Monday, March 20, 2017

On the nature of biology as an intellectual enterprise

Ashutosh Jogalekar, Why Technology Won't Save Biology, over at 3 Quarks Daily:
There have been roughly six revolutions in biology during the last five hundred years or so that brought us to this stage. The first one was the classification of organisms into binomial nomenclature by Linneaus. The second was the invention of the microscope by Hooke, Leeuwenhoek and others. The third was the discovery of the composition of cells, in health and disease, by Schwann and Schleiden, a direct beneficiary of the use of the microscope. The fourth was the formulation of evolution by natural selection by Darwin. The fifth was the discovery of the laws of heredity by Mendel. And the sixth was the discovery of the structure of DNA by Watson, Crick and others. The sixth [seventh?], ongoing revolution could be said to be the mapping of genomes and its implications for disease and ecology. Two other minor revolutions should be added to this list; one was the weaving of statistics into modern genetics, and the second was the development of new imaging techniques like MRI and CT scans.

These six revolutions in biology resulted from a combination of new ideas and new tools.
However, today:
In one way biology has become a victim of its success. Today we can sequence genomes much faster than we can understand them. We can measure electrochemical signals from neurons much more efficiently than we can understand their function. We can model the spread of populations of viruses and populations much more rapidly than we can understand their origins or interactions. Moore's Law may apply to computer chips and sequencing speeds, but it does not apply to human comprehension. In the words of the geneticist Sydney Brenner, biology in the heyday of the 50s used to be "low input, low throughput, high output"; these days it's "low input, high throughput, no output". What Brenner is saying is that compared to the speed with which we can now gather and process biological data, the theoretical framework which goes into understanding data as well as the understanding which come out from the other end are severely impoverished. What is more serious is a misguided belief that data equals understanding. The philosopher of technology Evgeny Morozow calls this belief "technological solutionism", the urge to use a certain technology to address a problem simply because you can.
Reductionism worked well for physics and chemistry, but not so effectively for biology.
Emergence is what thwarts the understanding of biological systems through technology, because most technology used in the biological sciences is geared toward the reductionist paradigm. Technology has largely turned biology into an engineering discipline, and engineering tells us how to build something using its constituent parts, but it doesn't always tell us why that thing exists and what relationship it has to the wider world. The microscope observes cells, x-ray diffraction observes single DNA molecules, sequencing observes single nucleotides, and advanced MRI observes single neurons. As valuable as these techniques are, they will not help us understand the top-down pressures on biological systems that lead to changes in their fundamental structures.

1 comment:

  1. Not at all sure about the answer but the first thought that comes to mind is general systems theory.