Enter Little b, a computational language that can penetrate the "mind" of a cell.

DNA is pretty straight forward. With its simple composition and linear structure, it easily lends itself to mathematical models. Not so with proteins. In fact, proteins are an order of magnitude more complex than DNA. It is proteins, not DNA, that carry out the cell's heavy lifting. However, with their intricately folded three-dimensional shapes determining a seemingly endless range of possible functions and their manifold interactions with other proteins and with DNA, the leg-work required to mathematically capture the protein universe seems absurd.

That is why a team of Harvard Medical School researchers have decided to attack this issue from an entirely new angle. Rather than build a mountain range of proteomic data one grain of dirt at a time, they have developed a computer program that can take on the responsibility of assembling such a gargantuan model.

"Through incorporating principles of engineering, we've developed a language that can describe biology in the same way a biologist would," says Jeremy Gunawardena, director of the Virtual Cell Program in Harvard Medical School's department of systems biology. "The potential here is enormous. This opens the door to actually performing discovery science, to look at things like drug interactions, right on the computer."

These findings will be published in the July 23 issue of Journal of the Royal Society Interface.

"Programming with models: modularity and abstraction provide powerful capabilities for systems biology"

Aneil Mallavarapu, Matthew Thomson, Benjamin Ullian, and Jeremy Gunawardena

Department of Systems Biology, Harvard Medical School, Boston MA

Mallavarapu used the programming language LISP, a language widely used in artificial intelligence research. LISP is famous among computer scientists due to its ability to write code that, in turn, can write code, enabling a programmer to derive new mini-languages.

"LISP isn't like typical programs, it's more like a conversation," says Gunawardena. "When we input data into Little b, Little b responds to it and reasons over the data."

For example, Gunawardena's lab works on kinases, a kind of protein that transfers phosphate chemicals to other proteins in order to regulate their activity. While this property is common to all kinases, there is a great deal of variety in how particular kinases carry this out. Little b, however, understands this basic property of kinases, this abstraction.

Here, the researchers demonstrated how they were able to interact with Little b to build complex models of kinase activity, using Little b as a kind of scientific collaborator, and not simply a passive tool.

"This language is stepping into an unknown universe, when your computer starts building things for you," says Gunawardena. "Your whole relationship with the computer becomes a different one. You've ceded some control to the machine. The machine is drawing inferences on your behalf and constructing things for you."

"The next step is to create an interface that's easy to use," says Gunarwardena. "Think of web page development. Lots of people are creating web pages with little or no knowledge of HTML. They use simple interfaces like Dreamweaver. Once we've developed the equivalent, scientists will be able to use our system without having to learn Little b."

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