• Question: What have been the hardest/ most challenging projects you have had to solve. How did you ovecome these hurdles? What was you thought processes? What were your solutions?

    Asked by Kareena to Steven, Rebecca, Martha, Jo, Holly, Christopher, Chris, Anna on 9 Mar 2018.
    • Photo: Jo Nettleship

      Jo Nettleship answered on 8 Mar 2018:

      Hi Kareena,
      Wow, this is like an interview question!!
      I think I can split this into two aspects: One is a scientific challenge and the other is more to do with people/management. Both are things which a lot of scientists deal with on a daily basis.
      Scientific challenge: This happens quite a lot as I am involved in producing proteins and some of them do not want to be produced in excess as we would like. In order to overcome these hurdles, we approach the problem by trying many different options at the same time. This involves splitting the protein into domains (if the protein we are interested in is made up of domains), trying different fusion proteins (adding another protein onto the one we’re interested in with the hope that this helps with producing our protein) and trying to produce the protein using different cells (E. coli, insect cells and mammalian cells). We then have a lot of results at the same time which we can analyse to find the “best” way to make the protein. This “high-throughput” approach means we can (hopefully) get to the “best” method in the least amount of time. Obviously, this doesn’t always happen and so we then have to decide if we want to try more options or if we want to abandon the project in favour of other projects.
      People/management challenge: This comes up a lot for me as we have users of our facility and collaborators. Occasionally this can be challenging, particularly if our priorities are different. For example, the results may not be encouraging and so we may want to finish with a project, but the user wants us to continue. In these cases, it is good to be diplomatic – I would explain the poor results and then what experiments could be done and the cost of these both in terms of money and time. Then I can give them the option of putting one of their staff onto the project or possibly that they may want to apply for funding specifically for this project which would be a way forward. Sometimes, I will do one more specific, defined experiment for them to wrap the project up. I find the best way to deal with collaborators is to have regular meetings (maybe via Skype) so that they are aware of the work I am doing and I am aware of the work they are doing. Keeping up to date with progress and also priorities means that there is less likely to be conflict.
      I hope this helps,
      Jo

    • Photo: Martha Nari Havenith

      Martha Nari Havenith answered on 7 Nov 2018:

      Hi Kareena, for me personally, the hardest (science-related) challenges have been parts of my work where I needed to think in ways my brain doesn’t just ‘naturally’ think. In the end, those were some of the most fun parts as well though, because they really give you new abilities. Kind of like learning new dance moves, but with your brain – awkward at the start, but then super-rewarding. For me that was especially the case for learning to program (which I do all the time now and enjoy), and getting into electronics, which we need in order to wire up instruments for our experiments. For both of these you have to think very step-by-step, and I naturally like to think lots of things in parallel and relate them all to each other in weird ways. What helped me was to keep practicing and remember that just because you’re not good at something now it doesn’t mean you won’t be good at it later. And to do stuff in between that comes more naturally – you don’t want to bang your head against the wall all day long.
      In terms of the actual science, it’s always challenging (but again, also very fun) when you get some data and they don’t look at all like what you would have expected. In neuroscience that still happens a lot because there is so much we don’t know yet. So the brain has a pretty easy time surprising us. In those moments it’s important to let go of your previous ideas. Take a step back and just look at the data. And then wait, sometimes a long time, until they start making sense. Don’t see the weirdness of the data as a failure of your idea, but as an interesting new piece of information. Of course, exclude stupid mistakes one by one. But then, if you’re sure there are no obvious errors in the data, just sit back and kind of let them tell you what they mean.

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