Many people I’ve talked to in the science and investment community equate Digital Biology with High-Throughput Biology. While related, they are not the same thing.
High-throughput is about speed, but digital (in the sense of Moore’s Law type geometric scaling) is about acceleration, or geometrically increasing throughput. The distinction is important for predicting the eventual successes of different technologies, and maybe your career if it is closely tied to a particular technology.
Moore’s Law exemplifies geometric scaling in semiconductors, and generally predicts that the number of transistors on a single chip doubles approximately every 18 months. If you work out the math, it’s about 1000x after 15 years. When that happens, a field is revolutionized once it reaches tipping point.
Indeed, when I started working in Silicon Valley as a freshly minted MIT engineer in the mid 1980’s, I was astonished that my friend’s company had a whopping 3 gigabytes of disks on their central computers (that’s for the ENTIRE mid-size company). Today, those 3GB fit on a USB pen drive, and you can get portable drives with 1 terabytes. In another 15 years, we may have portable 1 petabyte drives, and computers 1000x more powerful. It’s mind-boggling.
In contrast, high-throughput technologies include flow cytometry and robotics. While they are “fast” today, it is doubtful that they will become 1000x faster in 15 years. There are 96-well, 384-well, and 1536-well plates, but it is doubtful these will continue to geometrically scale to having million-well plates anytime soon.
Proteomics mass spectrometry, which often relies on ever faster and more sensitive electronics and sensors, is geometrically scalable for some time, and as such holds the possibility for a revolution. With continued scaling, it is possible to imagine when geometrically more proteins can be characterized from a single organelle.
Such will be the power of the Digital Biology Revolution.