
| العنوان: | Learning dynamical information from static protein and sequencing data |
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| المؤلفون: | Francis G. Woodhouse, Ashley J. Kelly, Philip L. Pearce, Jörn Dunkel, Aden Forrow, Halim Kusumaatmaja |
| المصدر: | Nature Communications, Vol 10, Iss 1, Pp 1-8 (2019) Nature communications, 2019, Vol.10, pp.5368 [Peer Reviewed Journal] Nature Communications |
| بيانات النشر: | Nature Publishing Group, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | 0301 basic medicine, Protein Folding, Computer science, Science, Gaussian, Gene regulatory network, General Physics and Astronomy, Inference, Markov process, Molecular Dynamics Simulation, Network topology, 01 natural sciences, Article, General Biochemistry, Genetics and Molecular Biology, Evolution, Molecular, 03 medical and health sciences, symbols.namesake, Molecular dynamics, 0103 physical sciences, Gene Regulatory Networks, 010306 general physics, lcsh:Science, Quantitative Biology::Biomolecules, Multidisciplinary, Markov chain, Dimensionality reduction, Computational Biology, HIV, Proteins, Energy landscape, RNA, General Chemistry, Applied mathematics, Network dynamics, Markov Chains, Computational biology and bioinformatics, System dynamics, 030104 developmental biology, Viral evolution, Path (graph theory), symbols, Protein folding, lcsh:Q, Biological physics, Algorithm |
| الوصف: | 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. Reconstructing system dynamics on complex high-dimensional energy landscapes from static experimental snapshots remains challenging. Here, the authors introduce a framework to infer the essential dynamics of physical and biological systems without need for time-dependent measurements. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2041-1723 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8663e4b2b8c4e89a0d452449f159c944 http://link.springer.com/article/10.1038/s41467-019-13307-x |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....8663e4b2b8c4e89a0d452449f159c944 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20411723 |
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