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SUMMARY:Application of Persistent Homology to Biological Networks - Bernad
 ette Stolz (Oxford)
DTSTART:20160610T140000Z
DTEND:20160610T150000Z
UID:TALK66040@talks.cam.ac.uk
CONTACT:Christian Lund
DESCRIPTION:Computational topology is a set of algorithmic methods develop
 ed to understand topological invariants such as loops and holes in high-di
 mensional data sets. In particular\, a method know as persistent homology 
 has been used to understand such shapes and their persistence in point clo
 uds and networks. It has only been applied in biological contexts in recen
 t years.\n\nIn network science\, most tools focus solely on local properti
 es based on pairwise connections\, the topological tools reveal more globa
 l features. I apply persistent homology to biological networks to see whic
 h properties these tools can uncover\, which might be invisible to existin
 g methods. In my talk I will show the use of three different methods from 
 Computational Topology\, so called filtrations: a filtration by weights\, 
 a weight rank clique filtration and a Vietoris-Rips filtration to analyse 
 networks. The example networks I apply these tools to consist of fMRI data
  from neuroscientific experiments on human motor-learning and the study of
  schizophrenia\, a mathematical oscillator model (the Kuramoto model)\, as
  well as imaging data from tumour blood vessel networks. In all cases I wi
 ll show how these tools reveal insights into the biology or dynamics of th
 e studied problems.
LOCATION:MR13
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