Great minds think alike and, often, their ideas appear approximately about the same time. In this case I am taking about the following articles: Stuck in the middle by Oxford's Linus Schumacher, a colleague of my friend and fellow Moffitt researcher Jacob Scott.
Interdisciplinary research: why is is seen as a risky route by Sarah Byrne, a PhD student from Imperial College London
The role of mathematical oncology by my colleague and friend at Moffitt Philip Gerlee.
Byrne's article highlights some of the challenges that young interdisciplinary researchers have to face in terms of career options. By becoming competent at many things it becomes very difficult to compete with people that are the masters of one field. Although places like Moffitt are happy to host scientists that are good at say, experimental research and mathematical modelling, most academic departments in many universities seem unwilling or uninterested in accomodating interdisciplinary scientists.
As Schumacher writes, this is not the only challenge for interdisciplinary researchers. A big problem, especially for us modellers, is getting recognition for our work and, as he says:
Sometimes I feel like the most valuable contribution by a mathematician to a collaborative research effort can be the different way of thinking they bring to the table, and the conceptual insights that result from the cross-disciplinary discussion.
That feeling seems to be mirrored in Gerlee's post:
This is not to say that mathematical modelling does not contribute to our understanding of cancer, but rather that the insights gained from it arrive in smaller chunks and are absorbed by the experimental and clinical community. These insights and novel concepts then shapes their thinking and inspires them to perform new experiments or look at existing data in new ways.
This is not always the rule as he points out in an edit referring to a key piece of mathematical work that helped the field of CML research. Mathematical and computational models provide a number of benefits. One of the key advantages of bringing mathematical modelling to biology is that we can help distinguish the hypotheses worth exploring experimentally from those whose logic is faulty. It's easy for people to forget the importance of weeding out the wrong hypotheses from those meriting further experimental work but that does not make it any less important.