Atte Aalto: Gene expression modelling from experimental data

Abstract

Developments in transcriptomics techniques have caused a large demand for tailored computational methods for modelling gene expression dynamics from experimental data. State-of-the-art modelling is based on time series data. Due to high cost of experiments, the time series are typically short and the sampling rate is low. In the first part of my talk, I will briefly discuss the challenges of modelling from short time series. Recently, so-called single-cell experiments have revolutionised genetic studies. These experiments yield gene expression data in single cell resolution for a large number of cells at a time. However, the cells are destroyed in the measurement process, and so the data consist of snapshots of an ensemble evolving over time. In the second part of my talk, I will discuss how gene regulatory dynamics can be modelled from such population snapshot data. I will present an approach based on tracking the evolution of the distribution of cells over time and discuss future directions.