Questions and answers with Philip Nelson

University of Pennsylvania professor Philip Nelson earned his PhD from Harvard University in 1984 under the direction of high-energy theorist Sidney Coleman. In his research career, Nelson has worked on geometrical and topological methods in quantum field theory and superstring theory, soft condensed-matter theory, mechanics of biomolecules, molecular motors, instrumentation in neuroscience, and retinal coding.

Nelson is the author of Physical Models of Living Systems (W. H. Freeman, 2015). In this month’s issue of Physics Today, reviewer Eva-Maria Collins describes the book as “an entertaining and engaging textbook that hits a perfect balance between biological experiments, physical models, and computational approaches.” Nelson is also author of Biological Physics: Energy, Information, Life (W. H. Freeman, 2003) and coauthor with Jesse Kinder of A Student’s Guide to Python for Physical Modeling (Princeton University Press, 2015) and with Tom Dodson of A Student’s Guide to MATLAB for Physical Modeling (self-published, 2015).

Physics Today recently caught up with Nelson to discuss Physical Models of Living Systems, among other things.

Photo Credit: Kavli Institute for Theoretical Physics

PT: What prompted you to write Physical Models of Living Systems?

NELSON: Like any instructor, I wanted to ignite students’ curiosity. I wanted a particular balance between classic foundational material and current work. For example, I wanted to engage students with news about the ongoing revolutions in superresolution imaging and synthetic biology, but also give them tools to understand what’s under the hood, how their inventors arrived at them, and what aspects physics can illuminate.

I wanted to use some concrete case studies to show life-science students the value of a physics viewpoint and also to show physical-science students the rich world of important phenomena out there waiting for them. I also wanted to reach engineering students, who make up nearly half my class, in a language that they speak.

I wanted to present imperfect, real-world experimental data—not prettied up—to help students see what kind of information can be extracted from them. I wanted to support instructors creating courses in which computer programming is an integral and necessary part of learning some biology and physics, not an abstract end in itself. Similarly, I wanted probability theory to be a thread through much of the discussion.

PT: What kind of feedback have you received?

NELSON: A handful of brave instructors used the book prior to publication, and the feedback I got from them and their students was invaluable. Students in my own class were instrumental in helping me find obscure points. Even more important, they gave me the constant impetus to get the job done—each chapter had to get written or revised on time for each week’s classes.

Most students finish first-year physics without much appreciation for the fact that every theory’s prediction about an experiment is actually a probability distribution. A student who knows a little bit about probability can use it to select (or reject) theories. Both physical and life-science students have responded enthusiastically to whatever demystification I’ve managed to give them on that art.

One big initial feedback from biologists—generous support from NSF’s biological sciences division—was crucial for getting the book done in a timely way.

University of Pennsylvania biophysicist Philip Nelson and middle-school students at the Philadelphia School conduct an electrolysis of water experiment.

Also, many, many friends and strangers gave me expert criticism (ouch!), data, and images.

PT: What features or content would a second edition contain that you wished you had put in the first edition?

NELSON: I’ll listen to readers more than to my own impulses on that.

PT: Do you foresee computational approaches becoming a staple of biology and biophysics courses?

NELSON: Well, we want to prepare our students for research, and practically no research gets done today without computers. So I think it’s important to get them using a general-purpose programming language to do little things from scratch as early as possible. Algorithmic thinking is an entirely new idea for many students, and it’s powerful. Also, the sorts of homework problems one can do with a few lines of code are often far more interesting and relevant to research than the strictly analytic ones I grew up on, although those are still important.

Luckily, we live in an era when every student has enormous computing horsepower in her own backpack. Open-source programming environments are now freely available, and some of them are so approachable that an instructor can spend just a week at the start of a term getting students going, and then add specific skills as they go along. Jesse Kinder and I wanted to foster an informal attitude to programming that gets students over their initial fear and gives enough early useful results to show them that it’s worthwhile going deeper.

PT: How do you balance your research with your commitment to crafting instructional monographs and engaging in K–12 outreach activities?

NELSON: The world has a lot of big problems. Some will have scientific or technical solutions. If I can help the next generation to teach themselves any of the skills and frameworks that they will need to find even one new solution, that’s got to be my priority for now.

PT: What books are you currently reading?

NELSON: Nick Lane, The Vital Question (W. W. Norton, 2015). Primo Levi, Moments of Reprieve (Summit Books, 1986).