Talks Part 1: Modeling Cells

I always enjoy attending good talks, and we’ve had quite a few lately: they’ve either been relevant to my current research, interesting applications of concepts I’ve encountered before, or just plain cool. (And of course, going to talks - or blogging, for that matter - is always a good way of taking a break from working all the time i.e. staying sane.) The material presented is always a good springboard for learning more. Here are a few summaries and references for the talks that stuck out the most, mainly because they dealt with things that I hadn’t directly encountered before.

Modelling Cell Motion and Morphogenesis: Mark Alber (Notre Dame)
Although this was billed as an applied math talk, it felt more like a physics talk: the speaker focused on trying to convey what was actually going on and less on the mathematical details involved in modeling it, which was refreshing. I’ve been to too many math talks where the speaker gets hung up on mathematical details, and the big picture somehow gets left out. Anyway, Prof. Alber is an applied mathematician at Notre Dame who spends a good deal of time modeling cells and biological processes at various scales (hence the term multiscale modeling) using ideas from statistical mechanics. The point is this: different modeling methods have their advantages and disadvantages. For example, macroscopic continuum methods abstract away many (often crucial) things and can miss different kinds of phenomena, while microscopic cell-level models - in which stochasticity is very important - can be computationally very intensive. This is particularly important in biological systems, where important processes take place at pretty much all scales; ideally, one would be able to construct a 3-dimensional model of a system and be able to zoom in and out with ease. This is what Alber et al. have been working on, in two different ways.

The first method is a 3D stochastic model of myxobacteria dynamics based on a lattice-gas cellular automata model, and using this, they’re able to study experimentally-observed phenomena like rippling, the formation of things like ’streams’ and ‘traffic jams’, and cell swarming/aggregation. The second method treats the cells as extended objects and goes off the philosophy that “while individual organisms and organs have very different structures and behaviors, many of the underlying interactions and components are the same.” In particular, it is an implementation of the Cellular Potts Model (CPM) of statistical mechanics, a non-equilibrium variant of the Ising model, coupling this to a continuum reaction-diffusion model for morphogen production/diffusion and a set of conditions dictating how genes are regulated. In particular, each cell is represented as a cluster of pixels in the CPM (with a multidimensional index indicating the type of cell) and interacts with other cells via a pairwise adhesion, and the cool thing is that they can use this to model - to a certain extent - limb formation in things like growing chicken embryos.

Further reading…
- Modeling myxobacteria dynamics: O. Sozinova et al., “A Three-Dimensional Model of Myxobacterial Aggregation by Contact-mediated Interactions“, PNAS 102 11308 (2005) and D. Kaiser, “Coupling Cell Movement to Multicellular Development in Myxobacteria“, Nature Reviews Microbiol. 1 45 (2003).
- Modeling limb formation: R. Chaturvedi et al., “On Multiscale Approaches to 3-Dimensional Modeling of Morphogenesis“, J. R. Soc. Interface 2 237 (2005).
- Somewhat related: D. A. Beysens et al., “Cell Sorting is Analogous to Phase Ordering in Fluids“, PNAS 97 9467 (2000) and W. Zeng et al., “Non-Turing Stripes and Spots: a Novel Mechanism for Biological Cell Clustering“, Physica A 341 482 (2004). In particular, I find the similarities between figure 2 of the latter reference and this picture of stripe formation in ferrofluids in one of my previous posts on electronic liquid crystals are striking.

Random 3am Thoughts

I typed this up pretty late last night, but I guess I never hit ‘post’. So…

• I just finished a 20-page lab report on using the Mössbauer effect to study the isomer shift and hyperfine splitting of ⁵⁷Fe, and I must say, it’s an incredible experiment. You move a thin foil of isotopically-enriched absorber (e.g. stainless steel/Fe metal) at extremely, extremely non-relativistic speeds of several millimeters per second relative to a ⁵⁷Co source. You record the number of 14.4keV gamma-rays detected. You analyze the heck out of your data. And somewhere along the way, somehow, you figure out how to deduce the magnitude of these splittings (sometimes as small as ~10^-9eV), among other things, from this mess of data. It works like magic, and apparently other people think so too (with applications from testing GR to looking at car exhaust/hemoglobin to the Mars Rover).

• Speaking of physics lab, I’ve just realized something: although classes, talks, homework, et cetera are fun (for very broad definitions of fun in some cases), I find that I’m happiest either when I’m actually doing an experiment, or trying to dissect my data. There’s this thrill involved with trying to learn something new about a system by playing with it, probing it, trying to force it to reveal its secrets in a systematic and careful manner; and either actually learning something new about it, or more commonly finding out why your approach is flawed. But debugging an experiment or trying to find a nugget of signal in a sea of noise can be fun, too. It’s like being a detective (cliché, I know, but it’s true). I really love experimental science, and I’m lucky because all of the experiments I get to do for this modern physics lab class are really beautiful. I’ve learned a ton from this class - and not just physics, too, but things like statistics, or more methodological things like really thinking things through and being careful and systematic. Which is perhaps why this post (via Chad Orzel) pissed me off, although I have better things to do than rant about it.

• And speaking of things of a curricular nature, I really, really hate in-class midterms for upper-level classes (the ones that you actually have to think deeply about). It just doesn’t make sense to compress the thought process involved in solving problem sets (an intense process of deep thought, trial, error, et cetera spread out over a week) into an hour-long block, and I find that when faced with such a situation, I’m so scared of screwing up that, well, I screw up. If I were in charge, I’d give really, really hard take-home midterms, or something of that sort, I think.

• I recently ordered a poster-size version of the ‘map of science’ (from here for $10), featured in Nature several months ago. I’ve always had the desire to map out the sciences, particularly the ones I’m interested in. It always struck me as a kid how throughout the history of science, hot new fields always seemed to emerge by drawing connections between fields that otherwise hadn’t been connected, and if you had a map of it all, identifying places to draw new connections would be a breeze. I’m not sure if I think quite so simplistically anymore, but I still agree with the general philosophy to a certain extent. That being said, the people who constructed this ‘map’ of science did a lot of work, and it shows: the only way to actually read the thing is by squinting.