Computer Scientists Get Wet
In the summer of 2008, Wired magazine ran a cover story titled “The End of Science.” Former Editor‑in‑Chief Chris Anderson argued that the data deluge had rewritten the scientific method: with enough information and the computational power to analyze it, correlation could outrun causation, theories could become optional, and science might learn more from Google than from Galileo. “There’s no reason to cling to our old ways,” he wrote.
Five years later, the picture is more complicated. Traditional sciences—physics, biology, chemistry—still depend on mechanisms, causation, and coherent theories, but computation is now inseparable from discovery. Across disciplines, researchers are blending classical training with data‑driven methods, creating a new class of “pi‑shaped” scientists who straddle both worlds. Their hybrid fluency is reshaping how problems are defined, how experiments are run, and how entire fields advance.
In May, Science Careers profiled some of these researchers who have added computational techniques to their native fields. “I love the fact that once I program and modify a single living cell, I can have millions of similar cells by just growing them up,” says Samuel Perli.
But pi‑shaped scientists can emerge from the opposite direction as well. Increasingly, computer scientists who learn the fundamentals of biology are helping drive the life sciences forward, laying the groundwork for new branches of study. It’s a promising path, though still one that demands sustained effort—rewarding effort, as one pioneer puts it.
Biology for computer scientists
Lawrence Hunter earned his Ph.D. in computer science during the “AI winter,” when enthusiasm and funding for artificial intelligence had ebbed. He pivoted to the fledgling Human Genome Project as a programmer, despite having studied biology only through tenth grade. Weekly genomics seminars at the National Library of Medicine, combined with coding that produced real biological insights—such as identifying genes that distinguish eukaryotes from prokaryotes—pulled him deeper into the field. “I spent a decade learning by osmosis from all the brilliant people around me,” says Hunter, now considered a founder of bioinformatics.
Today, Hunter directs the computational bioscience program at the University of Colorado School of Medicine. He has published seminal papers and written a molecular biology textbook for non‑biologists. Computer scientists, he argues, can cross over at any stage: “Studying CS is like learning to play a musical instrument and can’t be done quickly. Biology can be learned by reading and remembering.”
Rewiring cells
Synthetic biology—designing and testing genetic constructs in living cells—requires wet‑lab skills. But at MIT, Timothy Lu welcomes quantitatively trained students, even if they’ve never pipetted before. Enthusiasm for biology is the only prerequisite. That approach worked for Samuel Perli, a fifth‑year doctoral candidate in Lu’s lab who arrived with no college‑level biology. A talk by the director of MIT’s Synthetic Biology Center inspired him to take “Introduction to Experimental Biology.” He devoured textbooks, papers, and conversations with biologist friends. Now he spends most days at the bench, following in the footsteps of the field’s early pioneers.
At Harvard Medical School, systems biologist Pamela Silver takes a similar stance. Systems biology, she says, seeks to understand how evolution built the integrated machinery of life. Genomes alone aren’t enough; researchers must synthesize information across scales. On her advice, Avi Robinson‑Mosher—who holds a Ph.D. in computer science—took an intensive physiology course at Woods Hole. He now uses simulation to study macromolecular interactions, work that could guide the engineering of new proteins.
Inside black boxes
After more than a decade at Intel, Vaughn Iverson returned to the University of Washington as a graduate student in oceanography. For him, computer science had always been a means to solve scientific problems. In his doctoral work, he and his colleagues bypassed the limitations of culturing microbes—most of which refuse to grow in the lab—by sequencing entire microbial communities and using algorithms to reconstruct the organisms within them. His metagenomics research, published in Science, revealed species previously invisible to biologists.
Iverson argues that computational scientists now play a role akin to instrument designers. Most biologists use sequencing machines and software tools without understanding their inner workings. Pi‑shaped researchers can open these “black boxes,” explain their limits, and build better ones.
Where computation meets science
Joseph Hellerstein, manager of computational discovery for science at Google, says the relationship between computation and domain science shifted with the Human Genome Project. Through its Exacycle initiative, Google has lent its computing power to several academic big‑data projects—five of the six in the life sciences. These efforts promise both societal benefits and deeper scientific insight.
Biochemistry, Hellerstein argues, is foundational knowledge for the 21st century. This fall, he will teach “Biochemistry for Computer Scientists” at the University of Washington’s eScience Institute. “Whether it is medicine, machines through nanotechnology, agriculture, or materials,” he says, “design problems require simultaneous innovation in computing and science that can only be accomplished by those with the combined skills.”
Read the article here. html.