Interview with Dr. David Sivak

Department of Physics (Theoretical/Computational Biophysics)

Tier 2 Canada Research Chair (Nonequilibrium Statistical Biophysics)

Joined SFU in September 2014

Dr. Sivak’s group applies fundamental theory and computational methods to the efficient design of molecular machines. They seek to understand the communication of energy and information within and between driven biomolecular systems. His research program explores how fundamental physics leads to principles that might guide the evolution of efficient, powerful biomolecular motors. This knowledge will inform the design of artificial molecular machines or other nano-scale devices, e.g., for drug delivery or solar energy conversion purposes, and the development of drugs to target malfunctioning biomachines.

What motivated you to go from an undergraduate program in Applied Math, to a B.A. in Philosophy, Politics, and Economics, to a Ph.D. in Biophysics?
I’ve wanted to be a physical scientist for as long as I can remember.  My undergraduate major in applied math amounted to a smattering of math, physics, computer science, chemistry, and molecular biology. I took several social science courses as electives, and I wanted to explore that other side of me a bit more before committing to a life of hard science. I also found study abroad a compelling idea—friends who had studied abroad talked about their crazy experiences in countries from Australia to Tanzania—but I never did it as an undergraduate because I was having so much fun in my program. So after I graduated, I chose to study these other social science disciplines abroad in England in a place full of interesting, smart people from around the world. It was an incredible experience, but after a year I found myself reading information theory textbooks rather than assigned readings for my coursework, so I was pretty sure I wouldn’t continue on that path. Though biophysics and applied math sound quite different, in many ways I returned to my intellectual roots by pursuing a doctoral degree in biophysics, which is basically the application of mathematical, physical, and computational approaches to the study of biological phenomena.

What academic accomplishment are you most proud of?
I sought to physically conceptualize the problem of rapidly pushing some system to a new state (e.g., compressing a gas in a cylinder to a smaller volume) in a way that requires the least energy. When pushed slowly, the physics is straightforward and has been known for 150 years; when you have to go fast (e.g., the piston in a car engine has to fire many times per second), then things get really messy in a hurry.

I distilled a complicated problem down to a simple equation that describes something about how systems respond to being pushed really hard and fast. But then I was left with a mysterious mathematical term with no obvious physical meaning. By digging in the literature I came to the realization that the term is analogous to friction: I mathematically mapped a conceptually different object (resistance to rapid changes in the environment) onto a concept with which everybody is familiar - friction. At the end of the day I had reduced a thorny theoretical problem into a mathematical solution with a physical interpretation that gives a visceral idea about what the abstract equation really means.

Given your versatility you could be in just about any science department, so why Physics?
Physics is truly the best home for me: the questions I find interesting, my methods of investigation, the answers I devise, all very much jive with a physicist’s mindset. And students with training in physics have learned a set of quantitative tools and ways of thinking that prove useful in the study of many diverse subject matters. Having said this, what I do doesn't fit neatly into one area, so I work with people from numerous departments outside my own, such as Statistics, Computer Science, and Molecular Biology. There is a lot of ‘easy’ work to be done if you can speak more than one disciplinary language.  A good portion of my research involves taking frameworks from one discipline, mapping them mathematically onto a different discipline, and – what do you know? – we discover something new in the second discipline. In short, we’re often talking about the same thing in both disciplines but using different languages: surprisingly often it’s ‘just’ a matter of translation to obtain insights into phenomena we didn't have the language for before.

What is the best part of the work you do?
The best part is sitting down with a student or larger group of trainees to work through what data we have recently generated, what we are stuck on, and what the interesting next questions are. That is why I am in science. It's so fun: this is the exciting moment where creative people really work together to synthesize existing knowledge and come up with new ideas.

The applications for your molecular machine design principles are incredibly diverse. Which one affects you most personally or motivates you the most?
I’m driven by the desire to understand what is going on in biology, and the main way I know to get a handle on that is to ask what mathematics and physics can tell us about what is and is not possible. What really motivates me is asking “How can I understand the way that all of these incredibly complex proteins are put together and interact with each other? How do these collections of molecules behave in a way that I can make sense of them?” One thing that drew me to biophysics was looking at structures of proteins. For some proteins you look at their shape and immediately get a sense of what is going on: the protein kinesin has two feet (actually called heads - it’s a long story) and a long linker domain, and sure enough it ‘walks’ along molecular-scale tracks while carrying cargo along for the ride. In contrast, many other proteins are structurally more like blobs – I want to make sense of this seeming sludge that nevertheless does amazingly precise things.

What made you choose SFU?
It came down to my colleagues. I was already working with one professor in the Physics Department and met two others where it was blazingly obvious that we could start collaborating tomorrow. SFU Physics has a large core of faculty studying biology and soft matter – a quarter of the department, a very high proportion for a Physics department. It was also clear that there were people in Molecular Biology and Biochemistry, Math, Stats, and Chemistry whom I could learn from and collaborate with.

Given the range of departments you can work with, what specific strengths or backgrounds fit in with your group?
What is most important is that the work is interesting to the student and that they are not afraid of doing a little programming. Students with backgrounds in Physics, Chemistry, Biophysics, Applied Mathematics, Bioengineering, Chemical Engineering, Computer Science, or Molecular Biology would slot in easily with my research program.  If you're terrified of equations that's probably going to be a problem, but you don’t need a lot of experience in the research area – just an open mind.

What other interest/occupation fascinates you so much that you might choose it were you to start all over again?
I am pretty big on the outdoors and I don’t mind a little solitude. One summer I wrote for a travel guide in Australia covering the Outback, I used to lead week-long backpacking trips, and I still often get people together for long hikes. So in an alternate universe I’d probably be a park ranger, ideally in Alaska or New Zealand. And if I could also bring along an information theory textbook, I'd be pretty happy.

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Dr. Sivak enhances SFU’s expertise in nonequilibrium statistical mechanics and contributes significantly to the Biophysics and Soft Matter Group. His interdisciplinary versatility is an outstanding complement to colleagues in the mathematical, physical, and biological sciences. His research will advance our understanding of the constraints on the operation of efficient nonequilibrium biomolecular machines and define principles for achieving effective communication of energy and information. Moreover, investigating the fundamental links between nonequilibrium physics and information theory will allow direct translation of advances from machine learning and other information-theoretic disciplines to engineering and applied science.

Read more:  Dr. Sivak's personal website, profile on the Physics website, and New Science Faculty page

Interview by Jacqueline Watson with Theresa Kitos