October 25, 2007

THE NEW NEW SCIENCE IS THE OLD OLD CREATION::

All systems go: A powerful way of studying biology looks set for take-off (The Economist, 10/25/07)

[T]he beating heart was no simple video. It was, instead, the output of a stupendously complex computer model of a heart, developed over more than 40 years. This model is an example of “systems biology”, an approach that represents a significant shift both in the way biologists think about their field and in how they go about investigating it.

A central tenet of most scientific endeavour is the notion of reductionism—the idea that things can best be understood by reducing them to their smallest components. This turns out to be immensely useful in physics and chemistry, because the smallest components coming from a particle accelerator or a test tube behave individually in predictable ways.

In biology, though, the idea has its limits. The Human Genome Project, for example, was a triumph of reductionism. But merely listing genes does not explain how they collaborate to build and run an organism. Nor do isolated cells or biological molecules give full insight into the causes and development of diseases that ravage whole organs or organisms. A complete understanding of biological processes means putting the bits back together again—and that is what systems biologists are trying to do, by using the results of a zillion analytical experiments to build software models that behave like parts of living organisms.


Note the interesting but unsupportable assertion.

Posted by Orrin Judd at October 25, 2007 2:37 PM
Comments

A confusion of necessity with sufficiency. And objectivity with insight.

Posted by: ghostcat at October 25, 2007 3:55 PM

because the smallest components coming from a particle accelerator or a test tube behave individually in predictable ways.

"Predictable" is such a poor word choice there. They behave collectively and at the macro level in deterministic ways. Individually, they behave probabilistically, but individual particles are not predictable deterministically. "Predictable" confuses the issue.

Posted by: John Thacker at October 25, 2007 4:36 PM
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