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Thursday, 4 May 2017

The role of in silico medicine within biomedical research

The publication in 1823 of the first issue of Lancet symbolically marks the 19th century revolution of biomedical research. Out of this expansion, modern biomedical research organise itself around three fairly different paradigms, each trying to cope with impossible complexity of human body:
  • Cellular and molecular biological research, driven by an aggressively reductionist agenda, which focus on small sub-units of the system;
  • Clinical research, which largely treats the human body as a black box, and vastly relies on the statistical modelling of empirical observations;
  • Physiological research, which tries to investigate the human body following the approach typical of physical and engineering sciences.
The third approach, undermined by the dramatic limitations of 19th-early 20th century calculus and instrumentation, has been the least successful of the three, and its importance has been progressively decreasing.

Two events we believe are changing this scenario: the first is the dramatic progress that physical and engineering sciences drove around biomedical instrumentation. Using x-ray, magnetic fields, ultrasounds, we can now imagine the inside of the human body with amazing accuracy; automated chemical analysers, spectroscopes, sequencers are offering a high-throughput biochemistry that open entirely new scenarios; the amazing capabilities of modern electrophysiology give us details on the working of the heart, the muscles, the brain; motion capture, dynamometry and wearable sensors offer a detailed view of the biomechanics of human movement.  In short, today we can collect a deluge of quantitative data on each individual patient that describe in full detail their anatomy, physiology, biochemistry, metabolism, etc.

The second is the amazing development of calculus, thanks to the advancements of mathematics, computational science and engineering, and of the raw computational power that modern hardware and software can offer to modelling and simulation.  This development is essential, because for the first time in history we can solve the enormous number of complex mathematical equations that can quantitatively describe many physiological and pathological processes; we can finally handle the complexity, and we do have the means to measure pretty much all we want to measure in each individual patient. 

The problem with complex living organisms is that they are dramatically entangled, so the functioning of each of their parts can hardly be assumed independent from the others; while large part of biological research ditch this problem with reductionism, and clinical research bypass it entirely by renouncing to the search for detailed mechanistic explanations, a biomedical research agenda based on the methods of physical and engineering sciences must face this complexity; and this is possible only if we use mathematical and computational methods to formulate our theories and quantitatively compare their predictions to experimental observations as primary mean of their tentative falsification.  

At the same time, once a theory resist extensive falsification, the predictive model that embodies it can be used to solve clinically relevant problems, as many of the grand challenges of modern medicine (prevention, personalisation, participation) would be easily addressed by an increased ability to accurately predict the course of a disease or the effect of different treatment option for each individual patient. 

Therefore, we believe in silico medicine is the main road through which the great physiologists of the past generation will be ultimately proven right, and a biomedical science based on the methods of physical and engineering science will become more and more successful.

In this sense, we believe in silico medicine is not only a new family of technologies we can use to investigate the human body, but it may enable a paradigm shift in the sense proposed by philosopher Thomas Kuhn, “a fundamental change in the basic concepts and experimental practices of a scientific discipline”.

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