Speaker
Description
Mechanistic mathematical models are powerful tools in modern life sciences. Similar to experimental techniques, mechanistic models enable the investigation of biological processes and hypothesis testing. Furthermore, they allow the integrative analysis of multiple datasets as well as the prediction of latent variables and future experimental outcomes.
In this talk, I will outline how mechanistic modeling can support the analysis and integration of large-scale datasets. First, I will present tailored computational methods for training large-scale mechanistic models of cellular pathways using multi-omic data. This includes recent advances in combining mechanistic modeling with machine learning approaches to enable scalable inference and the handling of qualitative measurements. Second, I will introduce methods for handling heterogeneous data types and and population-level variability. In particular, I will discuss approaches for the analysis of cell-to-cell variability using amortized inference techniques, which enable efficient inference in complex stochastic and multi-cellular models.
