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This is from John Russell's column in Bio-IT World last week.



Two questions: Jeremy, do these high profile systems biologists know about
SBML and the array of solvers that support it? Everyone else, why LISP?



Harvard Tackles Systems Biology

Towards the end of this summer, Jeremy Gunawardena and Aneil Mallavarapu of
the Virtual Cell Program at Harvard Medical School's Department of Systems
Biology expect to unleash b -- pronounced "little b" -- a new open-source
computer language they hope will energize the biological modeling community
as nothing has before.



Today, a handful of companies and academic groups are building computational
models of disease and biological pathways. To a considerable extent, these
scattered efforts remain in their infancy (although modeling has recently
gained traction inside a few biopharmas). Moreover, the companies charge for
their services and tools and have a vested interest in controlling their
proprietary technology. On balance, there has been little synergy between
these isolated pockets of progress.



Imagine instead a biologist-friendly language designed specifically to
encapsulate biological knowledge and constantly pounded on and improved upon
by an open-source community. Add to this vision a growing library of models
(and model fragments) written in b and contributed by researchers worldwide
for free use by other researchers. Based on list processing (LISP), b is
designed to be modular, emphasizing the reusability of modules and a
Lego-like approach to model building.



Currently, b focuses on biochemical modeling using ordinary differential
equations. However, Mallavarapu says, "We want to expand to describe other
types of models like partial differential equations, and we're working on
what it will take to write stochastic models."

It's not a great leap to conceive of a GenBank for computational models that
researchers could contribute to and draw from. Imagine the interesting and
productive in silico research that an army of modelers might undertake.



This is a grand vision for a language with such a diminutive name, and it is
one of the first concrete projects emerging from Harvard's ambitious foray
into systems biology. The department is a little more than a year old, has
enrolled nine students in its Ph.D. program, and has assembled an impressive
faculty led by chair Marc Kirschner, deputy chair Timothy Mitchison, and
Lewis Cantley.



They were "the gang of three who believed sufficiently that something new
was happening to come together and make this [department] happen," says
Gunawardena, who is the director of the Virtual Cell Program.



Language Barriers

A self-professed "mathematician who fell from grace," Gunawardena had stints
in academia and industry, including a long stay at Hewlett-Packard doing
industrial research. While at HP, he caught the biology bug and moved to
Harvard's Bauer Center for Genomic Research. That was his first opportunity,
he says, "to actually live among biologists and understand what was going
on. Pretty much everything I thought before I came [to Bauer] turned out to
be...modified substantially." When ideas for the new systems biology
department bubbled up, he was invited to join.



Mallavarapu is a cell biologist and biochemist by training. He took his
Ph.D. with Mitchison at the University of California, San Francisco, working
on photomarking technologies to visualize cytoskeletal dynamics. During the
past few years, Mallavarapu worked at Millennium Pharmaceuticals developing
technology, writing software, and thinking about what is now b. He credits
conversations with Gunawardena for stoking his desire to make the language a
reality, and he recently joined the Virtual Cell Program as a research
scientist. These guys have great jobs!



Certainly, tricky issues remain. Defining b so that it can be easily
compiled on various LISP platforms isn't trivial. Attracting an early
adopter community will be important. Writing an easy-to-use graphical user
interface is another priority, as is getting models and modeling techniques
published in peer-reviewed literature.



"I think [it's] the classical catch-22 problem: Until there's a community of
people speaking [a language], why would you want to learn it?" Gunawardena
says. "I'm very keen on little b in the context of some courses that we'll
be teaching [and] think that's going to be a very influential early adopter
community. I think it's likely that some of our colleagues at the medical
school and also at MIT are very interested."



It will be interesting to watch how big little b becomes.