Is Science Mostly Driven by Ideas or by Tools?

This week’s issue of Science has a pair of interesting essays looking at the history of science and what drives progress in scientific research.  In the first essay, Freeman Dyson examines the question through the lens of two books: Thomas Kuhn’s The Structure of Scientific Revolutions and Peter Galison’s Image and Logic.  Whereas Kuhn argues that new ideas and paradigms (e.g. relativity and quantum theory) drive science forward, Galison sees the invention of new technologies (in particular the move from analog technologies to digital technologies) as having shaped the evolution of science.  Dyson falls more on the side of Galison, arguing that new technologies drive the creation of new ideas (e.g. invention of the steam engine led to the development of thermodynamics) and that new tools enable the discoveries that create new paridigms:

The great recent discoveries in the physical sciences were dark matter and dark energy, two mysterious monsters together constituting 97% of the mass of the universe. These discoveries did not give rise to new paradigms. We cannot build paradigms out of ignorance. The monsters were discovered by using the new tools of astronomy, wide-field cameras, and digital data processing. We must study the monsters patiently with new and more precise digital tools before we can begin to understand them. Galisonian science will continue to explore, with constantly evolving tools, the structures of space and time and galaxies and particles and genomes and brains.

On the other hand, Sydney Brenner looks at the history of biology in a companion essay and comes to the opposite conclusion:

We can now see exactly what constituted the new paradigm in the life sciences: It was the introduction of the idea of information and its physical embodiment in DNA sequences of four different bases. Thus, although the components of DNA are simple chemicals, the complexity that can be generated by different sequences is enormous. In 1953, biochemists were preoccupied only with questions of matter and energy, but now they had to add information. In the study of protein synthesis, most biochemists were concerned with the source of energy for the synthesis of the peptide bond; a few wrote about the “patternization” problem. For molecular biologists, the problem was how one sequence of four nucleotides encoded another sequence of 20 amino acids.

In essence, the revolution in biology was sparked by the simple idea that DNA encodes information.  Prior to the discovery of the double helix in 1953, biologists had been studying the physical and chemical properties of chromosomes, trying to understand how they could make a cell.  After 1953, however, the ideas born from Watson and Crick’s paper focused experimenters attention to the important question of how the information within DNA gets read.  Indeed, the answer to that question forms the central dogma of modern molecular biology.

As a biologist myself, I tend to side more with Brenner.  While it is true that new tools are essential for making new discoveries in science, new ideas are even more important because they define the important questions in science.  Indeed, today new tools such as next generation sequencing technologies are helping make new discoveries, but in many cases our ability to generate data outpaces our ability to understand it.

Of course, in the end Kuhnian science (driven by ideas) and Galisonian science (driven by tools) constantly feed back upon one another.  New tools can generate new findings that challenge old ideas, perhaps sparking the Kuhnians to seek new ideas.  These new ideas, in turn, generate new questions that the Galisonians can answer with new tools.  This feedback is already apparent in the various efforts to map the connections between all of the neurons in the brain (to create the so-called connectome).  The effort is a purely Galisonian one, to be driven forward by new microscopy technologies and data analysis techniques, but understanding how to interpret the connectome and make any sense of the data will require the invention of new paradigms for how we think of the brain.



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