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BEGIN:VEVENT
UID:DSC-22374
DTSTART;TZID=Europe/Berlin:20251030T150000
SEQUENCE:1761806310
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20251030T160000
URL:https://www.dresden-science-calendar.de/calendar/de/detail/22374
LOCATION:MPI-CBG\, Pfotenhauerstraße 10801307 Dresden
SUMMARY:Wan: Learning Collective Multicellular Dynamics with an Interacting
  Mean-Field Neural SDE Model
CLASS:PUBLIC
DESCRIPTION:Speaker: Lin Wan\nInstitute of Speaker: Academy of Mathematics 
 and Systems Science\, Chinese Academy of Sciences\; ELBE Visiting Faculty 
 of CSBD\nTopics:\n\n Location:\n  Name: MPI-CBG (MPI-CBG CSBD SR Top Floor
  (VC))\n  Street: Pfotenhauerstraße 108\n  City: 01307 Dresden\n  Phone: 
 +49 351 210-0\n  Fax: +49 351 210-2000\nDescription: The advent of tempora
 l single-cell RNA sequencing (scRNA-seq) data has enabled in-depth investi
 gation of dynamic processes in heterogeneous multicellular systems. Despit
 e remarkable advancements in computational methods for modeling cellular d
 ynamics\, integrating cell-cell interactions (CCIs) into these models rema
 ins a major challenge. This is particularly true when dealing with high-di
 mensional gene expression profiles from large populations of interacting c
 ells\, where the intricate interplay between cells can be obscured by data
  complexity. In this talk\, I will present our recent work on a neural int
 eracting mean-field stochastic differential equation (SDE) framework for t
 emporal scRNA-seq data. Our approach combines mean-field modeling with neu
 ral networks to learn the dynamics of large\, interacting cell populations
  directly from data. It enables the reconstruction of intrinsic cell popul
 ation trajectories and the systematic characterization of CCIs. Notably\, 
 the model uncovers biologically interpretable\, non-reciprocal interaction
  patterns and offers a principled way to study complex\, non-equilibrium m
 ulticellular systems.
DTSTAMP:20260818T110302Z
CREATED:20251015T053504Z
LAST-MODIFIED:20251030T063830Z
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