Science behind the design
By Jay Stone
I am a second year cell biology PhD student. My research group is based at the Institute of Ophthalmology in London and together we are working on characterising the molecular mechanisms behind an eye disease called Macular Telangeictasia (MacTel).
Our sight affects our interpretation of the world and the way we communicate with those around us. At the centre of our retina we have a specialised region known as the macular, which has no blood vessels. This area of our eyes is home to colour-perceiving cells known as cones. Our cones afford us a high ‘central’ visual acuity: clearness of vision. Patients with MacTel lose this central vision because the blood vessels in the peripheral regions of the retina start to leak and grow into the macular.
My lab hopes to identify genes candidates for MacTel so that we can design treatments. Our research team is screening animal models with the same retinal vessel abnormalities as human patients to uncover disease genes. Any such discovery would be groundbreaking and thus guaranteed publication in a well-respected academic journal. However, along the way we will have to rule out some genes as not being important, meaning we will generate a lot of ‘negative’ data. This negative data is an important part of the process and a result of the same rigorous scientific techniques that generate positive data. Although it is not considered new, novel or exciting and thus is of little interest to the big names in scientific publishing.
A scientist’s opinion on negative data might depend on where they’re at in their career. A PhD student might see the experiment as ‘not working’; the eager fresh-faced post doc might see it as a waste of their time; but I like to think that the established professor might see it as useful and worthy of publishing.
In my case negative data tells me I must’ve done something wrong. It must be my fault the experiment did not show what we hoped. And to increase my chances of getting a good job after my PhD, I need to get my work published; preferably in a good journal. Thus negative data can cause frustration.
‘The Good, the Bad and the Negative’ aims to challenge the perception of data as ‘negative’. Berit and I wanted to create something as much about science as design. Something we’d both been involved in from its inception, rather than an attempt to simply compress a scientific concept into a pretty design for people to look at. We wanted to comment on the thought processes behind the two disciplines, their similarities, their differences and ultimately what each can bring to the other.
Negative data may not be an exciting answer, but it is nonetheless correct. We should take a step back from our supposed ‘dead ends’ and see where they could actually lead us. It may be different from where we thought we were heading, but who’s to say it wouldn’t be better?
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