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Comment by mannykannot | Hacker News Reader

> Watson and Crick were not math savants. They did not have natural gifts.

They had the 'natural gift' of picking up on the clues hinting that DNA, rather than protein, was central to the mechanism of biological inheritance. It is the natural gift of being able to identify the key features of the forest despite the preponderance of obfuscating trunks and branches. It is the gift of seeing some sort of order behind cirucmstances in which almost all the details are accidental.

Math synthesizes complexity; in biology, you have to analyze what's given.

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>They had the 'natural gift' of picking up on the clues hinting that DNA, rather than protein, was central to the mechanism of biological inheritance.

That was Hershey and Chase [0]. Watson and Crick were the first to solve the structure of DNA.

[0] https://en.wikipedia.org/wiki/Hershey%E2%80%93Chase_experime...

Point taken, though that article also says "Hershey and Chase concluded that protein was not likely to be the hereditary genetic material. However, they did not make any conclusions regarding the specific function of DNA as hereditary material, and only said that it must have some undefined role."

Arguably, my general point still holds, though with an expanded cast of characters.

my observations which I think broadly agrees.

-Math/CS: inherently better at abstracting & are "proof" centric.

-Life Sciences: inherently better at observing then reasoning and are "argument" centric.

Coming from the CS side this is not a marriage made in heaven as opinions don't matter by design.

As a Math/CS graduate (I focused on mathy aspects of CS): math cs people struggle at working with real world abstractions. They abstract and simplify, but then tend to convince themselves that simplified model and its conclusions are totally all there is to reality.

And if they accept that model is not super perfect, they completely reject model - to find another simplified model that they convince themselves if flawless.

They struggle with the "this is model with such and flaws we need to keep in mind while interpreting".

I'm also from the mathy aspects of CS. To avoid ticking off a local majority I will simply agree that there are technical practitioners for whom accepting the that life science and data it produces are at best "a system of exceptions" is difficult.

And that this lack of flexibility can create hard times when they attempt to fit biology into a banking app because that is what they learned programming is.

On the converse side life sciences may feel everything is debatable and that having a strong personality should mater as much as an absolute mathematical proof.

Schrödinger published a very interesting treatise (What is Life - http://www.whatislife.ie/downloads/What-is-Life.pdf) which inspired a generation of physicists to study biology. Biology is still mostly at the early observational phase (similar to physics in the early Renaissance period), although slowly more systems-level approaches are being applied to be able to model biology with the same types of tools (namely mathematics) that have been so successful in physics.

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