September 15, 2005
Computer Modeling Reveals Hidden Conversations Within
By Sherry Seethaler
have developed a computer program that helps explain a long-standing
mystery: how the same proteins can play different roles in a
wide range of cellular processes, including those leading to
immune responses and cancer.
use the timing of signals to communicate, similar to the
way a telephone wire carries information about conversations.
Credit: Alexander Hoffmann, UCSD
Prior to the UCSD team’s
findings, which are published in the September 16 issue of the
journal Science, many scientists expressed doubts that
a computational approach could represent the intricate mechanisms
through which cells respond to outside signals. However, the
researchers report that their computer model accurately predicts
particular behaviors of living cells. They also believe that
the model has important practical applications, including guiding
the design of better treatments for cancer and other diseases
that involve failures in cell communication
approach revealed how the same set of proteins produce physiologically
different outputs in response to only subtly different inputs,”
explained Alexander Hoffmann, an assistant professor of chemistry
and biochemistry, who led the team. “This is the first
step toward developing drugs that interfere with one of the
pathological functions of the proteins, but leave the healthy
functions intact. For example, many current cancer drugs dramatically
reduce immune function. Computer modeling should make it possible
to design anti-cancer drugs that do not weaken patients’
The computer model
comprises 70 equations to account for the behavior of five proteins
and three RNA molecules in the “NF-kappaB signaling pathway,”
which regulates genes involved in cancer, inflammation, immune
function and cell death. Each equation takes into account a
different parameter, such as how quickly a protein is synthesized,
or how quickly it is degraded.
The researchers chose
the NF-kappaB proteins because there is a wide body of prior
research that they were able to draw on to set the initial parameters
in the model. As they were developing the model, they repeatedly
tested and refined it by comparing the model’s predictions
with the results of experiments with living cells.
“The beauty of
this kind of interdisciplinary work is the almost circular way
the model’s predictions drive the design of new experiments,
and the how results of those experiments can be fed back into
the model to improve it,” said Shannon Werner, a graduate
student in chemistry and biochemistry, who did the experimental
work described in the paper.
Once the model consistently
predicted the behavior of living cells in a variety of experimental
conditions, the researchers used the model to infer what was
going on inside cells in much greater detail than would be possible
through laboratory experiments alone.
The model revealed
why two natural chemicals have opposite physiological effects.
When exposed to one of the chemicals, the proteins create positive
feedback that lengthens the amount of time they are active.
When exposed to the other chemical, they initiate negative feedback,
which shuts them down rapidly.
view has been that proteins are either on or off like a light
switch, but that didn’t explain how activating the same
proteins with different chemicals could have opposing effects
on cells,” explained Hoffmann. “Our model shows
that, analogous to how a telephone transmits an infinite number
of different signals along a single wire, it is the timing of
the proteins’ activity that allows them to exert intricate
control over the behavior of a cell. The computer model reveals
the hidden conversations in the cell’s wiring.”
The researchers attribute
their success in developing the computer model, despite criticism
that the computational approach would require too many simplifications
to accurately model cell communication, to the diverse expertise
they brought together.
computer model is both science and art,” said Derren Barken,
a graduate student in bioinformatics and experienced software
engineer, who programmed the model. “It requires intuition
built up over time, but it also requires someone like Alex,
who can critically evaluate the scientific literature to decide
what parameters need to be included in the model, and someone
like Shannon who can take the predictions of the model and design
experiments to test them in the laboratory.”
The study was supported
by the National Institutes of Health, the National Science Foundation
and the UC Academic Senate.
Sherry Seethaler (858)
Comment: Alexander Hoffmann