I know. Things have been a bit quiet over here. But that’s to be expected during transitionary phases.That’s right. I finally wrapped things up at the old, unhappy postdoc place and have started the new, shiny postdoc. I will have more to say about that. Of course, this move–in physical, professional, and emotional contexts–has necessitated changes to my routine, which I’m still tweaking. For instance, my commute is longer and now occurs via public transit. I’m on a train!** Yah!
Anyhoo, enough about me–kind of. Down to business. And my business is science, specifically research.
Everyone has a different approach, and approaches vary between career stages (e.g. undergrad, grad student, postdoc…). Obviously the foundation ofany research is the design and execution of experiments using any number of methods. It’s the methods*** portion that I want to focus on today.
Scientists have a plethora of techniques at their disposal. The ones you use, of course, depend on your field, the question you want to answer, the materials and equipment available, the cost and how much money you have… But someone, somewhere, has to have a fundamental understanding of the method.
Most of you after reading that last line are probably thinking, “Well, no shit, Sherlock.” But if you really stop and think about it, you probably know some people, maybe even worked with or for some people who have missed this step along the way. Some people learn how todo a technique without truly understanding what it is they’re doing.
Perhaps you’re thinking, What’s wrong with a standardized protocol? Or a kit? Or a service? Nothing. That is, until there’s a problem, and no one can figure out what the problem is or how to work around it. That’s why someone who is directly involved with the project needs to understandhow stuff works from a technical and conceptual standpoint. They don’t need to necessarily become an expert in that method (unless that isthe point of their training), but they do need a basic idea of what’s involved.
Who’s responsibility is that? Undergrads are still learning core principles, and though they need to learn to think critically and analytically about their work, it might not be reasonable to expect them to grasp the key aspects of every methods. For techs, it depends on their level and independence. If you’re a grad student or postdoc, I expect you to have a damn good idea about what forms the basis of your research. Why you use certain conditions or concentrations? What’s your readout based on, e.g. is it a direct or indirect measure of what you’re interested in, and what end are you looking at? What things could introduce variability? Why are there differences in the numbers you get from different methods? How does the graphing program define an EC50/IC50/half-life, and is it defining it in such a way all your data sets have the same reference point? What’s the difference in the structural information you get from a mass spec versus an NMR? What’s the basis for separation of proteins or compounds using precipitation or column chromatography? What are the limits for loading and detection? These are things you should know because they influence everything you do and place limits on the conclusions you can draw from the data.
But where do/should PIs fall in this hierarchy? They’re likely not in the lab running experiments. It might have been years, decades even, since they touched a pipette. Yet they’re the ones staking their reputations and funding on the data and conclusions drawn using what could be a multitude of techniques. They certainly need to understand the limitations of the technologies, but do they need to know how the instrument is run? The components and time involved for an experiment? My feeling is yes, they do, to an extent. The lab I just joined has been bringing in new techniques and associated equipment over the past several months. My new boss’s view is that he needs to know enough about the method to (1) understand what’s going on, (2) to know what he’s asking of a trainee or tech–in terms of time, effort, and materials–when he suggests an experiment, and (3) to be able to talk through problems with a trainee or tech when things aren’t working. For him that means even though he’s not typically working at the bench, anytime a new technique is brought into the lab, he watches someone run an experimentand might even get his hands a little dirty to get a feel for the technique and instrumentation. I don’t know how it works in practice, at this point, but I like this concept. It gives the PI an idea of how much time is really required to set up, run, and analyze an experiment and just how trivial or not a certain step or method is. The PI also gains an appreciation for the limitations of the instruments and the data generated. It might potentially provide a little more continuity during the inevitable personnel turnover. It’s not just a thingamabob in that room over there; it’s something real.
What do you think?How do you/your PIs operate?How much should a PI know about the methods behind their madness?
** By that, I mean most of this post was written on a train. I may or may not be on a train when you read it.
*** Not this method. I’ll get to it later.
They Might Be Giants – Put It to the Test from They Might Be Giants on Vimeo.
) I think that was the first time that I ever considered writing because I wanted to, not because I had a class assignment. And I did just that. In high school, I started writing stories (or typing them, as the case may be). I fell in love with writing. By nature, I am rather quiet and reserved. I tend to internalize emotions. But for some reason, I found that I could pour an immense amount of emotion into a page. Much like reading, I put much of myself into the story, even if the story itself was far different from my life. Often I wrote them for an audience of one (myself) or with the intention of only sharing them with a few friends. Occasionally I would co-opt my creative writing for a class.
Ever on & on 