Machine Learning Seminar Series with Natalie Boehnke (CEMS, UMN)

Leveraging machine learning and omics toolsets to elucidate genomic determinants of nanoparticle delivery

Clinical translation of nanomedicine is hampered by limited accumulation at target disease sites. The complexity and heterogeneity of both biological environment and nanoparticle construct make it prohibitively challenging to deconvolute individual factors that drive nanocarrier targeting and accumulation. We have leveraged library-based and pooled cell screening approaches to gain a holistic understanding of both nanomaterials properties and biological features that mediate successful drug delivery. Through integration of omics data via supervised machine learning, we have identified predictive genomic features that modulate uptake of specific nanocarriers. In this talk, I will describe our nanoparticle library and pooled screen design as well as the results from these integrated screens, which indicate that matching nanocarrier properties and genomic profiles may be a key component to achieving targeted delivery.
 
About Natalie Boehnke
Natalie Boehnke is a new assistant professor in the Department of Chemical Engineering and Materials Science at the University of Minnesota. She received her Ph.D. in chemistry from UCLA in 2017 and recently completed postdoctoral training at the Koch Institute for Integrative Cancer Research at MIT. Her research interests include gaining a fundamental understanding of the mechanisms mediating drug delivery to accelerate clinical translation of nanomedicine, focusing on using high throughput screening, omics, and machine learning approaches.