More collaborative modelling please

Science, Technology 2013-10-18

Last week, the 2013 Nobel Prizes in PhysicsChemistry, and Physiology or Medicine recognised eight scientists whose research contributions, spanning many years, have contributed significantly to improving our understanding of how the natural world works. These discoveries include the theory of how particles acquire mass; the development of computer simulations of chemical processes; and the identification of the vesicular transport and trafficking system in cells. The research contributions also demonstrate three different approaches to using scientific modelling, raising the question: should we be using more theoretical models?


The Nobel Prize in Physics was awarded jointly to François Englert and Peter Higgs for their theory of how particles acquire mass, which they proposed (independently of one another) in 1964. Their theory describes a process whereby particles acquire mass through contact from an invisible field that fills all space. The theory depends on the existence of the Higgs particle, which originates from the invisible field, but it was only shown in 2012 that the Higg particle exists, following work done with the Large Hadron Collider at the CERN Laboratory in Switzerland. Englert and Higgs’ theory is also part of the Standard Model of particle physics, which defines matter particles and force particles. The model states that matter particles are the building blocks that make up everything in the world, and force particles control the governing forces which ensure all those building blocks (and the minerals or organisms that they make up) actually function. These theories may not answer all of the big questions that have occupied physicists and philosophers over the ages, but they do provide a framework and common language that can be used to ask, and address, further questions to aid our understanding.


The Nobel Prize in Chemistry was awarded to Martin Karplus, Michael Levitt and Arieh Warshel for their pioneering work in the 1970s. Their studies led to the development of computer simulations based on principles from both classical and quantum physics, in order to understand and predict the outcomes of chemical reactions and experiments. The field of chemoinformatics, which subsequently emerged, provides vital tools today for modelling to be carried out in silico. Such computational approaches allow processing of enormous datasets with numerous parameters that are well beyond the timescales and comprehension of manual researchers. This provides rapid, powerful and highly efficient ways for scientists to test out innovative ideas, and advance R&D in diverse fields, such as drug discovery, medicine, alternative energy and novel material sciences.


The Nobel Prize in Physiology or Medicine was awarded to Randy Schekman, James Rothman and Thomas Südhof, whose separate studies have identified the protein machinery, mechanics and regulation behind the cellular vesicle transport system. The findings have substantially advanced our understanding of how some individual components within cells work, and will be used to provide new insight into how malfunctions in vesicle transport systems can contribute to a variety of diseases. Unlike the theoretical physics and computational chemistry work mentioned above, these studies applied existing model systems and theories to generate new data and biological insights.


It often seems that we rely on physicians and mathematicians to develop more abstract laws and theories, while biologists are more focused on applying those laws to specific contexts and observing what actually happens. Biology-based research (sometimes labelled “soft science”) may depend on the governing principles of chemistry, physics and mathematics, but ultimately it generates context-specific, critical data upon which clinical therapies are developed.


There seem to be relatively few examples of fundamental theories and models being developed specifically from biological studies. The main examples might include Darwin’s theory of natural selection, Mendelian inheritance, Watson & Crick’s model of the structure of DNA, and cell theory. There are some relatively new research fields that are sometimes called ‘theoretical biology’, for example population genetics, phylogenetics, and conservation and modelling of climate change. These employ theoretical approaches to develop new tools that may be used to address biological questions, but these cross-discipline areas are led by statisticians, bioinformaticians, and computational scientists, as much as by biologists. Arguably, the success of these areas in developing and applying theories to advance our understanding of life comes from cobining scientists across multiple disciplines.


Should biologists be trained more thoroughly in mathematics and physics? The relatively recent field of systems biology integrates computational modelling and large quantities of systems-wide data from biological studies, in order to understand how molecular networks within organisms work. However, the reality is that we still don’t fully understand how the individual components of those molecular networks actually work.


As this year’s science Nobel Prizes have demonstrated, we need a combination of abstract thinkers, modellers, implementers and observers. Following Higgs’ and Englert’s proposals of how particles acquire mass, it took nearly 50 years for scientists to actually develop the instruments and experimental methods needed to apply the theory and collect data to prove it. We will always need more abstract thinkers to propose fundamental ideas and applied scientists to use those models, as well as state funding and industrial investment to develop technologies such as CERN’s Large Hadron Collider that are capable of providing the answers. However, with greater collaboration between the different scientific fields, and stronger recognition of the value that theoretic science brings to applied research, we might be more successful in reducing the time lags between idea generation and practical application so that we can all benefit from scientific advances more quickly.


What do you think? Let us know @kateatnotch 



The Nobel Prize in Physics 2013. Nobel Media AB 2013. Web. 18 Oct 2013

The Nobel Prize in Chemistry 2013 – Advanced Information. Nobel Media AB 2013. Web. 18 Oct 2013

The Nobel Prize in Physiology or Medicine 2013. Nobel Media AB 2013. Web. 18 Oct 2013