The journal Nature had a recent piece titled ‘Life-changing experiments: The biological Higgs‘ asking what fundamental questions in biology might inspire the same thrill as the Higgs particle is currently doing in physics. The four ‘quests’ they have came up with are:
- Is there life elsewhere?
- Is there foreign life on earth?
- How did life start…?
- …and can we delay its end?
Biological quests lack mathematical precision
That all sounds nice and I am sure these are valuable questions worth pursuing. But I still don’t feel a lot of excitement stirring up inside me. One of the reasons why I am underwhelmed is already pointed out in the Nature article:
“[M]ost biological quests lack the mathematical precision, focus and binary satisfaction of a yes-or-no answer that characterize the pursuit of the Higgs.” *
First of all –before we get into biology– I would be very surprised if physics was indeed a yes-or-no game providing ‘binary satisfaction’. Just read Collins and Pinch’s account in The Golem of two experiments that ‘proved’ the theory of relativity to see that things are messy everywhere in sciene. You always need statistics to sort signal from noise to get any satisfaction at all.
But –more importantly and back to biology– it is certainly true that biological quests lack quantitative theory and mathematical precision. In that respect biology and physics are worlds apart.
“Biologists may have little cause to envy physicists — they generally enjoy more generous funding, more commercial interest and more popular support,” *
writes Nature. And parts of that are true: I work in cancer biology, and the funding, commercial interest and popular support are indeed huge. But the general attitude is better captured in genius-physicist Sheldon Cooper‘s dismissive remarks about Amy‘s single-author paper on the cover of Neuron:
“[W]hat you’re forgetting is: it was an achievement in the field of biology. That’s all about yucky, squishy things.” *
Sheldon’s point is well taken: without a good theory biology will always be the inferior science. The reason is simple: without a theory you can never predict stunning novel facts. You can only describe what you have seen.
The Higgs boson is a case in point. The multi-billion dollar quest is based on theoretical predictions that were put forward in 1964 by several physicists, including Peter Higgs at Edinburgh University. That a purely theoretical result can spark world-wide collaborations and the development of expensive gadgets is seen as a sign for ongoing progress in physics — in particular if physicists actually manage to find the elusive particle.
Good science predicts stunning novel facts
Experimental validation of theoretically predicted facts is the thrill physicist seek. And the way physicist do science is often taken as the yard-stick all other sciences are measured against, just like the Nature article on the ‘biological Higgs’ did. Physicist-science also had a huge impact on theories of science – from Popper and Kuhn to Feyerabend and Lakatos.
[Lakatos] proposes that scientists regard the successful theoretical prediction of stunning novel facts – such as the return of Halley’s comet or the gravitational bending of light rays – as what demarcates good scientific theories from pseudo-scientific and degenerate theories, and in spite of all scientific theories being forever confronted by “an ocean of counterexamples”. *
If the prediction of stunning novel facts is what sets successful science apart from degenerate theories, how is biology doing? Not so well, you’d think. All those big international consortia swamping us in GWAS and ever more genome sequences don’t follow up on predicted facts, they generally don’t have any hypothesis at all (except ‘more is better’, of course).
But, before we despair, there are also some bright spots in the history of biology. For example, Jeremny Gunawardena writes in his essay Some lessons about models from Michaelis and Menten:
“In fact, quantitative methods and mathematical tools have always been used in biology, going back to Harvey’s wonderful demonstration of the circulation of the blood. If this synergy is not widely appreciated, it is because our historical memories are woefully short. In consequence, there is little shared understanding about models.” *
Gunawardena then uses Michaelis and Menten’s classic 1913 paper on enzyme kinetics to draw some lessons on the relationship between mathematics and biology. Quick recap: observing that the change of concentration of a product is highly non-linear in the concentration of substrate, Michaelis and Menten built a theory around a postulated enzyme-substrate complex.
“What makes Michaelis and Menten’s model so significant was not that it fits the experimental data, but that it provides evidence for something unseen” *
Indeed the enzyme-substrate complex was only verified 30 years later. Gunawardena stresses that this example shows a theoretical core of biology and even claims at some point that biology is ‘more theoretical than physics’.
Theory or no theory, that is the question
So, in summary we see a pretty mixed picture in biology. While there are some examples of theoretical predictions, most of it is quite descriptive. With this mixed evidence we could try and argue in one of two possible ways:
Option 1 — the Idealist: Biology is an intrinsically theoretical science, where mathematical predictions of stunning novel facts should guide experiments. Then the revivial of mathematical biology in the last decade under the name of systems biology might be a first step into the right direction (even though systems biology is not a clearly defined science and sometimes overly hyped and content-free).
Option 2 — the Pragmatist: Biology is an intrinsically descriptive science that is much more complex than physics and will never be as theory-guided. It’s hard enough to describe the ‘landscape’ of mutations increasing cancer risk in the general population even without a theory. And no one needs mathematics to follow cell divisions in a microscope and observe stunning novel facts.
The first option faces the problem that a predictive theory of biology still needs to be found, and the second option is highly unsatisfying (at least for me; maybe I should spend more time with microscopes).
As always, truth will be somewhere in the middle between these two extremes. For now, I will stay a descriptive curve-fitter and data analyzer, but with one eye on theoretical developments in systems biology and biophysics.