In silico medicine drives medical research back into Pasteur’s quadrant

Traditionally, we separate science basic research, which quests for fundamental understanding of nature without any consideration of the possible use of such knowledge, and applied research, which aims to solve specific problems without any ambition of adding anything to the fundamental understanding.  If we draw a 2x2 table, the top left quadrant with no consideration of use and quest for fundamental understanding is sometime called the Bohr’s quadrant, while the bottom right quadrant no quest for fundamental understanding and consideration of use is called the Edison’s quadrant.  But there is a third type of research, sometime referred as the Pasteur’s quadrant, that while motivated by clear considerations of use, produces nevertheless fundamental understanding. Donald Stokes, in his book “Pasteur’s Quadrant: Basic Science and Technological Innovation”, advocates that this third type of research, although least common, is potentially the most rewarding for society.

I agree with Stokes, but I have an additional reason to believe that that falling in Pasteur’s quadrant is the best science. Too frequently “curiosity-driven” research turns out to be “opportunity-driven” research, where we carefully select research questions that can be rigorously answered with the methods and approaches available; in my opinion, this can be epistemologically dangerous, for example when it produces causal reductionism.

The radical specialisation of biomedical research in the last 30 years has produced a dramatic polarisation of large volumes of basic biological research, which turns out to be very difficult to translate into any application related to human health, and an applied medical research still too frequently built on shaky methodological and theoretical foundations.

So, pushing biomedical research back into the Pasteur’s quadrant would be a desirable thing. I believe the trajectory of in silico medicine is producing this positive effect. When in silico medicine started, its key feature soon became, especially within the European Virtual Physiological Human (VPH) initiative, the ambition of predicting how specific physiology determinants would change due to the progression of disease or interventions in each individual patient. To do so effectively, it was essential to rely as much as possible on mechanistic knowledge, that does not rely on population average generalisations.  This forcefully narrowed the scope of VPH models to those parts of human physiology for which reliable mechanistic knowledge is available, such as biomechanics, mechanobiology, electrophysiology, bioenergetics, etc.

But now I believe we are starting to observe a reverse effect, where in silico medicine is not only research on the application of the available knowledge but also drives the production of new fundamental mechanistic knowledge on physiology, pathology, and the mechanism of action of interventions, knowledge that subject-specific models need to be more useful supporting relevant clinical decisions. This is producing a completely different type of in silico medicine research, that while motivated by well-defined clinical problems, pursue the discovery of new fundamental knowledge.  Interestingly, this does not limit anymore in silico medicine to the use of computational methods, but rather promotes the type of circular synergy between modelling and experimentation that others advocated.

We as a community need to reflect on this new trend, and explore how the available funding instruments need to be adjusted to support this potentially rewarding development.