Speaker: Smita Krishnaswamy (Yale University)

Title: Inferring and Characterizing Cellular and Neural Dynamics with Geometric and Topological Deep Learning

Abstract: In the last decade there has been a data revolution in biology with the advent of high-throughput high dimensional data modalities such as single-cell RNA-sequencing, fMRI data, molecular structure data and other modalities. A key issue in these data types is that they provide static snapshots of highly dynamic biological entities. In this talk I will cover our work inferring and characterizing cellular and neural dynamics during various processes. First, I will cover how to infer cell state dynamics during differentiation and disease with a neural ODE framework called MIOflow that is regularized with data geometric and manifold priors. Then I will discuss RITINI, our recent graph ODE network which allows us to learn gene regulation that underlies cellular dynamics, and potentially find new targets for treatments of disease. I will showcase applications of these in triple negative breast cancer and human embryonic stem cell differentiation. Once these dynamics are available, I will showcase tools to quantify and classify these dynamics based on graph signal processing and topological data analysis. This will involve our learnable geometric scattering transform to capture spatial signal patterns, as well as persistence homology and other tools to quantify time-varying patterns. Applications to characterization of brain activity data will be presented.

In person or on Zoom at https://mit.zoom.us/j/93513735220