DNA Is Multibillion-Year-Old Software

Nature invented software billions of years before we did. “The origin of life is really the origin of software,” says Gregory Chaitin (inventor of mathematical metabiology). Life requires what software does. It is fundamentally algorithmic. And its complexity needs better thinking tools.

DNA Is Multibillion-Year-Old Software


Nature invented software billions of years before we did. “The origin of life is really the origin of software,” says Gregory Chaitin. Life requires what software does (it’s foundationally algorithmic).

1. “DNA is multibillion-year-old software,” says Chaitin (inventor of mathematical metabiology). We’re surrounded by software, but couldn’t see it until we had suitable thinking tools.

2. Alan Turing described modern software in 1936, inspiring John Von Neumann to connect software to biology. Before DNA was understood, Von Neumann saw that self-reproducing automata needed software. We now know DNA stores information; it's a biochemical version of Turning’s software tape, but more generally: All that lives must process information. Biology's basic building blocks are processes that make decisions.

3. Casting life as software provides many technomorphic insights (and mis-analogies), but let’s consider just its informational complexity. Do life’s patterns fit the tools of simpler sciences, like physics? How useful are experiments? Algebra? Statistics?

4. The logic of life is more complex than the inanimate sciences need. The deep structure of life’s interactions are algorithmic (loosely algorithms = logic with if-then-else controls). Can physics-friendly algebra capture life’s biochemical computations?

5. Describing its “pernicious influence” on science, Jack Schwartz says, mathematics succeeds in only “the simplest of situations” or when “rare good fortune makes [a] complex situation hinge upon a few dominant simple factors.”

6. Physics has low “causal density” — a great Jim Manzi coinage. Nothing in physics chooses. Or changes how it chooses. A few simple factors dominate, operating on properties that generally combine in simple ways. Its parameters are independent. Its algebra-friendly patterns generalize well (its equations suit stable categories and equilibrium states).

7. Higher-causal-density domains mean harder experiments (many hard-to-control factors that often can’t be varied independently). Fields like medicine can partly counter their complexity by randomized trials, but reliable generalization requires biological “uniformity of response.”

8. Social sciences have even higher causal densities, so “generalizing from even properly randomized experiments” is “hazardous,” Manzi says. “Omitted variable bias” in human systems is “massive." Randomization ≠ representativeness of results is guaranteed.  

9. Complexity economist Brian Arthur says science’s pattern-grasping toolbox is becoming “more algorithmic ... and less equation-based.” But the nascent algorithmic era hasn’t had its Newton yet.

10. With studies in high-causal-density fields, always consider how representative data is, and ponder if uniform or stable responses are plausible. Human systems are often highly variable; our behaviors aren’t homogenous; they can change types; they’re often not in equilibrium.

11. Bad examples: Malcolm Gladwell puts entertainment first (again) by asserting that “the easiest way to raise people’s scores” is to make a test less readable (n = 40 study, later debunked). Also succumbing to unwarranted extrapolation, leading data-explainer Ezra Klein said, "Cutting-edge research shows that the more information partisans get, the deeper their disagreements.” That study neither represents all kinds of information, nor is a uniform response likely (in fact, assuming that would be ridiculous). Such rash generalizations = far from spotless record. 

Mismatched causal density and thinking tools creates errors. Entire fields are built on assuming such (mismatched) metaphors and methods.  

Related: olicausal sciences; Newton pattern vs. Darwin pattern; the two kinds of data (history ≠ nomothetic); life = game theoretic = fundamentally algorithmic.

(Hat tip to Bryan Atkins @postgenetic for pointer to Brian Arthur).

Further reading: Microsoft Plans to Have a DNA-Based Computer by 2020

More: Human DNA Could Store All the World's Data

Illustration by Julia Suits, The New Yorker Cartoonist & author of The Extraordinary Catalog of Peculiar Inventions.

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Credit: NASA / ESA via Getty Images
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This article was originally published on our sister site, Freethink.

An international team of astronomers has conducted the biggest survey of stellar nurseries to date, charting more than 100,000 star-birthing regions across our corner of the universe.

Stellar nurseries: Outer space is filled with clouds of dust and gas called nebulae. In some of these nebulae, gravity will pull the dust and gas into clumps that eventually get so big, they collapse on themselves — and a star is born.

These star-birthing nebulae are known as stellar nurseries.

The challenge: Stars are a key part of the universe — they lead to the formation of planets and produce the elements needed to create life as we know it. A better understanding of stars, then, means a better understanding of the universe — but there's still a lot we don't know about star formation.

This is partly because it's hard to see what's going on in stellar nurseries — the clouds of dust obscure optical telescopes' view — and also because there are just so many of them that it's hard to know what the average nursery is like.

The survey: The astronomers conducted their survey of stellar nurseries using the massive ALMA telescope array in Chile. Because ALMA is a radio telescope, it captures the radio waves emanating from celestial objects, rather than the light.

"The new thing ... is that we can use ALMA to take pictures of many galaxies, and these pictures are as sharp and detailed as those taken by optical telescopes," Jiayi Sun, an Ohio State University (OSU) researcher, said in a press release.

"This just hasn't been possible before."

Over the course of the five-year survey, the group was able to chart more than 100,000 stellar nurseries across more than 90 nearby galaxies, expanding the amount of available data on the celestial objects tenfold, according to OSU researcher Adam Leroy.

New insights: The survey is already yielding new insights into stellar nurseries, including the fact that they appear to be more diverse than previously thought.

"For a long time, conventional wisdom among astronomers was that all stellar nurseries looked more or less the same," Sun said. "But with this survey we can see that this is really not the case."

"While there are some similarities, the nature and appearance of these nurseries change within and among galaxies," he continued, "just like cities or trees may vary in important ways as you go from place to place across the world."

Astronomers have also learned from the survey that stellar nurseries aren't particularly efficient at producing stars and tend to live for only 10 to 30 million years, which isn't very long on a universal scale.

Looking ahead: Data from the survey is now publicly available, so expect to see other researchers using it to make their own observations about stellar nurseries in the future.

"We have an incredible dataset here that will continue to be useful," Leroy said. "This is really a new view of galaxies and we expect to be learning from it for years to come."

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  • The team at Clearspace, in collaboration with the European Space Agency, is on a mission to capture one such object using an autonomous spacecraft with claw-like arms. It's an expensive and very tricky mission, but one that could have a major impact on the future of space exploration.

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