Learning Collective Multicellular Dynamics with an Interacting Mean-Field Neural SDE Model

Date

Oct 30, 2025

Time

3:00 PM - 4:00 PM

Speaker

Lin Wan

Affiliation

Academy of Mathematics and Systems Science, Chinese Academy of Sciences; ELBE Visiting Faculty of CSBD

Language

en

Main Topic

Biologie

Host

Organisers: Nikola Sadovek, Maximilian Wiesmann, Giulio Zucal

Description

The advent of temporal single-cell RNA sequencing (scRNA-seq) data has enabled in-depth investigation of dynamic processes in heterogeneous multicellular systems. Despite remarkable advancements in computational methods for modeling cellular dynamics, integrating cell-cell interactions (CCIs) into these models remains a major challenge. This is particularly true when dealing with high-dimensional gene expression profiles from large populations of interacting cells, where the intricate interplay between cells can be obscured by data complexity. In this talk, I will present our recent work on a neural interacting mean-field stochastic differential equation (SDE) framework for temporal scRNA-seq data. Our approach combines mean-field modeling with neural networks to learn the dynamics of large, interacting cell populations directly from data. It enables the reconstruction of intrinsic cell population 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 multicellular systems.

Last modified: Oct 30, 2025, 7:38:30 AM