This is an interesting article to read in the light of the A Farewell To Bioinformatics forum posting bemoaning the state of bioinformatics. In that post we have someone trained in computing (I assume) venting his spleen (fair enough) about the imprecision in biology (esp molecular biology) and in the current post we have someone also trained in a highly technical and probably reproducible field complaining that biolgists are not taking advantage of technological and methodological advances in engineering to make their work more reproducible. C'est la vie.
However I think that one of the main problems with biomedical research is that it is not particularly reproducible. For example we sample (small sample usually) from a diseased population, carry out some assays, get back some noisy data and try to extrapolate from that. Setting aside the small sample problems if you and I both have asthma I doubt that we both have the 'same' asthma. The label 'asthma' is just that, a label for a constellation of signs, symptoms and processes that we often assume are driven by the same underlying cause. That's probably not always true (see here for example). Other polygenic diseases are also likely different at individual scales. The same review also makes the point that the pre-clinical animal models for diseases are mostly very poor models for the human disease. To my mind in drug development for human diseases we should be interested in the specifics of human disease not what we can generalise from pre-clinical models (indeed that does not seem to be working particularly well) and human biology is inherently noisy at the scale we currently operate at.
In light of this the application of more technology and, crucially, more individuals qualified to use that technology to biomedical and other biological problems should indeed be welcomed. We not only have to learn how to collect more human specific data but learn ways of stratifying within conditions (eg diseases) more effectively. The author of the article bemoans the lack of crossover from biologists willing to engage with technologists and I sympatise. However this works both ways. Those with more technological sense have to be willing to engage fully with the noisy nature of biological data and not necessarily expect the results of their techniques to be strongly reproducible across different samples. This noisey data seems to 'annoy' some of our more technically minded colleagues and there is likely no overarching model useful for all human biology.
The author ends on an optimistic note but the impact made by the researchers he mentions can only be measured once the compounds they are creating become available for treating diseases - early days yet. When I was a child we were promised flying cars and holidays on the moon!
In summary I think we are probably doing biomedical research 'wrong' to some degree and there does need to be change. I think that adopting techniques to study human disease in situ, decreased use of pre-clinical models and the application of technical advances to stratify individuals will have an impact. How that technical/biological dynamic develops will be interesting.
First off, don't repost copyrighted content from other sites:
For reprints and/or copyright permission, please contact Jay Mulhern, (781) 972-1359, jmulhern@healthtech.com.
Secondly this is not a question.
I reopened it as Forum - it is perhaps better to get the rants all in one place off our chest ;-)
Perhaps after removing the content leaving a short excerpt with a link it could be reclassified as Forum. After all we do have a similar post there.