Data-driven research

How many of us have planned out a string of experiments based on certain specific assumptions, grounded by previous work, published literature, and Professor’s expectations, only to have it all fail on the first try? And upon subsequent further attempts?

You spend weeks, months, sometimes years, trying to optimize every aspect of the experiment, but it still fails to give you the result you expect (or want). You hear people around you say, ah, this is the life of a scientist, things are never expected to work on the first try. You keep at it, going in on weekends, spending every waking hour thinking where did I go wrong? As you do that, time slips by, you are wasting reagents, your Professor is losing patience, and there is a dawning intense realization that your time is running up and you have to graduate in 2 years and with nothing to show for. Is it too late for a project change?

Scary as it sounds, it is a common scenario for many PhD students, myself included. I was trying to set up a cellular model for Huntington’s disease (HD), and to cure it by altering protein equilibrium in the cell. There were hundreds of papers showing how expressing the Huntingtin protein was enough to produce cell death, presumably via its aggregating properties. But it was not a simple and cut process, there were various models of inducible expression, overexpression, and cell-lines that endogenously express the “toxic” protein. But in my hands, these “toxic” effects were not apparent. It usually required an additional stress but then more questions would arise as to which stress is appropriate to reproduce a disease model, and how much stress should one apply? I know I was not alone because coming from industry prior to my PhD, I knew even pharma was having problems establishing the HD cell model. And so 2 years went by, filled with variable results and slow sometimes invisible progression. Thankfully, I had a backup project which was beginning to yield more interesting results and we ended up switching to that. And it was a harried next 2 years playing catch-up and producing enough results to enable me to graduate.

Sometimes one just has to know when to give up. Science is driven by nature, not man. No amount of hard work, hours in the lab, planning and theorizing, will change the outcome of an experiment. If it is not working, and you have exhausted the possibilities, let it go. This is called data-driven research. I see so many students, spending time, reagents, and their own energy, repeating an experiment over and over again (sometimes without varying anything!). And sometimes Professors (usually the young ones)  will be behind this insanity, being so intent on achieving a particular result, believing their careers rest on this, that they unwittingly drive their labs to the ground.

Usually, experiments fail for good reason. Sometimes reagents are faulty, conditions are not ideal, something stupid like not letting a gel run long enough for bands to separate, can cause weeks of head-scratching, agony and frustration. What does help is asking people around you, or sometimes asking the internet. Gaining new perspectives can reveal things you never realised. The problem is when students think it is their own fault and beat themselves up about it, repeating the experiment till the cows come home. And sometimes things just do not work. Move on already. Or find another method to prove your point. There are numerous ways to prove a point! An important side-note: It is key to record down all your experimental observations. Without this recording process, one cannot know where one went wrong. You do not want to be second-guessing yourself on whether you really added in the correct reagent.

It also helps to distance yourself from the lab when things go wrong. Take a walk, have a coffee, do something to take your mind away from it. Gives you some bigger picture perspective and also calms the nerves. Just my 2 cents 🙂

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One thought on “Data-driven research

  1. Pingback: Designing effective experiments – Science on a daily basis

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