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SUMMARY:Atomistic graph partitioning across scales: from molecules to prec
 ision healthcare - Professor Sophia Yaliraki\, Imperial College London
DTSTART:20180530T131500Z
DTEND:20180530T141500Z
UID:TALK98119@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:We have derived an all-scale graph partitioning approach that 
 preserves atomistic physico-chemical detail and by using diffusive process
 es on the graph (both on the node and the edge space)\, we have shown that
  we can obtain the behaviour of biomolecules and bimolecular assemblies at
  different timescales without the need of any reparametrisation or a prior
 i selection of relevant timescales. The approach is computationally effici
 ent and general and can be applied to molecules\, molecular assemblies as 
 well as data. I will discuss  the theory that brings together graph theory
 \, dynamics and diffusive processes and showcase it with examples from pre
 dictions and experimental verification of mutations that control protein d
 ynamics at different scales (AdK)\, prediction of allosteric sites for dru
 g design and communication and signalling in multimers and assemblies (ATC
 ase\, Rubisco). Finally\, the application of this unsupervised learning ap
 proach to trajectories and data will be briefly discussed.
LOCATION:Department of Chemistry\, Cambridge\, Unilever lecture theatre
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