The inner workings of the brain
Just like sequencing before it, gene editing technology is getting faster, easier, and more accessible. It’s opening up some very exciting research opportunities.
What are some of the hottest topics in genomics right now? Gene editing, and synthetic biology, are probably pretty high on that list. Where are the best places to work as a genomicist? The Broad Institute and the New York Genome Center are probably pretty high on that list. With all of that on his CV, that makes Neville Sanjana one of the most exciting researchers out there. The phrase ‘cutting-edge’ gets thrown around a lot, but Neville’s work definitely deserves that label.
FLG: You went to Stanford University for your undergraduate degree in, Symbolic Systems and English Literature. It’s been described to me as an interdisciplinary program concentration on ‘The Science Of The Mind’. As intellectually attractive as it sounds, it seems like it’s the kind of program you need to seek out quite specifically. The inner workings of the brain are still something you work on today. Where did that curiosity first come from?
NS: Growing up in San Diego, I had a school friend whose father was a cognitive scientist at the University of California, San Diego. In fact, his father was the founding chair of the first cognitive science department in the United States. So through being in that house, and hearing what kinds of problems a cognitive scientist thinks about, it stayed with me and gave me that early exposure.
Of course, many schools have a cognitive science major, but at Stanford they really created their own version of it with the Symbolic Systems Program. It included a lot of traditional cognitive science like cognitive psychology and neuroscience but also had this whole different side to it with logic, linguistics, and computational linguistics. It was very computationally focused and the underlying idea was that the mind is a computer. One of the major questions was how do we design experiments to understand the computations that the mind performs
FLG: Where did the English Literature side of things fit in?
NS: I think in science it always helps to write. One day I stumbled into a class on Charles Dickens, and really just enjoyed the enthusiasm of the professor, Christine Alfano, who was an expert about Victorian era literature. It was a wonderful department with master writer-scholars like short story author Tobias Wolff and James Joyce scholar Brett Bourbon. An amazing faculty — in fact, I think they were ranked as the best PhD program in English Literature in the United States during that time. So, it’s something I just stumbled into through traditional English literature, and it developed into a serious interest. It was fun for me but also very useful as science involves so much writing and critical reasoning.
FLG: Your next stop was your PhD in Cellular and Computational Neuroscience. How did that come about?
NS: I was very lucky that the Symbolic Systems lab in which I did my undergraduate thesis — Josh Tenenbaum’s lab — also moved from Stanford to MIT that same year. It is no exaggeration to say that, in large part, I came to MIT by following Josh, who was working at the cutting edge of reverse-engineering the algorithms that the human mind uses to learn. With Josh, I was working on probabilistic models of how humans make decisions, and how human learn new concepts.
Once at MIT, I joined Sebastian Seung’s lab, a non-traditional yet very innovative neuroscience group in the department of Brain and Cognitive Science. It was a perfect place for somebody like me who wanted to hedge their bets between studying cognitive science and neuroscience. Over time, I veered more into the neuroscience side of things and got really into molecular and cellular neuroscience.
Through being in his lab I became interested in developmental neuroscience, which actually carries forward to what I do in my own lab today actually. For my PhD thesis, I built a time-lapse microscope to image fluorescently-labeled axons from rodent brains over very long time period. I was able to do is track the trajectory of these growing axons as they go out and make synapses and find other neurons to connect to. And a lot of the work that I’d done in probabilistic modelling really came into play here because we ended up using the same kind of models to understand the trajectories of these nascent axons wiring up the brain.
Find out more about Neville’s gene-editing research at the New York Genome Center on page 53 of the latest issue of Front Line Genomics magazine.