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SUMMARY:Automated mapping of multi-cellular motifs during tissue morphogen
 esis
DTSTART:20190530T110000
DTEND:20190530T120000
DTSTAMP:20260919T102052Z
UID:d8058e4887e9b89fc94def9e7fcd242d2f475516548c5503451ad390
CATEGORIES:Conferences - Seminars
DESCRIPTION:Tomer Stern - In 2016 Tomer Stern received his Ph.D. in comput
 ational biology from the Weizmann institute of science in Israel. There\, 
 together with prof. Elazar Zelzer he focused on deciphering the developmen
 tal mechanisms regulating the morphology of the ossified bone and the cart
 ilaginous template using the mouse model. Since 2017 he is a postdoctoral 
 fellow at Princeton University as well as an EMBO fellow\, and together wi
 th Profs. Eric Wieschaus and Stas Shvartsman he is developing a unified qu
 antitative approach for mining tissue behaviors from live images using the
  fruit fly model.\n\nResearch over the last decades has identified an incr
 easing repertoire of conserved cellular behaviors\, or “motifs”\, that
  act as building blocks of tissue morphogenesis. However\, a comprehensive
  framework for the exploration and analysis of these motifs\, similar to t
 he frameworks used to map and discover motifs in sequence data\, is yet to
  be established. In this talk I will present our first step towards this g
 oal by developing a generic algorithm that can learn to recognize any sub-
  to multi-cellular behavior from user provided examples\, and accurately m
 ap its appearances in live imaging data. Our strategy relies on the transf
 ormation of the intricate geometry\, topology and molecular expression pro
 files of cells in a developing tissue into time-series data\, thereby allo
 wing to address the problem as a subsequence matching task. Using this app
 roach we mapped intercalary behaviors\, namely T1-transitions and rosettes
 \, during Drosophila germband extension in wild type embryos and embryos l
 acking the AP patterning information\, revealing differences in the kineti
 cs of junction contraction as compared to elongation. Moreover\, using Mon
 te-Carlo simulations we show that the frequencies of T1-transitions and 5-
  and 6-cell rosettes can be predicted by the spatial density of contractin
 g junctions within the tissue.\nWe believe that in the future our approach
  will begin to play in the study of tissue development the same role as st
 andard sequence analysis is playing in the discovery of regulatory interac
 tions in DNA and protein data.\n 
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
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