The essential challenge posed by human health requires the understanding of the cell’s machinery at a molecular level. The interplay among proteins, DNA and RNA is key for vital functions such as DNA transcription, translation and epigenetics. To understand these processes many experimental techniques are put in action, spanning a very wide range in terms of spatial resolution, temporal resolution and level of detail with which they can observe the macromolecules. This is necessary if we consider that DNA alone spans 9 orders of magnitude in space, with regulating mechanisms occurring at the level of single base pairs, all the way up to chromosomes and with times ranging from picoseconds for base-pairs formation, to hours for large structural rearrangements such as those of G-quadruplexes on the telomeres of chromosomes.

As for all branches of science, theoretical (modeling) and experimental approaches have been developed over the years to study these systems, and, with no surprise, the most successful strategies are those for which the two approaches come together to give a full picture of the system [1,2]. Indeed, because of the diversity and complementarity of the experimental techniques, molecular modeling becomes a necessary tool to decode experimental data, bridging different sources of information and building a coherent structural model compatible with experiments.

From the modeling perspective, to understand a molecular structure, and have hints on its function, the starting point is the molecule's sequence. Over the last 30 years a multitude of bioinformatic tools have been developed to exploit this information to infer protein and nucleic acids structures [3,4]. These methods, however, based on relatively simple and empirical scoring functions, find their limitations for large and complex molecules [5,6]. Physical models, on the other hand, provide a more realistic picture of the molecule and, despite being more computationally expensive, are better suited for the study of large, complex systems. Once more, the combination of the two approaches is often beneficial [1,7], especially if either the bioinformatics or the physical model, or both, are able to incorporate experimental data from the start.

This workshop aims to collect several experts in various fields to allow for a wide and up-to-date overview of the current bioinformatics tools and simulation techniques and for a presentation of the most recently available experimental results and experimental methods. The meeting will be an opportunity to build an interdisciplinary community to bring new insights into complex biological systems and to boost the development of an exchange program between Europe and the USA in the framework of this consortium.




  1. K. Lasker, F. Forster, Stefan Bohn, Thomas Walzthoeni, Elizabeth Villa, Pia Unverdorben, Florian Beckc, Ruedi Aebersold, Andrej Sali,, and Wolfgang Baumeister, Proc. Natl. Acad. Sci. USA 109:1380 (2012)
  2. J. Pérard, C. Leyrat, F.Baudin, E. Drouet, M. Jamin  Nat. Comm. 4:1612 (2013).
  3. K. Rother, M. Rother, M. Boniecki, T. Puton, and J. M. B J. Mol. Model., 17:2325 (2011).
  4. B. Webb, A. Sali Curr. Prot. Protein Science 86: 2.9.1 (2016).
  5. Z. Miao RNA 23:655 (2017)
  6. M F. Lensink, S. Velankar; M. Baek, L. Heo C. Seok, S. J. Wodak Protein 86:257 (2018)
  7. S. Olsson, H. Wu, F. Paul, C. Clementi, F. Noé Proc. Natl. Acad. Sci. USA 114 : 8265 (2017).




Yassmine Chebaro, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Strasbourg

Ivan Coluzza, Center for Cooperative Research in Biomaterials, San Sebastian, Spain

Elisa Frezza, Université Paris Descartes

Nicolas Leulliot, Université Paris Descartes

Samuela Pasquali, Université Paris Descartes

Tamar Schlick, New York University

Fabio Sterpone, Institut de Biologie Physico Chimique, Paris


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