Barbara Engelhardt, PhD, builds machine-learning models and statistical tools to make use of data (scientific experiments, longitudinal studies, clinical trials, and hospital records) and find ways to better understand, and even prevent, disease. She is now joining Gladstone Institutes as a senior investigator.
Engelhardt is also a full professor at Princeton University, on leave this academic year. She joined the Princeton Computer Science Department in 2014 from Duke University, where she had been an assistant professor in Biostatistics and Bioinformatics and Statistical Sciences. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.
“Since I first learned about Gladstone during my postdoc, it’s always seemed like an oasis of amazing science,” says Engelhardt. “I can’t wait to start collaborating with all the scientists here.”
Engelhardt’s lab at Princeton is not how you might picture a traditional science lab—one with cells, glass beakers, and microscopes. Instead, she runs what’s called a dry lab, where her team uses powerful computers to analyze data through mathematical and computational approaches.
One of the group’s focus areas is to understand how cells work together in the body. The researchers look at how cells pass information to one another, how they work as part of neighborhoods, and how those neighborhoods are structured. Ultimately, they are trying to understand exactly how changes within cells or their environment can lead to disease.
Engelhardt studies how traumatic events that occur in your life are stored in your cells, how they may affect your genome, and how this can eventually lead to disease.
“You essentially store traumatic events in your cells, like a battery,” she says. “And then later in life, these traumas may lead to depression, type 2 diabetes, obesity, heart disease, or mental health problems.”
Her team has been working with the Fragile Families and Child Wellbeing Study, for which nearly 5,000 unmarried mothers were recruited between 1998 and 2000—a sample that includes a large number of Black, Hispanic, and low-income families. Data has been collected over the past 22 years about these children, their mothers, and, when possible, their fathers.
“Unfortunately, though perhaps not surprisingly, these kids have been through a lot,” says Engelhardt. “A large number of them have incarcerated fathers, they’ve witnessed or been involved in crime, they’ve experienced bullying at school, they’ve gone to bed hungry, and they’ve been evicted from their homes.”
The instability in their lives has been recorded in their cells and shows up in chemical changes to their DNA, which was collected as part of the study. Engelhardt is using all the data available about these families to understand how traumatic events get stored in their cells, in order to find a way to erase the records and prevent disease outcomes.
“It’s challenging to work with data from a group of individuals from such diverse backgrounds, but it’s absolutely critical, and it’s pretty exciting that we get to do it,” she says.
This article was adapted from an article published on Gladstone Institute
Image source: Quanta Magazine