Bioinformatics: Developing molecular dynamics tools to understand DNA 3D organization

Our DNA is organized into spatially distinct compartments that affect important functions in the cell [1]. Thus, cell function depends not only on the linear arrangement of DNA elements but also on their three-dimensional (3D) organization. However, the mechanisms by which sub-nuclear compartments (See Figure.1) are structured and maintained, as well as what determines their composition, size and shape at various phases of the cell cycle remain substantially unknown.



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Molecular dynamics is a promising, new way to explore 3D genome organization. To this end, physical models for chromosomal DNA combining the fundamental polymer physics aspects along with the experimentally determined behavior of the proteins involved in the formation of chromosomal domains can be built. These models[2] address the physics of chromatin compaction and aids in interpreting experimentally observed structures.

LAMMPS[3] is one of the most broadly used computational tools for running simulations for research in molecular dynamics. While the tool itself is relatively easy to use, researchers often need to customize it to meet specific simulation requirements. In this project, you will focus on developing necessary and useful extensions to this software to study the coarse-grained systems through molecular dynamics simulations. The aim will be to develop tools to explore a set of minimal key parameters that play a role in the mechanisms underlying genome 3D organization and domain formation in the cell nucleus. This will potentially reveal new insights into how our cells function, both in normal conditions and in diseases like cancer.

This project could be of potential interest to students interested in interdisciplinary research domains including computer science, biology and physics. Thus, understanding basic concepts in physics and having a good knowledge of C++ and MPI is required.

[1] J. Dekker, K. Rippe, M. Dekker, N. Kleckner, Science, 295, 1306 (2002); E. Lieberman-Aiden, N. L. van Berkum, L. Williams, M. Imakaev, T. Ragoczy, et al, Science, 326, 289 (2009); J. R. Dixon, S. Selvaraj, F. Yue, A. Kim, Y. Li, et al, Nature, 485, 376 (2012).
[2] N. Nahali, A. Rosa, The Journal of Chemical Physics, 148(19), 194902 (2018); N. Nahali, D. Michieletto, A. Rosa, Physical Review Letters, 119(19), 197801 (2017).[3] Plimpton S 1995 Fast parallel algorithms for short range molecular dynamics J. Comput. Phys. 117 1–19

Emneord: Bioinformatics, biophysics, Molecular dynamics
Publisert 28. sep. 2021 10:13 - Sist endret 28. sep. 2021 10:14

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