Quick Search:

Viruses, polytopes and machine learning

Dechant, Pierre-Philippe ORCID: https://orcid.org/0000-0002-4694-4010 (2021) Viruses, polytopes and machine learning. In: Nankai Symposium on Mathematical Dialogues, 2-13 August 2021, Chern Institute of Mathematics, Nankai University, Tianjin, China (via Zoom).

Full text not available from this repository.
Related URLs:


Viruses are a prime example of symmetry in nature. Polyhedra and related concepts such as Hamiltonian paths are a useful framework for modelling various aspects of virus structure and assembly. I will give an overview of how these occur in the structure of viruses and fullerenes, and how similar design principles can be used for the artificial construction of nanocages. Recent research has shown an assembly mechanism by which RNA can be cooperatively involved in the assembly of the viral shell. A toy model of the underlying biophysical effects and RNA feature space has been exhaustively explored using supercomputer simulations. Yang-Hui He and I have recently shown that this assembly fitness space can be machine learnt very quickly, hinting at some deeper structure hidden in the simulations results.

Item Type: Conference or Workshop Item (Paper)
Status: Published
School/Department: School of Science, Technology and Health
URI: http://ray.yorksj.ac.uk/id/eprint/5458

University Staff: Request a correction | RaY Editors: Update this record