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NANO-D-POST Research team

Algorithmes pour la Modélisation et la Simulation de Nanosystèmes

Team presentation

The goal of the team is to help experimental biologists, physicists, and bioinformaticians to predict the structure, conformational heterogeneity and function of various macromolecular machines. This will be made possible thanks to developing novel mathematical, algorithmic, and computational approaches and also by using advances in several research fields, such as various experimental techniques and data science.

Research themes

Our research axes are: 

  • Developing novel physics-based computational methods for integrative structural biology. These include modeling of scattering experiments (i.e. SAXS and SANS), modeling of cross-link experiments, modeling of FRET experiments, inclusion of Cryo-EM, NMR, and XFEL data, modeling of missing structural fragments (loops and termini), adapting physics-based force-fields, developing and integrating docking algorithms, and using the theory of linear elasticity to model large-scale macromolecular flexibility.
  • Developing novel data-driven algorithms. These include methods for both the analysis of genomic and 3D structural databases and also for learning the models from these data. The ultimate goal of this axis is learning the organization of macromolecules and their complexes at physiological conditions. This includes learning physical models for the interactions within the system under study (the enthalpic contribution), and also the low-dimensional representation of the conformational variability of the system (the entropic contribution).
  • Combining knowledge-based and physics-based approaches together and developing practical user interfaces and applications. We will primarily develop stand-alone tools and later integrate them into web-based applications.