>I find the coding analogy for DNA or cell biology in general to be rather problematic. It's fine as a very rough and oversimplified analogy, but if you try to

>I find the coding analogy for DNA or cell biology in general to be rather problematic. It's fine as a very rough and oversimplified analogy, but if you try to apply it to the details I think it hurts more than it helps. [...] , but inevitably they will move out of the bounds of where the anlogy works unless they actually know the underlying biology. I think the author's "digital" explanation for DNA is more helpful than problematic because we can just understand where that knowledge can be appropriately applied. Focusing on the higher abstraction of DNA as "information" or "data" is important in bioinformatics: https://en.wikipedia.org/wiki/Bioinformatics https://en.wikipedia.org/wiki/Bioinformatics And analyzing DNA as "information" and not just analog chemistry is also important in CRISPR gene editing. Yes, the engineers creating machines used CRISPR editing need to know more "analog" aspects of biology but other scientists can focus on the "digital data" perspective. I think a similar digital-vs-analog split happens in computers. Consider the following different physical (analog) realizations of computation devices: - wood gates (https://www.youtube.com/watch?v=GcDshWmhF4A https://www.youtube.com/watch?v=GcDshWmhF4A) - vacuum tubes - silicon wafers of transistors - optical gates using light Can we extract a useful "information data" perspective that transcends all of those vastly different devices? Yes, we call it "computer science". We're discovering that many "computer software" ideas also apply to DNA. Scientists can look at the human genome as 750 megabytes of code and try to "reverse engineer" it. It's like forensic hackers using IDAPro/Ghidra to reverse engineer a malware binary to understand how it works. But that doesn't mean the "analog" can be ignored. Compsci telling us about an algorithm being O(1) vs O(log n) doesn't reveal that vacuum tubes give off more heat and fail more often than silicon transistors. Or the thinness of a wire trace on a circuit board means that deploying a computer in space orbit makes it susceptible to cosmic rays flipping a random bit causing weird errors. All those analog difficulties in the real world doesn't mean that looking at computers via lens of Computer Science is problematic. It's just a different perspective that lets us make progress in that specific area. Every knowledge specialization has inherent limitations. (I notice that my comment supplements dTal's comment: https://news.ycombinator.com/item?id=29649339 https://news.ycombinator.com/item?id=29649339)