|
Almost
exactly one year ago, David de Graaf became Pfizer’s
first director of systems biology — whatever that
means. Even David wasn’t precisely sure. Ostensibly,
it was to help Pfizer better understand system
biology’s potential and to act as a conduit for the
technology to researchers. He told me half-jokingly,
if I’m still here in a year, that will be one
measure of systems biology’s traction in Pfizer.
David
is still there. In fact, since then, Pfizer has talked
a little about one program that used systems biology
methods to suggest a method of action for drug-induced
vascular injury and may lead to definitive biomarkers.
Last month, following a panel discussion, Bruce Gomes
who works with David told me their biggest problem now
was meeting increasing demand inside Pfizer. He said
results of another program — he declined to specify
details — had excited Pfizer researchers, and the
spiked interest was stretching their ability to
respond.
Pfizer
is not alone. Last March, Novartis elevated its
modeling effort to departmental status. The FDA, under
Bob Powell, is slowly building a bank of models based
on legacy data (talk about an underused gold mine) to
help sponsors use simulation to design better Phase
III trials. This year Dana-Farber opened a Center for
Cancer Systems Biology. This summer, the American
Diabetes Association partnered with Entelos to provide
academic researchers with free access to a diabetes
simulation platform. For years the Institute for
Systems Biology has been pumping out experimental and
informatics tools to deal with the omic data deluge.
The list goes on.
Are
we nearing an inflection point for systems biology?
Hopefully. But abrupt change in the pharmaceutical
industry is still a years-long process. What seems
clear is that among early adopters like Pfizer,
systems biology concepts and tools are becoming
entrenched. They believe systems biology will speed
efforts to understand biology, identify biomarkers,
deliver drugs, and even help clinicians choose optimal
therapies for patients based on the patient’s
particular attributes.
In
this issue, columnist Nat Goodman, a researcher at the
Institute for Systems Biology, invites readers on a
journey of several columns to explore systems biology.
Among other things, he will review real tools that
researchers can use, an exercise that is now badly
needed.
In
his opening column, Goodman suggests too many people
still mistake the “systems” in systems biology to
mean it’s mostly an in silico and theoretical
endeavor. That’s wrong, he says. I agree. “Systems
biology is squarely an experimental field that eats,
drinks, and breathes data. To do systems biology you
need an experimental system that is amenable to
large-scale experimentation,” he argues. That’s
certainly true today and will be for years. Not enough
biology is known.
‘All
Knowable Knowledge’
The
promise of systems biology is large, and starting to
be realized. I’m a believer. But I really like an
observation by Ajay Royyuru during a conversation we
had recently. He’s the senior manager, computational
biology center, at IBM Research’s Watson lab. A
molecular biologist by training, he did a postdoc in
structural biology at Memorial
Sloan-Kettering Cancer Center, and joined IBM in 1998
after a brief stint at Accelrys. Blue Gene, IBM’s
supercomputer, is one of his toys.
“Systems
biology efforts in general are trying to define all
knowable knowledge of biological systems through a
variety of platform technologies. So one often wonders
what amount of work will it take to describe a complex
biological system, to dissect and know the entirety of
a complex biological system, and when do we know that
it has been accomplished well,” said Royyuru.
“From
complexity analysis it seems obvious that you can
claim that you’ve understood it to some extent only
when you are able to describe the behavior using tools
and techniques that are far less cumbersome than just
reproducing the system itself. If my in silico
model ends up being as complex and does not provide
any more detail and insight than the functioning
biological system in the Petri dish, then have I just
mimicked the complexity or have I understood the
complexity? You know what I mean?”
I do.
We still have a long and exciting way to go.
John
Russell, Bio•IT World’s executive editor,
writes a monthly systems biology newsletter (bio-itworld.com/archive/silicobio).
Email
this page to a friend
|
