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Predictive Polymer Models for 3D Chromosome Organization

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Hi-C Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2301))

Abstract

Polymer simulations and predictive mechanistic modelling are increasingly used in conjunction with experiments to study the organization of eukaryotic chromosomes. Here we review some of the most prevalent models for mechanisms which drive different aspects of chromosome organization, as well as a recent simulation scheme which combines several of these mechanisms into a single predictive model. We give some practical details of the modelling approach, as well as review some of the key results obtained by these and similar models in the last few years.

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Acknowledgements

This work was supported by ERC (CoG 648050, THREEDCELLPHYSICS). M.C. acknowledges the Carnegie Trust for the Universities of Scotland for PhD studentship funding.

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Authors and Affiliations

  1. SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK

    Michael Chiang, Davide Marenduzzo & Chris A. Brackley

  2. SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK

    Giada Forte

  3. MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK

    Giada Forte

  4. MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK

    Nick Gilbert

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  1. Michael Chiang
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  2. Giada Forte
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  3. Nick Gilbert
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  4. Davide Marenduzzo
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  5. Chris A. Brackley
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Corresponding author

Correspondence to Davide Marenduzzo .

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Editors and Affiliations

  1. Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy

    Silvio Bicciato

  2. IFOM, The FIRC Institute of Molecular Oncology, Milan, Italy

    Francesco Ferrari

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Chiang, M., Forte, G., Gilbert, N., Marenduzzo, D., Brackley, C.A. (2022). Predictive Polymer Models for 3D Chromosome Organization. In: Bicciato, S., Ferrari, F. (eds) Hi-C Data Analysis. Methods in Molecular Biology, vol 2301. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1390-0_14

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  • DOI: https://doi.org/10.1007/978-1-0716-1390-0_14

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1389-4

  • Online ISBN: 978-1-0716-1390-0

  • eBook Packages: Springer Protocols

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