CCB Colloquium: Justin Kinney

Name: CCB Colloquium: Justin Kinney
Start: 2021-01-26T13:00:00-05:00
End: 2021-01-26T14:00:00-05:00
Location: Virtual

Tuesday January 26, 2021

Presenter: Justin B. Kinney, Ph.D., Assistant Professor
Simons Center for Quantitative Biology
Cold Spring Harbor Laboratory

Topic: Massively Parallel Reporter Assays, Machine Learning, and the Biophysics of Gene Regulation

Abstract: Gene expression in all organisms is controlled by short DNA and RNA sequences called cis-regulatory elements (CREs). Proteins in the cellular milieu bind to nucleic acid sequences present within CREs, interact with one another, and thus form macromolecular complexes that modulate the expression of nearby genes. My lab uses a combination of experiments and mathematical modeling to study the biophysics of these regulatory processes in living cells. Our experiments are based on massively parallel reporter assays, which leverage the power of ultra-high-throughput DNA sequencing to measure the effects that thousands to millions of different CRE sequence perturbations have on gene expression. Our mathematical modeling efforts aim to infer biophysical models from the large DNA sequence datasets that these experiments yield. This machine learning task has proven to be remarkably fertile from a theoretical standpoint, tying together ideas in nonparametric Bayesian inference, information theory, dimensionality reduction, and deep learning.

About the Speaker

Dr. Kinney completed his Ph.D. in Physics at Princeton University in 2008 and began his term as a Quantitative Biology Fellow at CSHL in 2010. His research focuses on developing next-generation DNA sequencing as a tool for dissecting the biophysical mechanisms of gene regulation. As a graduate student, Kinney co-invented a widely used technique now known as the massively parallel reporter assay (MPRA). Kinney and colleagues further showed how, using ideas from information theory, such experiments could be used to infer quantitative biophysical models for how cells regulate gene expression. The Kinney lab continues to leverage a tightly knit combination of mathematical theory, machine learning, and experiments in order to illuminate the biophysics of gene regulation in two diverse contexts: bacterial transcriptional regulation and alternative mRNA splicing in humans. This latter context is highly relevant to understanding and treating human diseases like Spinal Muscular Atrophy and Cancer. The Kinney lab also develops algorithms and software for the analysis of MPRAs and other multiplex assays of variant effect (MAVEs).