It was so satisfying to be able to turn messy, qualitative biology into precise, quantitative numbers. Once I got into playing with image analysis, though, I was hooked. I spent more months and months copying and pasting code, figuring that out as I went. It also created a new challenge: I now had a huge pile of images to analyze. It took about a month to figure out how to program the microscope, but that saved me two months of time manually collecting images in a really boring fashion. I had no formal training in computer science. I decided that it would be great if I could figure out how to automate the microscope. It would have taken months to do manually. This required capturing thousands of microscopy images. During my cell biology doctorate work at the University of Illinois, Urbana-Champaign in the early 2000s, I was studying how chromatin, the complex of DNA and proteins in eukaryotic cells, responds to signals passed through the estrogen receptor. The transition was born out of necessity. The interview has been condensed and edited for clarity.Ĭomputer scientists have applied their skills in biology, but you took the less common path from biology into software engineering. “That’s why I ended up staying in computer science.”Ī Massachusetts Academy of Sciences fellow, Carpenter has received a National Institutes of Health MIRA award, as well as a CAREER award from the National Science Foundation and a 2020 Women in Cell Biology Mid-Career Award from the American Society for Cell Biology, among other honors.Ĭarpenter spoke with Quanta Magazine about the joy of translating messy biology into computationally solvable problems, an ambitious effort to screen drugs for 200 diseases in a single well, and how researchers who are humble, curious and able to communicate with people outside their discipline can create a culture that improves the diversity of computational biology and machine learning. “By the time I got toward the end of my postdoc, I found that I would much rather help other people accomplish their cool biology by making the tools than pursue my own particular biological questions,” she said. It started out as a side project during her training as a cell biologist - what Carpenter calls “a little scrap of code to do a thing” that she needed, which over time grew into a toolbox that other researchers found useful, too. It has been cited in more than 12,000 publications since its release in 2005. She developed CellProfiler, a widely used open-source software for measuring phenotypes (sets of observable traits) from cell images. This kind of image-based profiling is speeding up drug discovery by improving screening for compounds that desirably modify cells’ characteristics.Īnne Carpenter, a computational biologist and senior director of the Imaging Platform of the Broad Institute of the Massachusetts Institute of Technology and Harvard University, is a pioneer of this approach to research. By measuring thousands of visualizable cellular properties - the distribution of a tagged protein, the shape of the nucleus, the number of mitochondria - computers can mine images of cells for patterns that identify their cell type and disease-associated traits. Using machine learning methods similar to those that enable computers to recognize faces, biologists can characterize individual cells in stacks of microscopy images. For cells, however, that’s surprisingly less true. You can’t judge a book by its cover, or so we’re taught about people.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |