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UID:20260830T190218EDT-8278uCo66b@132.216.98.100
DTSTAMP:20260830T230218Z
DESCRIPTION:With the advance of machine learning\, there has been a renewed
  interest in the inference and the study of the geometry underlying biolog
 ical dynamics. I will present two recent applications of such ideas\, wher
 e our group could combine theory and experiment to build predictive models
  of complex biological processes. In the context of embryonic development\
 , experimental data on the entrainment/coupling of the segmentation oscill
 ator\, and new geometric models of somitogenesis\, suggest the existence o
 f an asymmetric core oscillator (work in collaboration with Alexander Aule
 hla\, EMBL Heidelberg). In the context of immune response\, we derived exp
 licitly a simple geometric picture to describe complex cytokine dynamics\,
  revealing a universal 'antigen encoding'\, with applications for CAR-T ce
 ll based immunotherapy (work in collaboration with Grégoire Altant-Bonnet\
 , NIH Bethesda). Our work reveals how systems level descriptions can be bu
 ilt from data\, leading to surprisingly accurate descriptions and associat
 ed predictions.\n\nThis seminar will be offered online via Zoom. Details i
 n attached poster.\n
DTSTART:20211217T160000Z
DTEND:20211217T170000Z
SUMMARY:Online seminar: Determining the geometry of biological dynamics fro
 m the data
URL:https://www.mcgill.ca/physiology/channels/event/online-seminar-determin
 ing-geometry-biological-dynamics-data-335486
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