Thermal capture and all the other blah

A lot of exciting things have been happening over the last few weeks (which also included a short vacation to the west coast) which kept me busy. Its time to blog about all the awesome stuff thatI came across before my mind tries to erase bits and pieces of them. So here it goes.

This post is about a study that came out this year in June and unlike most other research articles we all read, this is about teaching in the laboratory. First of all, I never heard of this journal (Adv. Physiol. Educ) before and it’s sad that most of us miss out on some really good stuff just by virtue of not knowing that some of these journals even exist. This study was born out of a Physiology course for graduate students taken by Prof. Luke Janssen at McMaster University, Canada.

Here at Penn, a small set of us meet up every Monday and we call it the Nano club and talk about recent papers in the motors field. I was pleasantly surprised when this one was listed in the spreadsheet of new papers that we circulate amongst ourselves. The revelation that molecular motors are stuff with kinetic energy and they move things around efficiently and generate force had a deep impression on me when I first learnt it. What we were taught in undergrad was these smart motors convert chemical energy of ATP hydrolysis to kinetic energy. That’s reasonable, not too hard to break your head over. Professors like to keep things simple in undergrad courses I guess. When I went about reading more about motors I realized that there is another way that scientists like to think about as well which is the Brownian ratchet model. To put it in a simple sentence – the motors can make use of their inherent random fluctuations and vibrations (present in all directions) in a way way that these motions occur only in the required direction and hence doing useful work. This is also called thermal capture. How are these vibrations filtered in one direction and negated in the other? This never made sense to me, or rather it was so hard to grasp this concept.

This study addresses just that! I could connect to the introduction of this paper where the authors talk about how this is not very easy to explain to students. They describe ways and methods to demonstrate this concept in a lab setting with things that we use almost everyday. One of their simplest models used a cell phone in the vibration mode with paper clips. Have the phone vibrate on the table and it vibrates in all directions. Have 2 long paper clips attached to it in a way that each time the phone vibrates, (due to the way the clips are attached) – one of which acts as a ratchet so the vibrations make it to move only in the direction of the paperclip* .

With this model, the students were asked to come up with other such models and some of them came up with very interesting ones which also demonstrated some fundamental things like how small changes in the setup could lead to huge changes like say, two motors very similar to each other but one moves towards the + end and the other towards the – end. They also have a very nice one for how DNA polymerase moves along a track adding nucleotides.

I strongly recommend you take a look at this if you’re even remotely interested in molecular motors and even if you’re not, just to take a peek at the elegance of such a teaching method and style.

*I wrote to Luke asking if I could use his pictures here and though as much as he appreciated my idea of having a post dedicated to their study, his mail made me realize its not the authors but the journal which has the copyright once its published. Its funny how once your study gets published in a journal, the ownership now becomes that of the journal.

Data analysis – the good and the bad

Last 2 weeks in April were crazy for me with finals and term paper submissions and assignments and trying to wind up experiments. As far as research was concerned (my 2^nd rotation in an awesome lab), I was able to stick to the plan I made - did all the experiments that I had to and was left with data analysis in the last few days. Yes, the other side of things. Getting experiments to work is just the first half of the story; trying to make (any) sense of the data you get out of experiments is the other half.

It was only after I plunged into this other half, did I realize how much of data I generated that it felt close to impossible to analyse all of it before my big day – when I’d be presenting in the lab meeting. I was beginning to wonder which is the tougher one that makes you go crazy – the experiments or the data analysis. It really tires you out. Though I got through everything and even got some interesting results, I still don’t have an answer to that question (which I think is a profound one).

But I did learn a couple of things about this whole process of studying data. The most important thing being - never leave this task to the end. Never. The best way to go about it is to start getting a hang of how the data looks as and when you have some of it ready. In my case, each time I did an experiment I took a good number of movies so I could have a good number of data points but once I started analysing the movies, I realized that they were not enough. I needed so many more movies. This happens when you are studying motors in invitro assays (which was what I was studying). There were also some data sets where I felt I’d be better off with a longer movie, maybe slower/faster frame rate and such things. Its always good to know what are the things you need to work on before you do the experiment again at a later point.

Another important aspect – honesty. Since I sort of knew what kind of a trend or pattern I should see in the parameters that I was studying across several different experiments, each time I got to a point where I was able to compare these cases, my mind would try to work its way and make sure I was seeing exactly the expected trend. I found it slightly hard in the beginning to do a completely unbiased analysis. But I got around this by telling myself that what people have seen in the past need not be true at all and maybe I’d see something novel (well, I know this is not true in most cases but you get what I mean, atleast it motivated me to do a blindfold analysis). Its very important to be completely honest with yourself and be able to communicate to others how exactly you went about analysing your data. That way me giving a talk in the lab meeting was good – I realized each person has a different style of looking at his/her data and even representing it. I think that’s one of the things that has to be given a good thought – what is the one best way to represent your data that will definitely make people think and not just listen.

All said, I’m glad I learnt these small things early on. Also, just so you know my talk went very well and the rotation was extremely satisfying :)

PS: About the title - I don't think there's an ugly side to data analysis, is there?