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Advances Using Single-Particle Trajectories to Reconstruct Chromatin Organization and Dynamics.
Shukron, O; Seeber, A; Amitai, A; Holcman, D.
Afiliação
  • Shukron O; Group of Data Modeling, Computational Biology and Predictive Medicine, Institut de Biologie, CNRS/INSERM/PSL Ecole Normale Supérieure, Paris, 75005, France.
  • Seeber A; Center for Advanced Imaging, Northwest Building, 52 Oxford St, Suite 147, Harvard University, Cambridge, MA, 02138, USA.
  • Amitai A; Department of Chemical Engineering, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; The Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA.
  • Holcman D; Group of Data Modeling, Computational Biology and Predictive Medicine, Institut de Biologie, CNRS/INSERM/PSL Ecole Normale Supérieure, Paris, 75005, France. Electronic address: david.holcman@ens.fr.
Trends Genet ; 35(9): 685-705, 2019 09.
Article em En | MEDLINE | ID: mdl-31371030
Chromatin organization remains complex and far from understood. In this article, we review recent statistical methods of extracting biophysical parameters from in vivo single-particle trajectories of loci to reconstruct chromatin reorganization in response to cellular stress such as DNA damage. We look at methods for analyzing both single locus and multiple loci tracked simultaneously and explain how to quantify and describe chromatin motion using a combination of extractable parameters. These parameters can be converted into information about chromatin dynamics and function. Furthermore, we discuss how the timescale of recurrent encounter between loci can be extracted and interpreted. We also discuss the effect of sampling rate on the estimated parameters. Finally, we review a polymer method to reconstruct chromatin structure using crosslinkers between chromatin sites. We list and refer to some software packages that are now publicly available to simulate polymer motion. To conclude, chromatin organization and dynamics can be reconstructed from locus trajectories and predicted based on polymer models.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cromatina / Modelos Biológicos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Trends Genet Assunto da revista: GENETICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: França País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cromatina / Modelos Biológicos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Trends Genet Assunto da revista: GENETICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: França País de publicação: Reino Unido