11 ms·

DNA seen through the eyes of a coder (2017)

326 points by sundarurfriend 9y ago

fjfaase 9y ago
I think that his talks on SHA2017 were the best. Not just with respect to the contents but also for the manner he presented them. A definite watch for anyone who wants to know a little more about DNA.
tw1010 9y ago
More like (2002)
- WhitneyLand 9y ago
  What’s wrong with it? Seems like a fantastic micro introduction to DNA for those with a computer science background. Of course it’s not perfect. I’d bet most readers like myself were enjoying it, while thinking of analogies that might be more apt or illustrative. But the power of it is how quickly it can establish a frame of reference, and if desired a jump off point for further understanding. If there are parts so fundamentally flawed or outdated as to negate the value, please, point them out. Of if you know of a better article that illuminates as much given only a 10 minute time investment, by all means please share it.
  - austinprete 9y ago
    I took his comment to reference the fact that this was originally written in 2002 with minor additions over time, as evidenced by the “Updates” section.
    - tw1010 9y ago
      This was the intent of my comment.
- tantalor 9y ago
  Yep Wayback Machine places origin in June 2002. https://web.archive.org/web/20020601182120/https://ds9a.nl/amazing-dna/ https://web.archive.org/web/20020601182120/https://ds9a.nl/a...
- sundarurfriend 9y ago
  Yeah, it was written in 2002, and it's unclear if the content was updated after that, so I'd originally left the year unspecified in title. By the way, the author apparently did a talk on this in Aug 2017, in the presence of biologists, with some updates about new tech like CRISPR. I submitted a link to the blog post about this[1] yesterday, but it didn't gain much traction then (probably due to bad title: "DNA: The Code of Life" just sounds like some basic biology piece). [1] https://medium.com/@bert.hubert/dna-the-code-of-life-12db4a17c66d https://medium.com/@bert.hubert/dna-the-code-of-life-12db4a1...
colemannugent 9y ago
Definitely an interesting take on the "coding" of DNA. Something that has always interested me is similarities in natural and unnatural mechanisms, like how modern cameras mimic eyes right down to the tiny motors that drive the auto-focus mechanisms. The long sequence of nucleotides reminds me of a strange sort of Turing machine capable of building humans.
- dekhn 9y ago
  the big difference between modern cameras and eyes is that a huge amount of neural processing is done in the retina of the eye. There aren't any cameras (that I know of) that compute such sophisticaed levels of processing (edge detecting, contrast detecting, motion detecting) within the sensor package.
  - kevin_thibedeau 9y ago
    There are CMOS sensors with in-array edge detection. More flexibly, all optical mice have integrated sensors that do motion processing in the same package if not the same die.
bitwize 9y ago
Since this was written, we've discovered CRISPR, which started off as a bacterial analogue to antivirus software that works against actual physical viruses.
- sundarurfriend 9y ago
  The author did a talk on this with updated content, apparently including "A little bit on CRISPR". Blog post with links to the talk videos: https://medium.com/@bert.hubert/dna-the-code-of-life-12db4a17c66d https://medium.com/@bert.hubert/dna-the-code-of-life-12db4a1...
searine 9y ago
This is a fun parallel, but I feel like it fails to go the other direction. How is DNA different from programming? This is important to understand because those differences are the foundation of our intuition about how DNA operates. We can't let ourselves fall into the misunderstanding that cells are like computers. In particular, the ideas of random mutation and populations are inherently different from software. Imagine a piece of code whose bits slowly decay over time. Where functions compete with functions in every other program on the filesystem to see who is most efficient. Where scripts need to constantly copy themselves to other folders simply to maintain their integrity. Its this kind of stochastic and unreliable environment that I think a lot of people forget about when talking about DNA. Yes it is a consistent heritable genetic library, but it is also in a constant state of change. Genes are strange little islands in a sea of noise.
- agumonkey 9y ago
  It's not different, but so far no one knows the interpreter semantics.
- Robotbeat 9y ago
  > Imagine... Where functions compete with functions in every other program on the filesystem to see who is most efficient. Where scripts need to constantly copy themselves to other folders simply to maintain their integrity. That sounds a lot like computer viruses.
  - tzahola 9y ago
    Good analogy! Funny, but in fact there’s even a whole class of self-replicating biomachines named after computer viruses! https://en.m.wikipedia.org/wiki/Introduction_to_viruses https://en.m.wikipedia.org/wiki/Introduction_to_viruses The similarities are eerie.
- jfarlow 9y ago
  Adding to that conceptual challenge is separating in time the various components of the regulation and manipulation in the above analogies. Low-level programmers have an intuition of the 'time-cost' value of various hardware calls - the various caches, RAM, HD, network, etc. and how they can be many orders of magnitudes different even if the API considers them identically. Life goes the other way and requires you not only to integrate over nanoseconds, but also over petaseconds. And at most scales in between there are mechanisms of regulation and operation.
jfarlow 9y ago
As someone building software to design genetic tools, this is actually a pretty good overview. It's analogies are pretty solid, even if they are just analogies. It's a well-described snapshot of the early phase of education where this helps build a grand intuition and relations between disciplines. Just be careful to remember that they're just analogies, and in general can't be relied on to actually discover, invent, design or conclude. The fun part for everyone here is that the science has improved so far so quickly that at this point that we are starting to be able to improve, implement and otherwise make good on those coding-style capabilities. We can start to write, patch, update, and affect that code - and do so in intelligent and rational ways, rather than the screens set up in the past century. The next section I would add would be one that actually started to talk about the compiled programs - the protein's themselves. All that code, all those rules, all those heuristics are there to be run through an atomic 3D-printer to produce proteins. And in an ensemble, those proteins form a closed community within a cell, which then form communities in a tissue, which form in communities called an organism. Those compiled proteins actually do the work at the lowest levels but often get a bit of short shrift while we all stare at their source-code in amazement. The source is elegant, but the programs are even more amazing, in my humble opinion. Here are some 'profiles' of the kinds of economically and socially interesting programs we've started to make based on the above source-code. An 'app-store' of compiled apps if you will: https://serotiny.bio/notes/proteins/ https://serotiny.bio/notes/proteins/
- westoncb 9y ago
  > The source is elegant, but the programs are even more amazing, in my humble opinion. Makes me curious about the 'runtime' which makes their impressive execution possible—granting that the analogy has definite limitations and could be misleading. The aspect of a 'runtime' which I see carrying over is that once the 'source' is 'compiled' into proteins, something makes the interesting behavior of those proteins what it is. I think where the analogy could be misleading is that the architecture of programming language runtimes tends to be a single coherent system with definite separation from programs executing within; whereas in the case of proteins, a large part of what provides for their biologically interesting behavior is just their general chemical properties (which are sort of contained within the 'programs' themselves, rather than in a separate external system). But there are probably aspects of the environment into which the proteins are released that systematically influence their behavior in a way that could be abstracted and studied (at which point it might have some loose resemblance in role to language runtimes). Maybe?
tzahola 9y ago
Ah, the good ol’ ”imagine gravity like aaaa... sheet of rubber”. Except this time for biology!
- dandare 9y ago
  So you prefer the "imagine gravity like aaaa curvature of space and time"? We explain our science in analogies. We think in analogies. And DNA like a programming language is a useful analogy.
  - chrisamiller 9y ago
    It's definitely a good starting place for people from a CS background, but if you really want to learn biology, don't get too attached! As you get deeper, you'll quickly discover the limitations of that analogy.
- coldtea 9y ago
  Nope, actually a very good analogy, which is miles better than the gravity as a sheet.
ozy 9y ago
I might have missed it, but key is that DNA is not executed linearly. Instead all instructions are executed all the time. Much like the difference in normal programming and programming an FPGA, but then probabilistic.
- dsnuh 9y ago
  >Furthermore, 97% of your DNA is commented out. DNA is linear and read from start to end. Looks like he is saying the opposite?
  - Obi_Juan_Kenobi 9y ago
    That statement is only true in a very narrow context. Much of your DNA is silenced by being formed into dense chromatin - DNA that is tightly packed onto a protein scaffold. However, the regions that are silenced change throughout development, and these regions are not strictly inaccessible. This is seen with CNSes, conserved non-coding sequences. As the name implies, these are not traditional genes that make protein products, but are usually small regulatory sequences that often affect chromatin state, and sometimes at a great distance. I've had colleagues attempt for years to identify a mutation, only to find it that the causative change occurred many kilobases away from the relevant gene. This is partly the reason why DNA is not 'plug and play', as the genomic context of a particular sequence often matters. Which is why DNA isn't really linear, at least on a genomic scale. The processes of transcription and translation (DNA -> RNA -> Protein) are linear to be sure, but the regulatory networks that determine gene expression are happening all at once on a massively parallel scale. These molecules are also 3-dimensional and can fold back on themselves and cause modifications. Essentially every fundamental process in gene expression is able to be regulated, whether it's enhancers and repressors affecting transcription, RNA-mediated silencing, RNA splicing, RNA modifications and stability, histone modifications, protein modifications, protein stability, phosphylation or any of the dozens of other post-translational regulation, and on and on and on.. And there are thousands of genes which can impact all manner of other genes at any of those levels, either directly or otherwise. It's a wonder that we're able to tease anything out of it that makes sense. Generally speaking, I caution against computer analogies for biology. Biology is messy! It rarely works how you want it to. Even relatively common and basic techniques in molecular biology require a great deal of troubleshooting, and even in the best labs with loads of experience, sometimes things simply refuse to work how you'd like them to. You don't hear too much about synthetic biology anymore (it's still going, just less hype) because the premise was incredibly naive; you were never going to get bits and pieces of DNA to behave in a predictable manner. Just about everyone with wet lab experience suspected this. For context, the current hype about CRISPR is almost entirely based on how well it works, not what it does. We've had a few different techniques for modifying DNA for a while now, but none of them worked quite well enough to practically accomplish all the interesting things that CRISPR is now enabling.
Maultasche 9y ago
Interesting, that was a really useful analogy. So what I learned is that our body is like an Actor model with lots of asynchronous processes sending messages to each other. I'm sure that's a leaky analogy, but it makes things simpler in my mind. I have the sudden desire to go emulate it with Elixir.
dandare 9y ago
This is amazing and I was looking for such analysis for a long time. I only wish it included more information about the actual programing language of DNA. >Now, DNA is not like a computer programming language. It really isn't. Maybe it would be interesting to talk about how it differs and what programming paradigms do not have analogies in the genetic code.
tehsauce 9y ago
A very important aspect of DNA as code the heavy use of macros. The code itself heavily influences the expression of other parts of the code.
- scalio 9y ago
  How so?
tvelichkov 9y ago
Great article, so no unit testing? I knew it!
bmsran 9y ago
"Is here. This not a joke. We can wonder about the license though. Maybe we should ask the walking product of this source: Craig Venter." The reference build of the human genome (provided here by Ensembl) is almost entirely derived from the public human genome sequencing project, not the private project led by Venter.
- chrisamiller 9y ago
  And the public reference genome is about 2/3 from a single African American individual from Buffalo, New York!
Razengan 9y ago
As a layman, I’ve always wondered about something but I’m not sure how exactly to ask/frame the question: How did biological “software” “evolve?” As in, the basic features that are common to many macroscopic lifeforms, like knowing one’s position within 3D space, as well as the related-but-distinct sense of proprioception [0]. https://en.wikipedia.org/wiki/Proprioception https://en.wikipedia.org/wiki/Proprioception
- chrisamiller 9y ago
  The short answer is that it was useful! The parameter space of things that have been tried in uncountable organisms over _billions_ of years is vast. And proprioception is incredibly useful, even in small doses. Imagine you're a single-celled organism: At the simplest level, some vague sensory input about your surroundings so that you can avoid the predator/find the food gives you a massive advantage over other organisms that can't. Every random mutation that improves this ability, even a tiny bit, is rewarded, as you can grow faster/die less and ultimately, reproduce more. (this is what we call "fitness" - how many of your genes get passed on). If you get a mutation that hurts this ability, you're going to be massively penalized - eat less, grow slower, reproduce less. In pretty short order, the organisms with higher fitness are going to take over. Mutations occur all the time. In humans, the rate is very low - about 3 new mutations during every cell division. In many bacteria, the fidelity rate is much lower, and the reproduction rate is much higher, and so they are _constantly_ exploring new combinations of parameters. Most are neutral, many are disasterous, a few give a slight advantage. Pile up these small advantages over millions or billions of years, and gradually, in fits and starts, very complex abilities evolve.
deleted 9y ago
[deleted]
tritium 9y ago
What happens if you copy paste the 'legs selector' part of a mouse HOX gene into the fruitfly Homeobox: 'In fact, when the mouse Hox-B6 gene is inserted in Drosophila, it can substitute for Antennapedia and produce legs in place of antennae' Mother of God, is that ever strange.
rumcajz 9y ago
On similar topic: http://250bpm.com/blog:89 http://250bpm.com/blog:89
callesgg 9y ago
From what I know genes don’t exist as distinct parts of the genome, genes are just a way that we humans grouped the DNA. 97% is not junk is is just not coding for pure proteines. The machinery can jump in to “commented” genes. We don’t know exactly how non protein coding dna works to the lowest level. Allot of it is probably useless but you probably can’t remove it without destroying stuff. Compared to a software project the DNA code has the worst code quality you will ever see in a working system. Almost everything is dependent on everything else. Is is essentially a software project made by a toddler cutting and pasting assembler code during millions of years.
- ajuc 9y ago
  Saying non-coding DNA is junk is like saying non-printf code is junk :)
- TeMPOraL 9y ago
  > Almost everything is dependent on everything else. Is is essentially a software project made by a toddler cutting and pasting assembler code during millions of years. I prefer to see it as code written by demoscene genius hackers - hyperoptimized, "almost everything is dependent on everything else" - the same opcode may have three different purposes in a program, plus being data to some other code. Still, I feel we're missing some important part of the picture - our current descriptions make living systems seem much more fragile than they really are. Maybe there's a new model / structural abstraction waiting to be discovered, that would make DNA machinery seem less ad-hoc?
- thriftwy 9y ago
  Calling it junk didn't make sense to me 10 years ago, and today it is plain false. DNA before protein sequence is used for binding modifiers. This means that every protein has a huge if() { before them, and many kinds of different stuff can bind there in order to either suppress production of this protein or increase it. This is how all this stuff work. How would it work otherwise, I always wondered. Will all proteins in DNA be produced at the same rate, as naive models will imply? It turns out, they aren't, and this is regulated by areas in 'junk' DNA. Complaining about junk in DNA is like complaining about dispatch in computer program. All kinds of ifs and whiles and fors. Everybody knows our programs are 97% dispatch and 3% business logic computations after all. Or worse.
DaniFong 9y ago
BlazzzapP I just took over your internet.
teekert 9y ago
Very nice piece, as a biologist turned programmer I think it nicely sums things up. Some remarks though... "Similarly, as an embryo develops in the mother's womb, its DNA is edited substantially to reduce its growth rate, and the size of the placenta. In such a way, the competing interests of the father ('large strong children') and the mother ('survive pregnancy') are balanced. Such 'imprinting' can only happen within the mother, since the father's genome doesn't know anything about the size of the mother." This is strangely worded, I think there is indeed a trade-off between size of the child and the mother surviving but the father also benefits from the mother surviving (better have breast milk available and love and care, right? Father genome?). Moreover, the fathers' genome is half inherited from a woman and is mingled with a woman's DNA every generation (most of the genome is not specifically male of female). I don't believe there is evidence that the Y chromosome solely drives this push for a larger child in the uterus, but please correct me if I'm wrong. I also think it is theoretically possible to produce a female child from two fathers by merging their genomes and supplying the child with two X chromosomes and no Y chromosome. So the statement that "the father's genome doesn't know anything about the size of the mother" is also wrong (for the used definition of "knowing"), the male genome "knows" just as much as the mother.
- ajuc 9y ago
  > So the statement that "the father's genome doesn't know anything about the size of the mother" is also wrong (for the used definition of "knowing"), the male genome "knows" just as much as the mother. It knows something about possible future female child, but not about that particular mother that will have to give a birth?
visarga 9y ago
You know what blew my mind about DNA? The "Gene Regulatory Networks". https://en.wikipedia.org/wiki/Gene_regulatory_network https://en.wikipedia.org/wiki/Gene_regulatory_network They work analogically like neural networks, where genes act as signals to other genes in a complex graph of dependency. Isn't that amazing? Each cell has a "gene based brain" (an RNN) which can do computation - it can map states to actions. Each neuron in the brain is actually a whole neural network. GRN's are probably the most energy efficient neural nets that exist in the universe, and they're also self replicators.
ajuc 9y ago
My internal model of DNA programming: - make 1 million sed scripts acting on directory X - put all of them in directory X - run all of them in parallel in infinite loops

I think that his talks on SHA2017 were the best. Not just with respect to the contents but also for the manner he presented them. A definite watch for anyone who wants to know a little more about DNA.

tw1010 9y ago

More like (2002)

WhitneyLand 9y ago
What’s wrong with it? Seems like a fantastic micro introduction to DNA for those with a computer science background. Of course it’s not perfect. I’d bet most readers like myself were enjoying it, while thinking of analogies that might be more apt or illustrative. But the power of it is how quickly it can establish a frame of reference, and if desired a jump off point for further understanding. If there are parts so fundamentally flawed or outdated as to negate the value, please, point them out. Of if you know of a better article that illuminates as much given only a 10 minute time investment, by all means please share it.
- austinprete 9y ago
  I took his comment to reference the fact that this was originally written in 2002 with minor additions over time, as evidenced by the “Updates” section.
  - tw1010 9y ago
    This was the intent of my comment.
tantalor 9y ago
Yep Wayback Machine places origin in June 2002. https://web.archive.org/web/20020601182120/https://ds9a.nl/amazing-dna/ https://web.archive.org/web/20020601182120/https://ds9a.nl/a...
sundarurfriend 9y ago
Yeah, it was written in 2002, and it's unclear if the content was updated after that, so I'd originally left the year unspecified in title. By the way, the author apparently did a talk on this in Aug 2017, in the presence of biologists, with some updates about new tech like CRISPR. I submitted a link to the blog post about this[1] yesterday, but it didn't gain much traction then (probably due to bad title: "DNA: The Code of Life" just sounds like some basic biology piece). [1] https://medium.com/@bert.hubert/dna-the-code-of-life-12db4a17c66d https://medium.com/@bert.hubert/dna-the-code-of-life-12db4a1...

colemannugent 9y ago

Definitely an interesting take on the "coding" of DNA. Something that has always interested me is similarities in natural and unnatural mechanisms, like how modern cameras mimic eyes right down to the tiny motors that drive the auto-focus mechanisms. The long sequence of nucleotides reminds me of a strange sort of Turing machine capable of building humans.

dekhn 9y ago
the big difference between modern cameras and eyes is that a huge amount of neural processing is done in the retina of the eye. There aren't any cameras (that I know of) that compute such sophisticaed levels of processing (edge detecting, contrast detecting, motion detecting) within the sensor package.
- kevin_thibedeau 9y ago
  There are CMOS sensors with in-array edge detection. More flexibly, all optical mice have integrated sensors that do motion processing in the same package if not the same die.

bitwize 9y ago

Since this was written, we've discovered CRISPR, which started off as a bacterial analogue to antivirus software that works against actual physical viruses.

sundarurfriend 9y ago
The author did a talk on this with updated content, apparently including "A little bit on CRISPR". Blog post with links to the talk videos: https://medium.com/@bert.hubert/dna-the-code-of-life-12db4a17c66d https://medium.com/@bert.hubert/dna-the-code-of-life-12db4a1...

searine 9y ago

This is a fun parallel, but I feel like it fails to go the other direction. How is DNA different from programming? This is important to understand because those differences are the foundation of our intuition about how DNA operates. We can't let ourselves fall into the misunderstanding that cells are like computers. In particular, the ideas of random mutation and populations are inherently different from software. Imagine a piece of code whose bits slowly decay over time. Where functions compete with functions in every other program on the filesystem to see who is most efficient. Where scripts need to constantly copy themselves to other folders simply to maintain their integrity. Its this kind of stochastic and unreliable environment that I think a lot of people forget about when talking about DNA. Yes it is a consistent heritable genetic library, but it is also in a constant state of change. Genes are strange little islands in a sea of noise.

agumonkey 9y ago
It's not different, but so far no one knows the interpreter semantics.
Robotbeat 9y ago
> Imagine... Where functions compete with functions in every other program on the filesystem to see who is most efficient. Where scripts need to constantly copy themselves to other folders simply to maintain their integrity. That sounds a lot like computer viruses.
- tzahola 9y ago
  Good analogy! Funny, but in fact there’s even a whole class of self-replicating biomachines named after computer viruses! https://en.m.wikipedia.org/wiki/Introduction_to_viruses https://en.m.wikipedia.org/wiki/Introduction_to_viruses The similarities are eerie.
jfarlow 9y ago
Adding to that conceptual challenge is separating in time the various components of the regulation and manipulation in the above analogies. Low-level programmers have an intuition of the 'time-cost' value of various hardware calls - the various caches, RAM, HD, network, etc. and how they can be many orders of magnitudes different even if the API considers them identically. Life goes the other way and requires you not only to integrate over nanoseconds, but also over petaseconds. And at most scales in between there are mechanisms of regulation and operation.

jfarlow 9y ago

As someone building software to design genetic tools, this is actually a pretty good overview. It's analogies are pretty solid, even if they are just analogies. It's a well-described snapshot of the early phase of education where this helps build a grand intuition and relations between disciplines. Just be careful to remember that they're just analogies, and in general can't be relied on to actually discover, invent, design or conclude. The fun part for everyone here is that the science has improved so far so quickly that at this point that we are starting to be able to improve, implement and otherwise make good on those coding-style capabilities. We can start to write, patch, update, and affect that code - and do so in intelligent and rational ways, rather than the screens set up in the past century. The next section I would add would be one that actually started to talk about the compiled programs - the protein's themselves. All that code, all those rules, all those heuristics are there to be run through an atomic 3D-printer to produce proteins. And in an ensemble, those proteins form a closed community within a cell, which then form communities in a tissue, which form in communities called an organism. Those compiled proteins actually do the work at the lowest levels but often get a bit of short shrift while we all stare at their source-code in amazement. The source is elegant, but the programs are even more amazing, in my humble opinion. Here are some 'profiles' of the kinds of economically and socially interesting programs we've started to make based on the above source-code. An 'app-store' of compiled apps if you will: https://serotiny.bio/notes/proteins/ https://serotiny.bio/notes/proteins/

westoncb 9y ago
> The source is elegant, but the programs are even more amazing, in my humble opinion. Makes me curious about the 'runtime' which makes their impressive execution possible—granting that the analogy has definite limitations and could be misleading. The aspect of a 'runtime' which I see carrying over is that once the 'source' is 'compiled' into proteins, something makes the interesting behavior of those proteins what it is. I think where the analogy could be misleading is that the architecture of programming language runtimes tends to be a single coherent system with definite separation from programs executing within; whereas in the case of proteins, a large part of what provides for their biologically interesting behavior is just their general chemical properties (which are sort of contained within the 'programs' themselves, rather than in a separate external system). But there are probably aspects of the environment into which the proteins are released that systematically influence their behavior in a way that could be abstracted and studied (at which point it might have some loose resemblance in role to language runtimes). Maybe?

tzahola 9y ago

Ah, the good ol’ ”imagine gravity like aaaa... sheet of rubber”. Except this time for biology!

dandare 9y ago
So you prefer the "imagine gravity like aaaa curvature of space and time"? We explain our science in analogies. We think in analogies. And DNA like a programming language is a useful analogy.
- chrisamiller 9y ago
  It's definitely a good starting place for people from a CS background, but if you really want to learn biology, don't get too attached! As you get deeper, you'll quickly discover the limitations of that analogy.
coldtea 9y ago
Nope, actually a very good analogy, which is miles better than the gravity as a sheet.

ozy 9y ago

I might have missed it, but key is that DNA is not executed linearly. Instead all instructions are executed all the time. Much like the difference in normal programming and programming an FPGA, but then probabilistic.

dsnuh 9y ago
>Furthermore, 97% of your DNA is commented out. DNA is linear and read from start to end. Looks like he is saying the opposite?
- Obi_Juan_Kenobi 9y ago
  That statement is only true in a very narrow context. Much of your DNA is silenced by being formed into dense chromatin - DNA that is tightly packed onto a protein scaffold. However, the regions that are silenced change throughout development, and these regions are not strictly inaccessible. This is seen with CNSes, conserved non-coding sequences. As the name implies, these are not traditional genes that make protein products, but are usually small regulatory sequences that often affect chromatin state, and sometimes at a great distance. I've had colleagues attempt for years to identify a mutation, only to find it that the causative change occurred many kilobases away from the relevant gene. This is partly the reason why DNA is not 'plug and play', as the genomic context of a particular sequence often matters. Which is why DNA isn't really linear, at least on a genomic scale. The processes of transcription and translation (DNA -> RNA -> Protein) are linear to be sure, but the regulatory networks that determine gene expression are happening all at once on a massively parallel scale. These molecules are also 3-dimensional and can fold back on themselves and cause modifications. Essentially every fundamental process in gene expression is able to be regulated, whether it's enhancers and repressors affecting transcription, RNA-mediated silencing, RNA splicing, RNA modifications and stability, histone modifications, protein modifications, protein stability, phosphylation or any of the dozens of other post-translational regulation, and on and on and on.. And there are thousands of genes which can impact all manner of other genes at any of those levels, either directly or otherwise. It's a wonder that we're able to tease anything out of it that makes sense. Generally speaking, I caution against computer analogies for biology. Biology is messy! It rarely works how you want it to. Even relatively common and basic techniques in molecular biology require a great deal of troubleshooting, and even in the best labs with loads of experience, sometimes things simply refuse to work how you'd like them to. You don't hear too much about synthetic biology anymore (it's still going, just less hype) because the premise was incredibly naive; you were never going to get bits and pieces of DNA to behave in a predictable manner. Just about everyone with wet lab experience suspected this. For context, the current hype about CRISPR is almost entirely based on how well it works, not what it does. We've had a few different techniques for modifying DNA for a while now, but none of them worked quite well enough to practically accomplish all the interesting things that CRISPR is now enabling.

Maultasche 9y ago

Interesting, that was a really useful analogy. So what I learned is that our body is like an Actor model with lots of asynchronous processes sending messages to each other. I'm sure that's a leaky analogy, but it makes things simpler in my mind. I have the sudden desire to go emulate it with Elixir.

dandare 9y ago

This is amazing and I was looking for such analysis for a long time. I only wish it included more information about the actual programing language of DNA. >Now, DNA is not like a computer programming language. It really isn't. Maybe it would be interesting to talk about how it differs and what programming paradigms do not have analogies in the genetic code.

tehsauce 9y ago

A very important aspect of DNA as code the heavy use of macros. The code itself heavily influences the expression of other parts of the code.

scalio 9y ago
How so?

tvelichkov 9y ago

Great article, so no unit testing? I knew it!

bmsran 9y ago

"Is here. This not a joke. We can wonder about the license though. Maybe we should ask the walking product of this source: Craig Venter." The reference build of the human genome (provided here by Ensembl) is almost entirely derived from the public human genome sequencing project, not the private project led by Venter.

chrisamiller 9y ago
And the public reference genome is about 2/3 from a single African American individual from Buffalo, New York!

Razengan 9y ago

As a layman, I’ve always wondered about something but I’m not sure how exactly to ask/frame the question: How did biological “software” “evolve?” As in, the basic features that are common to many macroscopic lifeforms, like knowing one’s position within 3D space, as well as the related-but-distinct sense of proprioception [0]. https://en.wikipedia.org/wiki/Proprioception https://en.wikipedia.org/wiki/Proprioception

chrisamiller 9y ago
The short answer is that it was useful! The parameter space of things that have been tried in uncountable organisms over _billions_ of years is vast. And proprioception is incredibly useful, even in small doses. Imagine you're a single-celled organism: At the simplest level, some vague sensory input about your surroundings so that you can avoid the predator/find the food gives you a massive advantage over other organisms that can't. Every random mutation that improves this ability, even a tiny bit, is rewarded, as you can grow faster/die less and ultimately, reproduce more. (this is what we call "fitness" - how many of your genes get passed on). If you get a mutation that hurts this ability, you're going to be massively penalized - eat less, grow slower, reproduce less. In pretty short order, the organisms with higher fitness are going to take over. Mutations occur all the time. In humans, the rate is very low - about 3 new mutations during every cell division. In many bacteria, the fidelity rate is much lower, and the reproduction rate is much higher, and so they are _constantly_ exploring new combinations of parameters. Most are neutral, many are disasterous, a few give a slight advantage. Pile up these small advantages over millions or billions of years, and gradually, in fits and starts, very complex abilities evolve.

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tritium 9y ago

What happens if you copy paste the 'legs selector' part of a mouse HOX gene into the fruitfly Homeobox: 'In fact, when the mouse Hox-B6 gene is inserted in Drosophila, it can substitute for Antennapedia and produce legs in place of antennae' Mother of God, is that ever strange.

rumcajz 9y ago

On similar topic: http://250bpm.com/blog:89 http://250bpm.com/blog:89

callesgg 9y ago

From what I know genes don’t exist as distinct parts of the genome, genes are just a way that we humans grouped the DNA. 97% is not junk is is just not coding for pure proteines. The machinery can jump in to “commented” genes. We don’t know exactly how non protein coding dna works to the lowest level. Allot of it is probably useless but you probably can’t remove it without destroying stuff. Compared to a software project the DNA code has the worst code quality you will ever see in a working system. Almost everything is dependent on everything else. Is is essentially a software project made by a toddler cutting and pasting assembler code during millions of years.

ajuc 9y ago
Saying non-coding DNA is junk is like saying non-printf code is junk :)
TeMPOraL 9y ago
> Almost everything is dependent on everything else. Is is essentially a software project made by a toddler cutting and pasting assembler code during millions of years. I prefer to see it as code written by demoscene genius hackers - hyperoptimized, "almost everything is dependent on everything else" - the same opcode may have three different purposes in a program, plus being data to some other code. Still, I feel we're missing some important part of the picture - our current descriptions make living systems seem much more fragile than they really are. Maybe there's a new model / structural abstraction waiting to be discovered, that would make DNA machinery seem less ad-hoc?
thriftwy 9y ago
Calling it junk didn't make sense to me 10 years ago, and today it is plain false. DNA before protein sequence is used for binding modifiers. This means that every protein has a huge if() { before them, and many kinds of different stuff can bind there in order to either suppress production of this protein or increase it. This is how all this stuff work. How would it work otherwise, I always wondered. Will all proteins in DNA be produced at the same rate, as naive models will imply? It turns out, they aren't, and this is regulated by areas in 'junk' DNA. Complaining about junk in DNA is like complaining about dispatch in computer program. All kinds of ifs and whiles and fors. Everybody knows our programs are 97% dispatch and 3% business logic computations after all. Or worse.

DaniFong 9y ago

BlazzzapP I just took over your internet.

teekert 9y ago

Very nice piece, as a biologist turned programmer I think it nicely sums things up. Some remarks though... "Similarly, as an embryo develops in the mother's womb, its DNA is edited substantially to reduce its growth rate, and the size of the placenta. In such a way, the competing interests of the father ('large strong children') and the mother ('survive pregnancy') are balanced. Such 'imprinting' can only happen within the mother, since the father's genome doesn't know anything about the size of the mother." This is strangely worded, I think there is indeed a trade-off between size of the child and the mother surviving but the father also benefits from the mother surviving (better have breast milk available and love and care, right? Father genome?). Moreover, the fathers' genome is half inherited from a woman and is mingled with a woman's DNA every generation (most of the genome is not specifically male of female). I don't believe there is evidence that the Y chromosome solely drives this push for a larger child in the uterus, but please correct me if I'm wrong. I also think it is theoretically possible to produce a female child from two fathers by merging their genomes and supplying the child with two X chromosomes and no Y chromosome. So the statement that "the father's genome doesn't know anything about the size of the mother" is also wrong (for the used definition of "knowing"), the male genome "knows" just as much as the mother.

ajuc 9y ago
> So the statement that "the father's genome doesn't know anything about the size of the mother" is also wrong (for the used definition of "knowing"), the male genome "knows" just as much as the mother. It knows something about possible future female child, but not about that particular mother that will have to give a birth?

visarga 9y ago

You know what blew my mind about DNA? The "Gene Regulatory Networks". https://en.wikipedia.org/wiki/Gene_regulatory_network https://en.wikipedia.org/wiki/Gene_regulatory_network They work analogically like neural networks, where genes act as signals to other genes in a complex graph of dependency. Isn't that amazing? Each cell has a "gene based brain" (an RNN) which can do computation - it can map states to actions. Each neuron in the brain is actually a whole neural network. GRN's are probably the most energy efficient neural nets that exist in the universe, and they're also self replicators.

ajuc 9y ago

My internal model of DNA programming: - make 1 million sed scripts acting on directory X - put all of them in directory X - run all of them in parallel in infinite loops