Yuchi Qiu, UC Irvine: intelligence (AI) has emerged as a pivotal tool in biology, revolutionizing data analysis at both large-scale and single-cell levels. However, the lack of interpretability in AI poses challenges in extracting intricate functions and dynamics from high-dimensional, complex heterogeneous, and noisy biological data. In this talk, we aim to address these challenges by investigating dynamics and topology of data via multiscale modeling and topological data analysis. First, we will discuss our approaches for deciphering cellular spatio-temporal dynamics, focusing on the interplay between gene regulation, spatial signals, and intercellular mechanical interactions. Our approaches include stochastic simulations, the subcellular element method, and reaction diffusion equations. Building upon this foundation, we have developed a deep learning-based dynamical model using unbalanced dynamic optimal transport to connect time-course single-cell transcriptomic snapshots and interrogate underlying gene regulatory networks. Lastly, we will discuss AI models designed to expedite protein design that incorporate a persistent spectral Laplacian method, large language models, and a hierarchical clustering-based Bayesian optimization approach.