Mimicking cells with transistors
As the world has become less analog and more digital — as tape decks and TV antennas have given way to MP3 players and streaming video — electrical engineers’ habits of thought have changed, too. In the analog world, they used to think mostly in terms of quantities such as voltage, which are continuous, meaning they can take on an infinite range of values. Now, they tend to think more in terms of 0s and 1s, the binary oppositions of digital logic.
Since the completion of the Human Genome Project, two thriving new disciplines — synthetic biology and systems biology — have emerged from the observation that in some ways, the sequences of chemical reactions that lead to protein production in cells are a lot like electronic circuits. In general, researchers in both fields tend to analyze reactions in terms of binary oppositions: If a chemical is present, one thing happens; if the chemical is absent, a different thing happens.
But Rahul Sarpeshkar, an associate professor of electrical engineering in MIT’s Research Laboratory of Electronics (RLE), thinks that’s the wrong approach. “The signals in cells are not ones or zeroes,” Sarpeshkar says. “That’s an overly simplified abstraction that is kind of a first, crude, useful approximation for what cells do. But everybody knows that’s really wrong.”
At the Biomedical Circuits and Systems Conference in San Diego in November, Sarpeshkar, research scientist Lorenzo Turicchia, postdoc Ramiz Daniel and graduate student Sung Sik Woo, all of RLE, will present a paper in which they use analog electronic circuits to model two different types of interactions between proteins and DNA in the cell. The circuits mimic the behaviors of the cell with remarkable accuracy, but perhaps more important, they do it with far fewer transistors than a digital model would require.
The work could point the way toward electronic simulations of biological systems that not only are simpler to build and more accurate, but run much more efficiently. It also suggests a new framework for analyzing and designing the biochemical processes that govern cell behavior.