Last week, I walked into the office of a fellow graduate student to ask for help on a new experiment I’d been struggling with. I began: “I can’t get this staining protocol to work. Can you help me figure out what I might be doing wrong?”
It was a complicated protocol, and I expected him to start asking questions about my sample or technique. Instead, he answered without hesitation, “You’re probably not removing enough media after the first wash.” I was flabbergasted. The fact that he could go straight from, “this doesn’t work” to a perfect solution without any more detail amazed me. But he explained that he’s taught this protocol to several people in the past, and that’s the step everyone has the most trouble doing right.
Of course my lab mate was right; I fixed that first wash, and my stain worked perfectly.
The process of earning a Ph.D. is called graduate “school,” but it is really an apprenticeship without a well-defined curriculum. Every graduate student’s training is different because the only way to learn is by doing, and we all do slightly different things. This can be both a blessing and a curse — we become world experts at the few experiments underpinning our thesis projects, but we can graduate as complete novices when it comes to basic lab techniques that we practice more infrequently.
In light of this reality, the best way I have found to augment my lopsided skill set is to ask other scientists about the experiments they struggle with and the solutions they learn in the process of troubleshooting.
Graduate students learn by doing, and realistically, we learn the most by screwing up. Molecular biology rarely works the way you expect on the first try because too many things can go wrong with your sample, your protocol, your technique or a million other variables. I like to consider this a feature of science rather than a bug — I believe the best way to really understand a system is to understand all of the ways it works smoothly and all of its limitations. As Thomas Edison famously said: “I have not failed 700 times. I have succeeded in proving that those 700 ways will not work.”
But it would also take each of us a lifetime of trial and error to master every protocol perfectly, so our expertise is necessarily limited to the experiments we repeat day in and day out. The graduate students in my lab meet once a week to talk about our progress and our struggles, to share the insights we’ve stumbled across and the barriers stubbornly stuck in our way. If we’re lucky, the holes in our skill sets are complemented by our colleagues, and we have the opportunity to learn from someone else’s mistakes instead of our own.