Ernest Fraenkel - Biology, driven by data
Newly tenured biological engineer Ernest Fraenkel goes where the numbers lead.
Anne Trafton | MIT News Office
January 27, 2015
are incredibly complicated machines with thousands of interacting parts
— and disruptions to any of those interactions can cause disease.
Tracing those connections to seek the root cause of disease is a
daunting task, but it is one that MIT biological engineer Ernest
Fraenkel relishes. His lab takes a systematic approach to the problem:
By comparing datasets that include thousands of events inside healthy
and diseased cells, they can try to figure out what has gone awry in
cells that are not functioning properly.
“The central challenge of this field is how you take all those
different kinds of data to get a coherent picture of what’s going on in a
cell, what is wrong in a diseased cell, and how you might fix it,” says
Fraenkel, an associate professor of biological engineering.
This type of computational modeling of biological interactions, known
as systems biology, can help to reveal possible new drug targets that
might not emerge through more traditional biological studies. Using this
approach, Fraenkel has deciphered some key interactions that underlie
Huntington’s disease as well as glioblastoma, an incurable type of brain
Science without borders
As a high-school student in New York City, Fraenkel had broad
interests, and participated in a special program where physics,
chemistry, and biology were taught together. The program’s teacher, a
Columbia University student, suggested that Fraenkel do some summer
research at a lab at Columbia. The lab was run by Cyrus Levinthal, a
physicist who had previously taught one of the first biophysics classes
“He had this cool lab where they were doing image analysis of
neurons, and modeling proteins, and doing experiments. I just thought it
was fantastic. That’s when I decided I wanted to go into science,”
He enjoyed the lab so much that he dropped out of high school and
starting working there full time, while also taking a few classes at
Columbia. After earning a high-school equivalency degree, Fraenkel went
to Harvard University to study chemistry and physics, then earned his
PhD in biology from MIT. As in high school, he was drawn to all of the
sciences, and enjoyed pursuing knowledge from all angles, ignoring the
traditional boundaries between fields.
“My early experience was that they were all deeply connected,” Fraenkel says.
As a graduate student, he studied structural biology, which uses
tools such as X-ray crystallography to understand biological molecules.
“What drew me to the field was really the fact that it was very
data-rich in a way that biology, at the time, was not,” Fraenkel says.
However, that was about to change: While Fraenkel was doing a
postdoctoral fellowship in structural biology at Harvard, new techniques
— such as genome sequencing and measurement of RNA levels inside cells —
were generating huge amounts of information. Helping to crunch those
numbers seemed an enticing prospect.
“As I was finishing up my postdoc I was realizing more and more that I
wanted to study biology at a more general level,” Fraenkel says. “I
really wanted to find out whether there was a more systematic way of
trying to understand biology.”
After leaving Harvard, he became a Whitehead Fellow, allowing him to
set up his own lab at the Whitehead Institute and pursue his new
interest in systems biology. From there, he joined MIT’s Department of
Biological Engineering, which had just been formed.
Now, Fraenkel’s lab analyzes vast amounts of data, including not only
genomic data but also measurements of proteins and other molecules
found in cells. For each set of cells, healthy or diseased, he tries to
devise models that could explain what is producing the data. “One way to
think about it is a map of a city where these proteins or genes are
lighting up different things, and you have to figure out what the wiring
is underneath that’s got them talking to each other,” he says.
To do that, his team uses algorithms they have developed themselves
or adapted from network analysis strategies used to analyze the
Internet. In the biological networks that Fraenkel studies, connections
form between nodes representing a protein, gene, or other small
molecule. Nodes that differ between diseased and healthy cells light up
in a different color. Ideally, just a few such nodes would light up, but
this is usually not the case, Fraenkel says. Instead, you end up with a
wiring diagram with color all over the place.
“We lovingly call those things ‘hairballs,’” he says. “You get these
giant hairball diagrams which really haven’t made the problem any easier
— in fact, they’ve made it harder. So our algorithms go into that
hairball and try to figure out which piece of it is most relevant to the
disease, by weighing the probability of different kinds of events being
Those algorithms filter out the irrelevant information, or noise, and
zoom in on the pieces of the network that seem to be the most likely to
be related to the disease in question. Then, the researchers do
experiments in living cells or animals to test the models generated by
Using this approach, Fraenkel has developed model networks for
Huntington’s disease and glioblastoma. Such studies have revealed
interactions that might never have been otherwise identified: For
example, blocking estrogen can help prevent the growth of glioblastoma
“The fundamental thing we’re trying to do is take an unbiased view of
the biology,” Fraenkel says. “We’re going to look everywhere. We’ll let
the data tell us which processes are important and which ones are not.”