Dr. Elizabeth Read (UC Irvine)
Noisy-omics: Statistical Inference and Stochastic Modelling to Shed Light on Gene Regulation
Time & Location
25 Nov 2020, 12:00 pm – 1:00 pm AEDT
Theory of Living Systems Webinar
About the Event
Cell biological data is increasingly available at single-cell, single-nucleotide, and singlemolecule resolution. Such experiments reveal often-unexpected levels of heterogeneity at these scales. In the Read lab, we use stochastic biochemical network modeling to quantitatively describe this heterogeneity and infer mechanistic insights from data.
I will present recent work in two areas: epigenetics and gene regulatory networks in development. In both areas, we use stochastic models to inform interpretation of noise in -omic (epigenomic, transcriptomic) data. Specifically, we find that genomic regional correlations derived from pulse-chase experiments on post-replication DNA methylation can be used to discriminate between enzymatic model assumptions. In the second area, we use stochastic models of gene regulatory networks to develop statistical measures for inference of gene-pair network interaction motifs.
About the speaker
Elizabeth Read is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at University of California Irvine. Her group uses computational approaches from engineering and the physical sciences to study cell-biological processes relevant to human health. They develop mathematical models, computer simulation tools, and statistical inference techniques. They apply these methods to a variety of areas, including immune cell activation and epigenetic regulation. The themes that link all of their projects are stochastic processes in cell biology and dynamics of biomolecular networks.