> The course was so much about WHAT and never about WHY.
Biology is pretty notorious for most stuff being arbitrary. There is no model. People who say there's a model, they're talking about something else - sometimes, a literal Platonic ideal of biology, that makes for an appealing pop science course, but not knowledge that like, helps you design experiments or better use drugs or whatever.
From the article:
> It was hard to get through a sentence without having to consult Wikipedia. In immunology in particular the nomenclature is expansive.
This is a common refrain. "Paternal allo-antigens" for example, straight from the literature: people who do immunology research know what that means, they do not get tripped up like this author does.
Programming computers and being good at math does not prepare you at all for understanding this stuff! Watson and Crick were not math savants. They did not have natural gifts.
> 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.
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.
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.
> Programming computers and being good at math does not prepare you at all for understanding this stuff!
This is something that lots of people, and especially people in the tech industry, keep forgetting. They go ''well i can calculate partial differential equations, did a course on topology ánd created an api based on open data, how hard can this 'biology' actually be???'' and then it turns out that yes, it is hard.
Only in HN a comment like this is not laughed out of the room.
Biology is packed with useful, relevant models, from the lock and key model for enzymes in one extreme to the Lotka-Volterra (predator-prey) for ecosystems population in the other, there are plenty of models who are A) Theoretically sound. B)Has great quantitative explanatory power.
> Watson and Crick were not math savants. They did not have natural gifts.
You have a very quaint and naive view of biology. Since the work of Maynard-Smith at the very least, has rigorous mathematics taken an important role in biology. Population genetics,neuroscience, systems biology, biochemistry among others are underpinned by lots of math. People working deciphering all the signaling pathways in the cell are working with what is effectively a complicated,messy but fascinating discretely-encoding program.
>Programming computers and being good at math does not prepare you at all for understanding this stuff! Watson and Crick were not math savants. They did not have natural gifts.
Out of all the hn chauvinism I've read over the years this post is a strong contender for the most absurdly delusional. Sorry, being able to program does not make you an ubermensch.
Biology is pretty notorious for most stuff being arbitrary. There is no model. People who say there's a model, they're talking about something else - sometimes, a literal Platonic ideal of biology, that makes for an appealing pop science course, but not knowledge that like, helps you design experiments or better use drugs or whatever.
From the article:
> It was hard to get through a sentence without having to consult Wikipedia. In immunology in particular the nomenclature is expansive.
This is a common refrain. "Paternal allo-antigens" for example, straight from the literature: people who do immunology research know what that means, they do not get tripped up like this author does.
Programming computers and being good at math does not prepare you at all for understanding this stuff! Watson and Crick were not math savants. They did not have natural gifts.