Title: Using Stochastic Models to Understand, Plan, and Control Gene Expression in Single Cells

Speaker: Zach Fox, Los Alamos National Lab

Abstract: Basic cellular behavior, such as the transcription of DNA and translation of RNA within a cell, are well described by stochastic processes that are discrete and non-Gaussian. In the last 10 years, experimental platforms have allowed us to measure these fluctuations in individual cells. When viewed from the inference perspective, these non-Gaussian fluctuations provide a plethora of information about how genes are regulated. This talk will describe computational and statistical methods that make use of fluctuations to (1) reduce computational burdens associated with complex stochastic models, (2) design optimal experiments to estimate relevant biochemical parameters using Fisher information, and (3) control the gene expression of individual cells using advanced microscopy, machine learning, and stochastic models. Overall, we will show that non-Gaussian fluctuations in biological data can impact how we fit, predict, and use models to generate new data types.