
Learn Genetics - zerubeus
https://learn.genetics.utah.edu/
======
svara
Commenters here are noting many ways in which CS and biology are different,
and how computer analogies can break down.

I've got a PhD in biology, and have been into computers all my life. I write
code as a biology researcher every day.

To me, there's a much more practical level to this than that of philosophical
questions on how far analogies take you. Biologists and computer scientists
learn, in their studies and through lots of experience, a different mindset
about how things work.

As a computer scientist, finding a solution to a problem, or predicting how a
system will behave, is ultimately just a question of having a deep knowledge
of the system, plus being a little bit smart about using that knowledge.

As a biologist, having a deep knowledge of what you are dealing with plus
being a little bit smart is just a starting point for formulating hypotheses
that you will then still need to test. Every biologist knows in their gut that
a plausible story is ultimately just that. It's not a proof of anything, just
a starting point.

This runs really deep and can make communication between people who aren't
aware of this difficult. I see this in comments here all the time, where
someone has read up on a little biology, and then goes on to explain that,
therefore, clearly this or that has to be true. Usually that makes me go:
"Yeah, maybe. But what about all these other things you didn't consider? And
what about all those things that literally no one in the world knows you would
need to consider in this particular case?"

Anyhow, I think it's still productive to try to find simple physicsy
explanations in biology. Sometimes it does work, and then you get things like
PCR or gene editing... ;)

~~~
lowdose
Are you willing to consider the opinion that CS people can make a similar
mistake with out of field problems as people with a PhD in Biology make about
CS problems?

~~~
sn9
(DISCLAIMER: The following is based on my own experiences and may not agree
with your own. These are just things I've personally observed to be true. And
of course these are just tendencies rather than absolutes.)

Do you often see biologists without CS backgrounds making those kinds of
mistakes about CS topics that people with CS backgrounds make about biology
topics?

I also have a background in CS and biology, with some math and physics, too.
I've also observed the tendency of people mainly trained in formal sciences
(e.g., CS, math, logic) who's schooling involves a large amount of deriving
things from first principles, to make this mistake (this is also true of more
mathematically inclined physics majors and certain flavors of economics
majors). They often think that what they know is enough to derive a novel
insight from first principles, when the further you get from physics, the less
true that becomes as the nonlinearity and sheer complexity of the world starts
to interfere.

Different areas provide different ways of thinking with different strengths
and weaknesses. They aren't mutually exclusive in the sense that learning one
makes it harder to learn the other, but they require a non-trivial depth of
study to pick up so most people tend to get mentally siloed unless they either
study one of those other fields or somehow pick it up through a more non-
traditional route (which absolutely happens, but is less reliable).

If you want an example of the type of things biologists tend to be weak at,
I'd say quantitative thinking (at least relative to other sciences).
Biologists tend to be the most math-phobic of the natural sciences, so most
have a mental ugh field around most math. You'd be shocked how many grad
students can't do some fairly basic stuff. Many undergraduate programs barely
require calculus, though that's slowly changing, and you'll often get some
exposure to some elementary probability and combinatorics in your introductory
genetics class. And it's shocking how many people with whom I studied
evolution and comparative physiology and anatomy didn't come away with some
degree of probabilistic intuition.

Other fields I've studied to variously minor degrees that train particular
mental habits or develop particular skills and perspectives that I've found
valuable are psychology and the study of cognitive biases, cognitive science,
computer programming and software engineering, chemistry and biochemistry,
literature, economics (both the traditional kind and the more modern
behavioral kind), probability, game theory, history, anthropology and a few
others.

Fields I suspect train other mental habits/skills/etc. that I lack and haven't
yet studied include poetry, martial arts, dance, jazz and/or improv, music
theory and music in general, drawing, deeper dives into the topics I've
already encountered, and probably a ton more that I'm can't remember.

Really the more wide ranging your curiosity is, the more well-rounded you
become. And since most people don't bother leaving their silos (maybe a
handful of others at most), you can after many years start to put together all
sorts of insights that others find non-obvious (though they will still rarely
be novel).

(I think nowadays people might call these "mental models", but learning about
mental models directly through a description in a listicle has always seemed
less useful than studying the fields themselves and indirectly building the
mental model yourself.)

~~~
valarauko
As a bioinformatician, while I largely agree with you, it's worth nothing that
it's not that biologists are math-phobic or bigoted about the value of numbers
- it's that biology has undergone the most radical changes in recent years of
any of the primary sciences. A helpful metric (perhaps apocryphal) that I
heard at a talk is that the total amount of knowledge in the sciences doubles
about every 10 years, in biology every five, and in genetics every two years.
Most biologists working today were trained before the genomics revolution, and
have not developed the mental heuristics around it. In comparison to today,
the genetics of 25 years ago feels practically paleolithic, and it's a
comparison this stark is hard to find in any of the other sciences. Genomics
in the early years was also an unregulated Wild West, with lots of speculative
studies and predictions that never panned out. The field matured rapidly, but
as a consequence experimentalists are vary of "predictions". For example,
despite intense scrutiny, about a third of E. coli genes remain
uncharacterised, and we don't even know if they're really genes, or an
artefact of the gene prediction process. As a bioinformatician, the barrier to
entry for predictions is quite low, and experimentalists are understandably
cautious. It's also a case of moving goalposts - they get used to and begin to
accept computational predictions from one domain, and meanwhile whole new
fields of computational biology have opened up. It'll take a while for them to
accept them.

~~~
lowdose
Sounds like a great time to be alive!

~~~
valarauko
It is! If you look at some of the brand new labs being established by freshly
minted assistant professors, many of these fields didn't even exist 5-10 years
ago. Machine Learning has just begun to percolate into Biology, and we are on
the verge of major shifts in the field.

It's also not just computational biology that's booming. Another of the fields
I follow, population genetics, has been practically rewritten in the past
decade, thanks to improved techniques extracting Ancient DNA. When I started
my PhD, the idea of extracting and assembling the genome of a Neanderthal was
a distant pipe dream. By the time I finished my PhD, we have multiple
Neanderthal genomes from across Eurasia, and the discovery of a previously
unknown human ancestor, sequenced entirely from a single finger bone in a
Siberian cave.

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vikramkr
This looks like a pretty awesome resource. I think it's well worth it to spend
some of your quarantine time on learning the basics of the biology you need to
know to understand what's actually going on with the virus that's the reason
you're in quarantine in the first place. It's not super productive to try and
understand this stuff through analogy to computers and algorithms. Assuming
DNA works just like code amd that cells are biological computers turned out to
be a very poor assumption indeed, as evidenced by how much less we understand
after sequencing the human genome compared to what we thought we would have
known. It's also why the secrets of the coronavirus didnt reveal themselves
immediately after sequencing it's genome. See the epigenetics section for a
bit of an understanding of the layer of complexity that lies on top of DNA,
and let's take a moment to appreciate the biochemists and the protein
biologists unraveling what makes SARS-COV-2 tick.

If you want to really grok genetics and be able to understand and interpret
news and discussion about the field, especially considering how important the
field is in our day to day lives, both with the virus and with
biotech/medicine in general.

~~~
sankha93
I am from a computer science background. I understand basic biology and
genetics. I have been trying to understand what are reasons why code and that
cells are biological computers is a poor assumption. Anecdotal evidence like
the SARS-COV2 you mentioned or things I hear from biologist friends mostly
along "it is not so simple". Are there good studies that shed light on what
are the missing pieces and how can we simulate/model biological processes
better?

~~~
pugio
Okay, imagine Mel, of hacker lore [0] had several billion years to write the
HumanOS program...

The point is that biology is ridiculously, ludicrously, compressed. Reading a
basic biology book introduces you to all of these wonderful and seemingly
complete abstractions: DNA blueprints, RNA messengers, information transfer
into assembly units constructing little protein machines... at least that's
how we wish it would look, and how we abstraction-craving mortals would like
it to go.

But Melvolution is parsimonious - it sees a region of DNA and says "well sure
that section encodes one gene, but if I bump the read head up by one and start
halfway through I can magically read a whole other sequence for this entirely
different task. Oh and that RNA you thought was for message transfer, well
turns out that the right message can cause the thing to fold up and act sort
of like a protein, so let's use that too. And sure this repeated section looks
like uninitialized memory "junk" DNA, but it's too much work to take out, so
let's arbitrarily read from addresses 12, 42, and 107, and stitch that
information into a contiguous unit. Except that every once in a million times
the read head can slip and start reading from location 14 instead of 12... and
that possibility is __important__ because if you take it out the whole system
crashes.

Every possible quirk of chemistry and physics is ruthlessly exploited again
and again and again in a million simultaneous ways. Talk about leaky
abstractions.

(Not to mention that we still can't reproduce the algorithm reality uses to
compute this stuff. It takes a super computer hundreds of hours to simulate a
reasonably okay protein fold (which happens in a cell in a fraction of a
fraction of a second) - and even then we get it wrong most of the time. )

[0] [http://www.catb.org/~esr/jargon/html/story-of-
mel.html](http://www.catb.org/~esr/jargon/html/story-of-mel.html)

~~~
acqq
And the biggest magic that allows for all that complexity to arise is:
everything happens in parallel and at the scales that are beyond any intuition
of humans. We already know that even extremely simple rules can produce
extremely complex-appearing artifacts, like linear congruential generators,
fractals, automata, Conway's game of life.

Now, everything is being generated all the time in all the places, in immense
amounts of cases, and during billions of years. The results are extremely
complex, but those results that we are aware of are only those that survived
all the competition and we also see them only as the aggregates.

Humans have problems even just to imagine the exponential growth, because even
that is beyond our intuition. In nature, a lot of stuff grows exponentially as
long as the resources aren't depleted. That's how the latest pandemics also
started to grow, before we limited the possibilities of spreading by the
physical separation of human carriers.

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Qision
Thanks a lot for this! Could someone recommend me a good book to start
learning cell biology and genetics? I study physics and I'd like to learn
about biophysics.

~~~
AareyBaba
The Molecular Biology of the Cell is the standard text.
[https://www.amazon.com/Molecular-Biology-Sixth-Bruce-
Alberts...](https://www.amazon.com/Molecular-Biology-Sixth-Bruce-
Alberts/dp/0815345240)

But you'll probably benefit from taking an Edx course
[https://www.edx.org/course/introduction-to-biology-the-
secre...](https://www.edx.org/course/introduction-to-biology-the-secret-of-
life-3)

~~~
villedepommes
Do I have to brush up on inorganic and orgranic chemistry in order to get the
most out of this book?

~~~
AareyBaba
You should be able to get through with basic high-school chemisty. You need to
know there are elements C, H, N, O, P, Ca, K, S, know what an ionic bond and
covalent bonds is and be able to look at the structure of a molecule.

~~~
villedepommes
thanks!

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sudokill
Is there are other good resources like this? I am definitely going to read
this.

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Rochus
Great material, thanks for the hint.

Unfortunately some of the animations require Flash or other plugins which are
no longer common.

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cameronbrown
I like this. On a more meta note, does anyone have any generalist advice on
picking up the basics of a new field? For instance, I'm looking into hobbyist
electronics, but it's hard to know where to start.

~~~
chrisco255
[https://hackaday.com/](https://hackaday.com/) is a great community for
learning hobby electronics.

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yters
People say DNA is not like computer code. However, it seems that people are
really saying it is not like human written computer code. Otherwise, it is
still a digital code that is turing complete.

