Speaker
Description
Artificial Intelligence Virtual Cell (AIVC) is increasingly emerging as a frontier in the interdisciplinary integration of biology and artificial intelligence. Its core vision is to construct digital twins capable of simulating and predicting the dynamic evolution of cellular states, thereby providing computational support for experimental design and mechanistic analysis. We have explored a unified modeling framework that integrates generative artificial intelligence methods and dynamical systems theory. By combining mathematical theories such as Optimal Transport, Schrödinger Bridge, and differential geometry with generative AI techniques like Flow Matching and diffusion models, this framework effectively infers continuous dynamic processes of complex state transitions—such as cell proliferation, apoptosis, differentiation, migration, and interactions—from static, heterogeneous single-cell omics temporal snapshots. Compared to black-box methods, this approach not only exhibits strong generative and generalization capabilities but also offers improved mechanistic interpretability. It enables the generation of cell state data across temporal scales and spatial structures, providing a promising direction for constructing dynamic virtual cell models with interpretability, predictive power, and the ability to integrate biological priors.
