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Mechanistic modeling of chromatin folding to understand function

Nature Methods volume 17, pages 767–775 (2020)Cite this article

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Abstract

Experimental approaches have been applied to address questions in understanding three-dimensional chromatin organization and function. As datasets increase in size and complexity, it becomes a challenge to reach a mechanistic interpretation of experimental results. Polymer simulations and mechanistic modeling have been applied to explain experimental observations and their links to different aspects of genome function. Here we provide a guide for biologists, explaining different simulation approaches and the contexts in which they have been used.

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Fig. 1: Coarse-grained molecular dynamics simulations of chromatin.
Fig. 2: Commonly used models for understanding chromosome organization.
Fig. 3: A gene locus model: the Highly Predictive Heteromorphic Polymer model (HiP-HoP).

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Acknowledgements

The authors would like to thank members of their groups for stimulating discussions. Research in the Marenduzzo group is supported by the European Research Council (CoG 648050, THREEDCELLPHYSICS); research in the Gilbert lab is funded by the UK Medical Research Council (MR/J00913X/1 and MC_UU_00007/13).

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Authors and Affiliations

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

    Chris A. Brackey & Davide Marenduzzo

  2. MRC Human Genetics Unit, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK

    Nick Gilbert

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  1. Chris A. Brackey
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  2. Davide Marenduzzo
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C.A.B. designed and co-wrote the manuscript. D.M. co-wrote the manuscript. G.N. conceived and co-wrote the manuscript.

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Correspondence to Davide Marenduzzo or Nick Gilbert.

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Peer review information Lei Tang and Nicole Rusk were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Brackey, C.A., Marenduzzo, D. & Gilbert, N. Mechanistic modeling of chromatin folding to understand function. Nat Methods 17, 767–775 (2020). https://doi.org/10.1038/s41592-020-0852-6

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