Credit: Julia Mirabella

There is a big elephant in the biology room, as evidenced by the DREAM7 challenge: how to encourage collaboration between industry and academia to solve today’s biggest biological problems. Fortunately, the elephant is beginning to receive deserved attention – in places like the University of Oxford, where I am, students are being placed in the interface between industrial and academic partnerships to tackle biological problems using computational methods.

Unlike traditional PhD programmes in which students are simply paired up with academics in the faculty, our programme (SABS-IDC, Oxford) gives us the opportunity to collaborate with industrial supervisors. This combination of academic and industrial viewpoints has broadened my perspective on how to approach today’s research problems. Since our PhD (or what we call a ‘DPhil’ at Oxford) integrates these two ends of the spectrum, I feel that we get the chance to develop elegant solutions toward pertinent problems in today’s biomedical research.

Effectively, because we are given such distinct research questions – such as, “How do we model therapeutic antibodies?” – the major benefit of working with industry has been validation. As a computational biologist, I sometimes get light-hearted criticism from experimentalists who ask, “What’s the point of the model?” or, “How do you know this will be observed in the bench?”

They are correct; my computational models would be useless if they did not agree with experimental data. However, working with industry has validated for me the use of computational methods in biomedical research. With the industry’s emphasis on high-throughput analysis and an increasing trust in computational methods, the problems that I am trying to solve feel just as important as an experimentalist’s research.

Ultimately, industrial collaboration has given me perspective on how to investigate biological problems in novel, unorthodox methods, mainly from a computational view. Moreover, it feels that there is a definite immediate impact in the research that makes the work even more rewarding. I think other students will feel the same, and hopefully, more challenges like DREAM7 will inspire industrialists and budding students alike to cooperate, and develop new (hopefully computational) tools for solving tomorrow’s problems.

Jinwoo Leem