Aaron Streets received a BS in physics and a BA in art at UCLA. He completed his PhD in applied physics at Stanford University with Dr. Stephen Quake. Streets then went to Beijing, China as a Whitaker International Postdoctoral Fellow and a Ford postdoctoral fellow and worked with Dr. Yanyi Huang in the Biodynamic Optical Imaging Center (BIOPIC) at Peking University. Streets joined the faculty at UC Berkeley as an assistant professor in the Department of Bioengineering in 2016 and is currently a core member of the Biophysics Graduate Group and the Center for Computational Biology and he is a Chan Zuckerberg Biohub investigator. Streets has received the NSF Early Career award and was named a Pew Biomedical Scholar.
QB3-Berkeley: What is the biggest challenge and greatest reward of running your own lab?
Aaron Streets: The greatest reward is watching students succeed and become really impressive scientists in their own right. The most recent example of that has been seeing three PhD students graduate this year and go on to secure their dream job after leaving Berkeley.
Of course, along with these student successes are the new discoveries that our lab is making. From a scientific standpoint, the students’ success has come in the form of developing new bioengineering tools and validating that those tools are useful by learning new things about immunology, metabolism, and genome regulation. That’s been really exciting.
The biggest challenge stems from the lab being a technology development lab that builds tools that hopefully are useful to a wide range of biologists and scientists. This means that we interface with a wide range of biological questions and biological systems. So I find myself dipping into many fields that are outside of my training, which is both exciting and challenging.
QB3: When did you become interested in becoming a scientist?
AS: If I were to find an old “what do you want to be when you grow up” essay from elementary school, that essay would say that I wanted to be an inventor—a Benjamin Franklin, or a Leonardo da Vinci. As a kid that struck me as a cool job: to make new, useful machines. But the route between there and here was very circuitous. I studied physics in undergrad, and I was captivated just learning about that field. But while I had always been interested in science, it wasn’t until after graduate school that I knew that I wanted to be a professor, and more importantly, that I thought I might have the chance to become one.
QB3: What got you interested in studying physics as an undergraduate student?
AS: The notion that you could use a mathematical equation to predict what would happen in real life like trajectory, projectile motion, or just regular Newton’s laws—that was always fascinating to me. What attracted me to physics was really just understanding these equations, understanding these laws without having a particular need or application; I was simply seeking knowledge for knowledge’s sake. It wasn’t until graduate school that I realized that you could actually do science with this theoretical framework. I enjoyed learning about it, but I didn’t know anything about doing it. In grad school, I realized you can actually then use those laws and use those equations and design experiments to learn new things that we never knew about before.
QB3: What’s an exciting question or challenge that your field and/or your lab is trying to answer?
AS: We know that an incredible amount of information is stored in our genetic code, in the linear sequence of our genome, which is 3 billion letters long. What is also fascinating is that that three billion-letter long genome is wrapped into an incredibly intricately wound ball of yarn that fits inside the nucleus of every cell. And the way that it’s compacted is a three-dimensional shape that also stores information. People sometimes refer to this three-dimensional organization and the molecules that maintain this structure, as the “epigenetic state” of a cell. This idea that our genome, the same 3 billion letters, can encode an additional layer of information based on how it is folded, to make each cell different, is really a fascinating notion. And the exciting and challenging question that we’re asking in our lab is, how can we measure that epigenetic information? How can we measure the structure of the genome, the organization of the genome, and learn how different cells can manipulate that program to do different things?
QB3: What advice would you give to students who are interested in your field?
AS: One of the things that’s critical about the ever changing, rapidly evolving fields of biophysics and computational biology is that they’re quantitative fields. And they’re data driven. The more we learn about the incredible amount of information in the genome, the more we need computational tools to quantitatively analyze that information. That means that learning math is important for students; learning statistics is important. Additionally, familiarity with computational analysis is important. Also, because it’s such a broad field, and because it’s changing so rapidly, it’s extremely exciting. If you’re interested in the field, you must be willing to jump into a space that you might not have thought was relevant because it might turn out that something surprising or unexpected might be really important to your research. I’ve found that students who do really well in this field are typically open to learning new things and that’s exciting as a mentor and professor.
QB3: Are there any forthcoming papers or current projects that you’d be willing to tell us about?
AS: There are absolutely some exciting projects happening in the lab; I’ll tell you about three. Returning to this idea about how we measure the three-dimensional structure of the genome, and how we understand the epigenetic state of the genome, one of the challenges has been that to sequence the genome, we tend to chop it up and sequence it in 200 letters at a time. All of the technologies that exist for mapping the epigenetic state of the genome rely on these short reads, which makes it very hard to understand what’s happening across certain long links of letters. Our lab has developed a new technology that allows us to use new long-read sequencing technologies that can sequence thousands of letters at a time to map the epigenetic state of cells. The manuscript describing this technology called DiMeLo-Seq (which stands for directed methylation and long read sequencing), was recently published in Nature Methods. That paper was led by Nick Altemose, who graduated last year, and Annie Maslan in my lab and has been a collaboration with Aaron Straight’s lab at Stanford. Another forthcoming paper is a collaboration with the Nir Yosef lab that examines how immune cells develop in our thymus, led by Zoë Steier who graduated last year and is now a postdoc at MIT. The last paper that I’ll mention is a study about how fat cells develop in the human body. That paper is a collaboration with Drs Yu-Hua Tseng and Mary Elizabeth Patti at the Joslin Diabetes Center and was led by Anushka Gupta, who just graduated in the winter semester. All three of those papers were led by graduate students who recently graduated, which is exciting.
QB3: What does being part of QB3-Berkeley mean to you?
AS: QB3 is part of the larger umbrella community in which my lab and our research lives. My home department is bioengineering, and that’s where I get endless professional support. And I am also a core faculty in the Center for Computational Biology and the Biophysics Graduate group. These three programs form the three pillars of the QB3 research community, and beyond these programs, my research intersects with a lot of other communities at Berkeley, including scientists from molecular and cellular biology, EECS and chemical engineering. QB3 encapsulates the breadth of these communities and provides a home for us. Along with the community of researchers at QB3, Stanley Hall is physically an amazing building and research environment. I feel very lucky to have our lab in Stanley Hall where our lab members can interact with other scientists in the building, which creates a really cool space for researchers who are part of the larger QB3 and Berkeley community.