專題演講

2018/07/17(Tue)     10:30 -11:30    5th Floor(1st meeting room)

Title

From single-cell experiments to statistical inference of gene expression models: A physicist’s perspective

Speaker

Dr. Yen-ting Lin

Los Alamos National Laboratory

Abstract

What happens when a mathematical physicist studying random processes collaborates with biologists studying stochastic gene expression in single cells? In this talk, I will first introduce an experimental technique---single-molecule RNA fluorescent in situ hybridization (sm RNA FISH)---which measures transcribed mRNA and the discrete state of activation in a single cell, and provides a ``snapshot'' of the stochastic process of gene expression. Then, I will discuss how we use a class of coarse-grained stochastic models, formulated as continuous-time and individual-based chemical reactions in a well-mixed environment, to infer kinetic properties of stochastic gene expression from the experimental data. I will present an accurate sampling procedure (up to 1000-fold speed-up compared to conventional algorithms) to efficiently solve the problem numerically. The increased efficiency permits us to go beyond standard fitting procedures and enter to the realm of statistical inference . In the final part of the talk, I will present a high-level description of how we carry out the full-scale Bayesian analysis on our continuous-time probabilistic models using data from discrete-time observations. The outcome of the analysis, the uncertainty quantification of the parameters and model structures, will be presented.