IMAG/MSM WG Multiscale Modeling and Viral Pandemics Zoom @ everyone. scMod: Marrying machine learning and deterministic modelling of longitudinal single-cell data

About this Class

The schedule for this week consists of one presentation:

Lorenz Adlung, UMC Hamburg-Eppendorf, will discuss: scMod: Marrying machine learning and deterministic modelling of longitudinal single-cell data
Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of biological processes. However, despite their high throughput, these measurements represent only a snapshot in time. But longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modeling to mechanistically describe molecular or cellular dynamics. In my talk, I will present two examples of how we are using time-resolved single-cell datasets to gain a better understanding of cellular signaling, immune responses, and tissue regeneration. Our multidisciplinary efforts are focused on developing methods for applying predictive models in biomedical contexts. For example, we envision that deconvolution of time-resolved bulk mRNA sequencing data could complement scRNA-seq resources, e.g. from the Human Cell Atlas, for ODE-based modeling to leverage large-scale single-cell data in clinical practice.