ArticleNature communications2019
Learning dynamical information from static protein and sequencing data.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Energy landscape decomposition for cell differentiation with proliferation effect.National science review · 2022Article
- Reduction of a stochastic model of gene expression: Lagrangian dynamics gives access to basins of attraction as cell types and metastabilty.Journal of mathematical biology · 2021Article
- Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.Nature communications · 2021Article
- A Dimension Reduction Approach for Energy Landscape: Identifying Intermediate States in Metabolism-EMT Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics.In silico biology · 2021Article
- A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.The Journal of chemical physics · 2020Article
- Conformational state switching and pathways of chromosome dynamics in cell cycle.Applied physics reviews · 2020Article
- A coarse-grained model of the expansion of the human rhinovirus 2 capsid reveals insights in genome release.Journal of the Royal Society, Interface · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.