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UID:20251024T091704EDT-7723gU5VwL@132.216.98.100
DTSTAMP:20251024T131704Z
DESCRIPTION:Title : Regulatory networks: searching for periodic orbits\nAbs
 tract : In cells\, proteins are produced by DNA sequences. The rate of pro
 duction is influenced by the presence of other proteins connected to the D
 NA sequence. We can represent such interaction by a regulatory network\, a
  directed graph where the edge i—> j exists if x_i affects the rate of cha
 nge of x_j. For biological reasons\, we expect x_i to appear in the rate o
 f change of x_j through a sigmoidal function\, H_{ij}.\nIn this talk\, we 
 first approximate each sigmoidal function H_{ij} by a step function. Each 
 step function is defined by three parameters and we expect the parameter s
 pace to be high dimensional. In such high dimensional space\, we want to d
 etermine which choice of parameters allow for oscillations in a given netw
 ork. For this goal\, we will use a combination of combinatorial tools and 
 Morse graphs.\nOnce we have found parameters compatible with oscillations 
 using step functions\, we want to discuss the stability of such oscillatio
 ns if the step functions are smoothened back into sigmoidals. For this\, w
 e will discuss tools developed in the context of machine learning.
DTSTART:20200120T210000Z
DTEND:20200120T220000Z
LOCATION:BURN 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Elena Queirolo (Rutgers University)
URL:/mathstat/channels/event/elena-queirolo-rutgers-un
 iversity-310191
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