Why I’ve Switched to Biophysics

Along with moving to a new institution for my Ph.D., I have decided to switch fields, moving from hard condensed matter/ nanoscience to soft condensed matter/biological physics. This decision was totally unplanned - even when applying to and visiting graduate schools, I thought I wanted to do some variant of what I did as an undergrad. The thing is, as I visited more and more schools and learned about different people’s work, I found that what really captured my interest and got me excited was the biological side of things. Things tend to be messier and less well understood, but this just means that there’s more to learn and quantify, and I think physicists are well-positioned to bring something new and useful to the table.

Two months later, I’m spending all my time either reading about, talking to people about, or working on biology/biophysics experiments. It’s a lot of fun. Research-wise, I’m currently interested in the mechanics of cells and aggregates of cells (like biofilms); broadly speaking, I want to learn more about how they interact with each other and with their environment.

While in one sense my interest in soft condensed matter and biological physics has been steadily increasing over the past few years due to some really great classes I took as an undergrad, there are three key experiments or ideas that I’ve come across in the past two or three months that solidified my falling in love with this field.

1. Tensegrity and the structure of biological systems
Tensegrity is a mechanical design principle pioneered by Kenneth Snelson and Buckminster Fuller in the 1960’s, in which structures are designed such that the competition between forces - tension versus compression - throughout has a self-stabilizing effect. (A well-known example of this is the geodesic dome.) Among others, Don Ingber has spent a lot of time exploring the application of this idea to the structure of cells. Basically, the idea is that the cytoskeleton of the cell is composed of a network of interconnected units - the microfilaments, microtubules, and intermediate filaments - under tension and compression; that is, it is structured according to the principle of tensegrity. Many groups have explored this idea since it was first proposed, and other theories exist for understanding cellular structure; indeed, many groups, including the one I’m in, currently spend a lot of time trying to better understand the structure and physical properties of cells. (You can read more about this here.) I just thought the idea was so darn cool when I first came across it in this very nice Scientific American article written over a decade ago (look at the cell on page 54!). What’s more, this idea could be used to understand the structure of other assemblies at the micro- or nano-scale, such as buckyballs or nanotubes (e.g. see the chapter by Yakobson on “Carbon Nanotubes: Supramolecular Machines” in the Dekker Encyclopedia of Nanoscience and Nanotechnology), actuated nanocolumns, and even…

2. Viruses from a materials perspective
Yep - reading about tensegrity led to me to Caspar and Klug’s classical work in the 1960’s, in which they attempted to understand the structure of ’spherical’ viral capsids within a tensegrity-inspired framework. Since then, a number of physicists and engineers have spent a good deal of time trying to understand the structure of viral capsids. One framework in particular, developed by David Nelson and co-workers, really appeals to me: I think it’s an elegant combination of ideas from crystallography and continuum mechanics (what they call “spherical crystallography”). Basically, the idea is that if you try to pack a number of particles - be they beads, or the protein subunits of a viral capsid - on the surface of a sphere, the resulting assembly necessarily possesses crystallographic defects resulting from geometrical frustration. I wrote a small review of viral structure and mechanics focused on this work for a nanomechanics class not too long ago, which you can read here, if you want to explore this further. And this is just the tip of the iceberg - people are doing all sorts of crazy things with viruses: playing tug of war with them, watching them spit out their DNA, poking on them, shocking them, and filling them with various cargoes, among other things. Pretty cool stuff.

3. Hitting worms with laser pulses
Again, this is a very broad field in which a lot of great work has been (and continues to be) done. I don’t know enough about it. What first got me excited about biological neural networks and c. elegans was learning about this experiment by Mehmet Fatih Yanik. Basically, Yanik et al. used femtosecond laser pulses to cut single axons in c. elegans worms, observed the resulting phenotypic effects, and watched them grow back within 24 hours. This is pretty neat - after all, being able to perturb these affords researchers quite a lot of control, and could be used to study nerve regeneration processes one axon at a time, among other things. c. elegans is quite the model system, and I’m sure there’s a lot of other cool work going on trying to understand various processes and mechanisms in these worms. For example, in addition to Yanik’s work, my very little reading of research in this field has exposed me to some very interesting papers from Richard Morimoto’s, Ikue Mori’s, Aravi Samuel’s and William Bialek/William Ryu’s groups, to name a few off the top of my head. I still need to learn more about this field, particularly of what the biologists are doing - but again, this femtosecond laser stuff really got my attention when I first came across it.

Cool Papers 1: General

I’ve come across a number of pretty cool papers in the past few months. Some of them deal with particular phenomena (stay tuned for possible upcoming posts on molecules at surfaces, biomimetics, phononics, crystallization, nanoparticles, wetting phenomena, computational physics, etc. etc. - at some point), and so are probably better off getting their own blog posts. Here are a few papers that didn’t fall into specific categories…

1. Frictional Anisotropy on a Quasicrystal Surface
Along with ~10 other things, a subject that I’ve recently become interested in is nanoscale mechanics, broadly defined. A key experimental tool in this field is the use of local probes to push or pull on things controllably. Miquel Salmeron’s STM group at Berkeley does work on this and related subjects, and I finally got around to reading this paper of theirs from a few years back.

The idea is conceptually very simple: while friction unsurprisingly depends on commensurability (that is, if two surfaces in contact are structurally ‘complementary’, they will ‘lock in’ to each other and hence have high friction between them - an idea that apparently dates back to da Vinci), trying to think about friction using just this notion is unrealistic. For starters, most contacting surfaces are probably incommensurate, and other factors - such as periodicity(?) - contribute, as well.

This paper nicely singles out the role of periodicity by looking at different directions along Al-Ni-Co quasicrystal surfaces using STM (to image the surface and hence distinguish the periodic and aperiodic directions of atom ordering) and AFM (to measure the probe tip-surface friction along these directions) in ultra-high vacuum. The AFM friction data can be modeled using a classical model relevant to the experimental situation (the Derjaguin-Muller-Toporov or DMT model, which I need to learn more about), enabling key parameters to be derived from the measurements.

In particular, the authors find a larger friction force (8x) along the periodic direction than along the aperiodic direction. Unsurprisingly, they ascribe this to differences in energy dissipation via electron or phonon excitation+propagation along the different directions, although it is unclear to what extent each kind of excitation plays a role. Perhaps similar local-probe measurements of a different kind (e.g. ones sensitive to electrical versus mechanical properties) might be useful… At the end of the day, I like this paper because it is an elegant example of using a unique microstructure, in which just one variable (here periodicity) changes in ways that are well understood, to study something interesting as a function of just that variable.

2. Liquid Crystals and the Origins of Life
Noel Clark gave a great talk about this work here at Penn not too long ago. I won’t write too much about this since Randy has a nice description of it over at the condmat journal club.

Here’s the executive summary: according to extensions of Onsager’s rigid-rod model for the formation of liquid crystal phases, individual molecules must be sufficiently anisotropic (i.e. the aspect ratio has to be above a certain minimum) to form a liquid crystal (LC). Surprisingly, the authors of this paper observed LC phases consisting of single-stranded (ss) DNA molecules too short to satisfy this criterion. Optical and x-ray measurements indicate that this results from end-to-end stacking of duplexes of complementary short ss-DNA molecules (known as ‘living polymerization’) into larger rods that satisfy the Onsager criterion, even at low temperatures (in concentrated phases of duplexes separated from the isotropic phase of unpaired ss-DNA molecules).

This autocatalytic behavior is like positive feedback, in a sense, and is why this work is so interesting from a biological point of view: it provides a mechanism by which the right molecules can be ’selected’ out from a ’soup’, and ‘evolve’ into larger ones as part of an RNA world. It’s an interesting idea - definitely one that’s gotten a lot of press, it seems - and while this work doesn’t provide much hard evidence for it, I’ll be interested to see what it stimulates.

3. Suprafroth!
This is a very interesting paper out recently on the arxiv, I think to be published in Nature Physics. While I don’t understand all the details, I like this particularly because it’s a nice combination of ideas from soft- and hard-condensed matter physics, like electronic liquid crystals.

The authors used magneto-optical imaging, which I need to learn more about, to image the flux pattern of superconducting lead (a type-I superconductor). Turns out that the magnetic field on the edge of a disc-shaped sample of lead is larger than the actual applied field, and for large enough magnetic field some flux can penetrate the sample. This leads to a phase intermediate between the normal and superconducting phases, possessing a froth-like magnetic structure - specifically, the froth cell boundaries are superconducting, while the interiors are normal metal. This shows up very clearly in the magneto-optical images (see figures in the paper).

The nice thing is that, unlike ‘conventional’ froths, mass-transport processes like drying or drainage are not present here (as the authors point out, “this superconducting froth involves only electrons”). This means that the froth structure can be tuned reversibly using the applied magnetic field or temperature, and the nice magneto-optical images allow for quantitative analysis of the froth structure as a function of just these parameters.

This is philosophically similar (loosely speaking) to paper #1 - the friction measurements of quasicrystals: again, it is a very nice example of using a unique microstructure (here, a froth structure that doesn’t suffer from irreversible processes, and can be controlled by magnetic field or temperature) to study something interesting (here, the structure and dynamics of froths) as a function of just the variables that you can control.

4. Universality in Conference Registration
This is a cute correspondence recently sent to Nature Physics describing an intriguing social application of statistical mechanics.

The authors used registration data from two physics conferences (# of registrants as a function of time to the deadline), saw that they matched up remarkably well (after rescaling), and came up with a simple model to capture the observed phenomenon in which the ‘pressure’ felt by potential attendees to register varies inversely with respect to the time to the deadline. Also, incorporating a Boltzmann-like factor (instead of uniform probability to register over the period of time) leads to a prediction that agrees well with # of payments as a function of time to the deadline data.

Of course, there are a number of assumptions and fitting parameters floating around here, and I’m not entirely sure this work will change the world of physics, but I always find things like this fun.

Graduate School

It’s graduate admissions season, apparently. So far I’ve heard back from six institutions: four acceptances (including a number of my top choices) with fellowships at two of them, one we’ll-fly-you-out ‘interview’, and one phone interview that went rather well… so at the end of the day, figuring out where I want to end up may be nontrivial.

As I noted in a previous comment, my algorithm for deciding where to apply to was pretty simple: I spent a good deal of time soul-searching and deciding (roughly) what I want to do for the next five-ish years, made a list of all the people I thought would make good research advisors to that end, and applied to the n departments that contained the maximal number of the people on my list (where n was determined by how many applications I was willing to fill out; turned out to be thirteen in total - I’m not superstitious).

My algorithm for deciding where to go for grad school will probably be a variant of the algorithm I used to pick my undergrad institution, and again is not too complicated (Sean Carroll has a very nice post on this subject, btw, as does okham as I just discovered):

1. Make a list of all the factors that I care about: for example, number/quality of advisors who I’m interested in (based on various factors like personal interactions, reputation, publication record, how they place people…), how excited I am by current research efforts, intellectual environment, potential for interesting collaborations, other students, location, quality of the department/school/life, bureaucratic requirements, funding, etc.

2. Weight individual factors accordingly: pretty self-explanatory, although this requires a lot of thought.

3. Visit all the places I’m seriously considering/find out as much about them as possible: this is the data collection stage, so that I have a good idea of how various places shape up in terms of the factors I listed. I’m pretty much traveling every weekend from next Friday to the end of March, with a few days in between for the APS March meeting. That’ll be fun.

4. Assign data values corresponding to each factor for each department: i.e. the results of step #3. These data values will obviously have error bars to reflect the subjectivity inherent to the data collection process, but the inverse relationship between error bar size and time spent on step #3 should enable a single-valued result.

5. Plug and chug: go wherever above algorithm says to go. Hey, it worked pretty well for my undergrad.

Speaking of which, I feel compelled to plug Penn. If anyone reading this happens to be a senior who got into Penn (terminology: I refer to UPenn, not Penn State) for something physics / materials science / nanoscience-related, I strongly urge you to think about coming here for grad school. There’s a lot of very exciting work going on here, and a lot of great people to work with - fantastic intellectual environment (I would say Penn does pretty highly on all the factors I mentioned above).

Wrapping up the summer

Crazily enough, the summer’s over. I’ve been bitten by the research bug, which means that I’m not as enthusiastic about taking classes as I once was, although they all look very interesting and useful. In particular, balancing nocturnal data-taking sessions with going to class/taking tests has never been my forte; and it’s tougher now, what with graduate school applications and pesky standardized tests.

Anyway, the past month’s pretty much consisted of finishing up taking/analyzing data for this paper I’ve been working on, as well as actually writing it. When I started out, I thought my data was somewhat interesting. Thanks to an excellent theorist collaborator, we have a good sense of what’s going, and it’s more interesting than I thought (which is always nice).

I did take a little vacation and went to the Princeton Center for Complex Materials (PCCM) summer school on condensed matter physics (unfortunately they don’t have their talks online yet). This is the second year that I’ve gone; hopefully there will be more. Two talks in particular resonated with me - Philip Kim’s talk on electronic transport in graphene, and Paul Chaikin’s talk on some aspects of colloidal physics. The graphene talk was of particular interest from a technical point of view. While it was very cool, given the nature of my work it dealt with things that I’m very familiar with and/or think about regularly, so I’m not going to describe it here.

On the other hand, Chaikin’s talk was interesting to me from a more conceptual point of view. I’ve encountered some notions of soft matter physics before, but his talk really drove home how exciting some of the things going on in that field are. He started off discussing his recent highly-publicized work on packing hard particles (interesting not only for studying granular materials or phase transitions, but also for designing three-dimensional colloidal photonic crystals) - the point being that random ellipsoid packings (like those formed using M&M’s) can pack denser than ‘conventional’ random jammed packings, even potentially approaching the FCC packing fraction of 0.74, because of their added rotational degrees of freedom. This, in turn, may help understand how glasses form. For me it wasn’t the story but how they fleshed it out (experimentally and using simulations) that was the exciting part.

Unfortunately I had an experiment to finish up and had to miss Chaikin’s second talk, on replication and self-assembly using colloids; his third talk on ‘random organization’ describing some work by David Pine (at NYU) and Jerry Gollub (at Haverford, but also affiliated with Penn) was equally good. He started out discussing reversibility in viscous liquids - that is, the fact that low Reynolds number shear flows are time reversible, a notion I first came across in an excellent article by Brewer and Hahn describing NMR spin echos. There’s a classic demonstration of this phenomenon by G. I. Taylor using a cylindrical Couette cell, although I couldn’t find a nice movie online. Anyway, similar experiments have been performed involving tracking small dyed spheres placed in the liquid while shearing them. Interestingly, while the system remains reversible for low enough strain amplitude, for whatever reason (e.g. collisions/chaos), hydrodynamic irreversibility sets in very quickly as the strain amplitude is increased. Really striking stuff. At the end of the day, colloids really are great systems to work with: they’re easy to make, specific and controllable (e.g. using DNA sticky ends or electric fields), and exhibit all kinds of interesting condensed matter phenomena.

Now that I think of it, the PCCM summer school was so enjoyable partly because it filled in the void left by the lack of regular talks and seminars over the summer. Thankfully, now that the new semester has started, that void has been filled again. Most recently, Jack Harris (from Yale) gave a talk on coupling light and MEMS using radiation pressure, something I don’t know much about. Part of the experimental challenge is in using the right structures; interestingly, a route his group is taking is to use silicon nitride membranes with holes drilled in them. I find this particularly amusing because our group has used (indeed, I made some my first year of research) similar porous membranes to image carbon nanotubes or nanotube-derived structures (like ‘peapods’) using TEM - it never occurred to me that they could be used for such a different purpose.

Anyway, writing this post has been very relaxing; now it’s time to get back to work…

‘Hard’ measurements, ’soft’ materials

So it’s been what, a little less than two months since I last posted? I tend to work on many projects at once - some are ones I’ve been plugging away at for a while, while others are “let’s see what happens” experiments that I work on when I get the time, motivated by some half-brained idea. In particular, I’ve made significant progress on a project of the latter category, and the month-and-a-half has been spent making samples, furiously taking and analyzing data, trying to figure out what it means/delving through the literature, &c. - and of course, effectively disrupting any prospects of sleep or studying for pesky standardized tests. And making headway on my other projects, too. The good news is that I, for one, find the data pretty exciting.

(Oh, and moving to my sweet new apartment, which apparently scores a very respectable 98/100 on the walkability scale. Not too bad, especially given the relatively low rent.)

Anyway, when I haven’t been concentrating on my research, I’ve been reading up on things like organic semiconductors and STM modification of molecules (I suppose what one could call ‘hard’ condensed matter measurements of ’soft’ materials, although admittedly some of my own research falls into this genre). I find people like Paul Chaikin, Heinrich Jaeger and George Gruner particularly fascinating since they seem to be actively doing this kind of research in addition to hard condensed matter physics of the more ‘traditional’ kind (superconductivity/correlated electron systems…). I wonder how many other PIs do this kind of thing?

And of course, two new additions to the reading list: “charge transfer on the nanoscale: current status“, and “electrostatic modification of novel materials” - both hefty reviews of topics relevant to this post.

Also: Heinzel’s book on mesoscopic physics is a new addition to my list of the greatest books of all time - in particular, its clarity is unmatched by many other books I’ve come across on the subject.