I think if you enjoy teaching, and I really do, seeing 100 students write on their end-of-the-year evaluations that they enjoyed it, and they appreciated that it was taught well, that’s a real kick.Karthik Shekhar
By Denise Klarquist
In the fall of 2020, the Department of Chemical and Biomolecular Engineering (CBE) welcomed Karthik Shekhar as an assistant professor. Shekhar currently leads a research group (www.shekharlab.net) in the College of Chemistry and is also a member of QB3, the Helen Wills Neuroscience Institute, Cellular and Computational Biology, and the Biophysics graduate program. Combining his background in chemical engineering, computational biology, and neuroscience, Shekhar’s work is guided by fundamental questions: What more can we understand about the general principles behind neural diversity in the brain? What are the molecular mechanisms that regulate its development? And how did it evolve?
Using experimental techniques of single-cell genomics and computational approaches based in machine learning, Shekhar and his team are generating insights into the developmental and evolutionary origins of cellular diversity, and exploring its biological consequences.
We recently spoke with Karthik to learn about the origins of his interests and his work.
Denise Klarquist — You grew up in Mumbai, India. Tell me about your early background and what inspired your interest in science and technology.
Karthik Shekhar — My father was a chemical engineer, and, in my youth, he was definitely an inspiration although he was not a scientist; he became a successful entrepreneur. During high school, I enjoyed math and physics, but unlike in the U.S., where you have the option of entering in your first year of college and then later choosing to major, in India you had to decide in advance. So, I chose engineering because it was a more common option and it naturally made sense.
I attended the Indian Institute of Technology, Bombay (IIT), where I received my B.Tech. and M.Tech. in 2008. I did an internship at Purdue University during my third year and realized that research was something I enjoyed. I applied to a few Ph.D. programs and ultimately decided on MIT’s chemical engineering program.
DK — How did you transition from chemical engineering in Mumbai and Boston to what you do now, which is at the intersection of cellular biology, chemical engineering and machine learning?
KS — During high school, modern molecular biology was simply not part of the curriculum in India the way it is now. And as an engineer, it was just not part of my thinking.
At MIT, the chemical engineering Ph.D. degree program has a biology requirement. You have to either demonstrate a sufficient number of credits in biology as an undergraduate, which I didn’t have, or you have to supplement that with an MIT course. I ended up taking an undergraduate freshman biology class, which I wasn’t very happy about at the time. But the nice thing about MIT is that all the basic undergraduate classes are taught by some remarkable scientists, giants in their field. And that’s when I became excited and decided to bring these two aspects together; the quantitative thinking of physics math, and biology.
In 2009, I joined Arup K. Chakraborty’s group at MIT, who previously had been at Berkeley. I worked on applying statistical physical models to the area of immunology, particularly focusing on how the adaptive immune system works. Most of my focus was on understanding the evolutionary landscape of HIV, the Human Immunodeficiency Virus. This was when I began using machine learning methods to analyze large-scale biological measurements using technologies that were just being developed.
For my postdoc, I joined the Broad Institute, in the laboratory of Aviv Regev, where she was contributing to the emerging area of single-cell genomics. At the time, most genomic measurements were classically done by averaging many cells together. We began working on ideas to combine various engineering approaches with molecular biology tools to conduct measurements at the single-cell level where you treat each entity as separate. In consequence, the data that you end up measuring is large-scale and highly dimensional, so we needed statistical, inference, and machine learning techniques to make sense of it.
DK — A lot of what you’re doing now has to do with studying vision and the retina. Can you explain why?
KS — Two reasons, really. The nice thing is that the retina has a very well-defined input, which is light, and a very well-defined output, which is the optic nerve. So, it’s almost like a self-contained microcosm of the brain. The other reason is while it’s been studied for more than 100 years, a lot remains that we don’t fully understand.
During my postdoc, we focused on diversity of neurons in the retina in adulthood. Now, we’re studying how this diversity arises during the early years of development. What molecular changes are important and what’s associated with that? We’re also looking across evolutionary timescales. There is this nice dichotomy in that the basic fundamental architecture of the retina has been conserved for about 500 million years, however one level deeper, there is a lot of divergence we don’t fully understand associated with the unique visual needs of different species.
Going beyond the retina, we have a project now in collaboration with Professor Larry Zipursky at UCLA to understand how diverse neurons develop in the visual cortex, and the interplay of genetic predisposition as well as visual experience. This is a new breed of research for me, and it was initiated during the pandemic!
DK — Tell us a little bit about what attracted you to Berkeley and what you enjoy most?
KS — At Berkeley, in particular the Department of Chemistry and Biomolecular Engineering, I felt at home intellectually. I’ve definitely felt like an outsider wherever I’ve been because you’re either an engineer among biologists, or you’re a biologist among engineers. But the assistant professors here are people who have a focus on energy, analytical chemical systems, some who are doing more condensed matter theory and so on. The intellectual diversity is almost an article of faith. Also, the fact that we have a great undergraduate student body, and for a chemical engineering department, we are one of the best in the country.
DK — Having come on board only a month before the pandemic forced the lockdown of campus facilities, your teaching experience at Berkeley has certainly been unique. What have you found most rewarding?
KS — I think if you enjoy teaching, and I really do, seeing 100 students write on their end-of-the-year evaluations that they enjoyed it, and they appreciated that it was taught well, that’s a real kick. The way I see it, that’s the greatest and most sustainable contribution scientists can make in their life because you’re educating individuals who possibly will go on to make breakthroughs that you couldn’t.
DK — Thinking about the future, what’s next?
KS — Specific questions about how cell types evolve and how do you go from molecules to structure in the visual system were not things I was thinking of working on in 2020. I have also initiated a collaboration with my colleague Prof. Kranthi Mandadapu to explore new areas at the interface of neuroscience and physics, which I am really excited about. I hope a year from now there will be something related but completely different which I also could not have predicted. This is one of the joys of science. I’m reminded of a quote from Alice in Wonderland that says, “if you don’t know where you’re going, any road will take you there.”