Abstract: Stochastic analysis for inferring gene expression models from single-cell and single-molecule experiments
In this high-level talk, I will first introduce how an experimental technique---single-molecule RNA fluorescent in situ hybridization (sm RNA FISH)---accurately 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 a class of coarse-grained stochastic models, formulated as continuous-time and individual-based chemical reactions in a well-mixed environment, is used to infer kinetic properties of stochastic gene expression from the experimental data. I will briefly discuss how our developed accurate sampling procedure efficiently solves the problem numerically (up to 1000-fold speed-up compared to conventional algorithms). 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 illustrate how we carry out the full-scale Bayesian analysis on our continuous-time probabilistic models using data from discrete-time observations.