ArticleNucleic acids research2017
Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 30 citations in OpenAlex.
- Modeling conformational transitions in DNA, RNA, and protein-nucleic acid complexes.Nucleic acids research · 2026Article
- Generating three-dimensional genome structures with a variational quantum algorithm.Briefings in bioinformatics · 2025Article
- Integrative Modeling of 3D Genome Organization by Bayesian Molecular Dynamics Simulations with Hi-C Metainference.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Genome structural dynamics: insights from Gaussian network analysis of Hi-C data.Briefings in functional genomics · 2024Article
- Multiscale Bayesian simulations reveal functional chromatin condensation of gene loci.PNAS nexus · 2024Article
- Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing.International journal of molecular sciences · 2023Article
- Scipion Flexibility Hub: an integrative framework for advanced analysis of conformational heterogeneity in cryoEM.Acta crystallographica. Section D, Structural biology · 2023Article
- Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow.PLoS computational biology · 2022Article
- State-dependent sequential allostery exhibited by chaperonin TRiC/CCT revealed by network analysis of Cryo-EM maps.Progress in biophysics and molecular biology · 2021Article
- A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C.PLoS computational biology · 2021Article
- PHi-C: deciphering Hi-C data into polymer dynamics.NAR genomics and bioinformatics · 2020Article
- Microrheology for Hi-C Data Reveals the Spectrum of the Dynamic 3D Genome Organization.Biophysical journal · 2020Article
- BHi-Cect: a top-down algorithm for identifying the multi-scale hierarchical structure of chromosomes.Nucleic acids research · 2020Article
- Differences in the intrinsic spatial dynamics of the chromatin contribute to cell differentiation.Nucleic acids research · 2020Article
- Tales from topographic oceans: topologically associated domains and cancer.Endocrine-related cancer · 2019Review
- Coarse-Grained Protein Dynamics Studies Using Elastic Network Models.International journal of molecular sciences · 2018Review
- Exploring chromatin hierarchical organization via Markov State Modelling.PLoS computational biology · 2018Article
- Quantifying the similarity of topological domains across normal and cancer human cell types.Bioinformatics (Oxford, England) · 2018Article
- Chromatin loops and causality loops: the influence of RNA upon spatial nuclear architecture.Chromosoma · 2017Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Understanding the three-dimensional (3D) architecture of chromatin and its relation to gene expression and regulation is fundamental to understanding how the genome functions. Advances in Hi-C technology now permit us to study 3D genome organization, but we still lack an understanding of the structural dynamics of chromosomes. The dynamic couplings between regions separated by large genomic distances (>50 Mb) have yet to be characterized. We adapted a well-established protein-modeling framework, the Gaussian Network Model (GNM), to model chromatin dynamics using Hi-C data. We show that the GNM can identify spatial couplings at multiple scales: it can quantify the correlated fluctuations in the positions of gene loci, find large genomic compartments and smaller topologically-associating domains (TADs) that undergo en bloc movements, and identify dynamically coupled distal regions along the chromosomes. We show that the predictions of the GNM correlate well with genome-wide experimental measurements. We use the GNM to identify novel cross-correlated distal domains (CCDDs) representing pairs of regions distinguished by their long-range dynamic coupling and show that CCDDs are associated with increased gene co-expression. Together, these results show that GNM provides a mathematically well-founded unified framework for modeling chromatin dynamics and assessing the structural basis of genome-wide observations.
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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.