4 ms·

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 kn

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 6y ago
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?
- smallnamespace 6y ago
  Computer code is written by humans and based on abstractions that are designed to be comprehensible to the human mind. You can say that computer programs are memes that undergo two types of selection pressure: they need to be understandable to both humans and computers. Genetic code only needs to work, therefore it’d be surprising if neatly grokable abstractions fall out of the system.
- AareyBaba 6y ago
  "It is not so simple" These are the biochemical pathways that we know within a cell which give a picture of cellular complexity. Zoom out to see the high-level block diagrams. http://biochemical-pathways.com/#/map/1 http://biochemical-pathways.com/#/map/1 http://biochemical-pathways.com/#/map/2 http://biochemical-pathways.com/#/map/2
  - acqq 6y ago
    The background about these posters: https://www.roche.com/sustainability/philanthropy/science_education/pathways.htm https://www.roche.com/sustainability/philanthropy/science_ed... The book (2012): https://www.amazon.com/Biochemical-Pathways-Biochemistry-Molecular-Biology-dp-0470146842/dp/0470146842/ https://www.amazon.com/Biochemical-Pathways-Biochemistry-Mol... From the the accompanying text of the paper posters (all emphasis mine), it can be seen that the paper posters showed just a convenient selection of all the relevant knowledge: "In the wall chart “Biochemical Pathways” the following principles were applied:" "The selection of reactions has to be made arbitrarily. Of course, no discussion is necessary about e. g. glycolysis, protein biosynthesis and other central reactions. Peripheral reaction pathways are preferably selected if they are of high interest in biochemical, medical or biological research (receptors, vitamins, antibiotics, compounds of importance in regulation etc.), if they are of interest in medicine (e.g. blood coagulation, complement system), if they lead to important end products (e.g. microbiological fermentations) or if they enable comparison of phylogenetic development (anaerobic/aerobic respiration or photosynthesis in various species). Some indication on the degree of selection can be taken from the fact that in the present “Pathways” about 1000 enzymes are shown, while the 1984 “Enzyme Nomenclature” with its 2 supplements names 2859 enzymes. Estimations of the number of proteins (with and without enzymatic activity) in a single mammalian cell are in the order of magnitude of 30000." "e) In general, we desisted from showing detailed reaction mechanisms. Only in cases where discrete steps (e. g. in multi-enzyme systems) are involved or well-characterized intermediates exist, single steps are given. The same holds true for receptor-activation steps etc. f) The interrelationships of metabolic pathways cause the biggest technical problem of graphical representation. Since many compounds take part in various pathways, one would obtain a “spider web” of lines criss-crossing the whole chart. In order to avoid this, one has to “cut” connections. The respective compounds, which reoccur in other places of the chart, are written here in sharp-edged boxes."
- akiselev 6y ago
  I disagree with the other commenters: code and computers are a great analogy for biology. The first poor sod we'd call an "engineer" started writing the code of life four-ish billion years ago and a trillion trillion trillion engineers followed, giving new meaning to "reverting to the mean." Lacking any kind of version control or even a method of communication with adequate error correction, they kept copying the software over and over again, each one modifying a few bits at random, until their were trillions of tiny variations all competing for attention. The only commonality between all the engineers was nucleotides and amino acids - hardly a universal language capable of supporting comments - and a few billions years later, these nerds discovered sex and a new level of technical debt was born. Here we are, a few mass extinction events and genetic bottlenecks later, trying to decrypt code with no history because it has a half life of a few hundred years. Oh and the worst part? The computer architecture can only be programmed using a bootstrapped compiler - and we've lost billions of years of releases. That's why every program basically looks like a chicken before linking.
  - acqq 6y ago
    Even if that narration is something easily relatable by software developers, the simplification is too big to allow the reasonable comprehension of the subject. So I still suggest anybody interested to really try to learn more about the actual science topics, instead of comforting themselves falsely believing they "understood" anything. For the start, I would suggest the nicely produced courses: https://www.thegreatcourses.com/courses/biology-the-science-of-life.html https://www.thegreatcourses.com/courses/biology-the-science-... https://www.thegreatcourses.com/courses/understanding-genetics-dna-genes-and-their-real-world-applications.html https://www.thegreatcourses.com/courses/understanding-geneti... Just to illustrate how non-intuitive our "common sense" is, the current estimate of the number of human cells in human body is 30,000,000,000,000 (30e12). The current estimate of the number of bacteria in human body (in the mouth and guts) is 3 times more. The size of human genome (present in each human cell) is around 3 billion (3e9) base pairs (encoding information units of DNA https://en.wikipedia.org/wiki/Base_pair https://en.wikipedia.org/wiki/Base_pair ). But not all the information is in the DNA alone. The biochemical reactions happen in parallel even when a single cell is observed. The reconstruction of gene transcription, also played in the speed it actually happens: https://www.youtube.com/watch?v=7Hk9jct2ozY&t=248 https://www.youtube.com/watch?v=7Hk9jct2ozY&t=248
- Jabbles 6y ago
  A better analogy would perhaps be studying how a machine-learning model works, once it has been fully trained. Code is written by humans for humans. The way models are trained has some similarities with the way evolution has created our biology.
- TeMPOraL 6y ago
  As a programmer with a side interest in molecular biology, my take is this: Yes, protein synthesis via RNA translation, as explained in high-school biology, does look like reading Assembly opcodes with 3-pairs-long window, with some opcodes being redundant (translating to the same protein). But then you learn (probably not in school) that they're not redundant at all; some organisms (like bacteria) actually translate RNA with different offsets of the read window, so the same RNA string will code for different proteins simultaneously. Sure, we did things like these back in the heyday of the industry, but that's just the tip of the iceberg. Then you discover things like epigenetics, or that protein functions are determined by how they fold (something we can't simulate just yet), or that horizontal gene transfer (direct exchange of genetic material between cells, instead of through reproduction) is not only a thing, it's a very important (if not the primary) way by which bacteria evolve. You zoom into how electron transport chains work in chloroplasts (i.e. how cells power themselves by light), and you see a series of complexes that are tuned in dimensions to enable quantum tunneling of the electrons. And now think of how all of these is like a bag of sand - everything is there next to each other, bumping against each other all the time, and not only it works this way, a lot of things in the cells depend on that random walk to work. In the end, I think our programming experience is useful for viewing some of the biology in terms of high-level systems and their interplay. On the mathy side, perhaps the fundamentals of information theory can inform some aspects of biological research. But beyond that, these things are just different. It's like you took a piece of code, run it through an optimizing compiler, and then through a magical demoscene compressor that makes the code self-modifying, re-encodes opcodes on top of each other by exploiting misaligned reads, makes the binary use PC register as arithmetic input everywhere, and ensure lock-free parallelism by abusing delays from cache misses as synchronization mechanism. And then you give that to someone else to reverse-engineer. That's like 1% as difficult as the stuff biologists have to deal with.
- pugio 6y ago
  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 6y ago
    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.
yrb 6y ago
The mismatch is expectations about what would be known from sequencing DNA doesn't speak to the validity of the analogy but to the poor mental models about the reality of code, computation and systems we have. Though I agree that the analogy doesn't really buy you much useful leverage when seeking useful understanding.
- lend000 6y ago
  Indeed. Turing arguably had a better intuition for how developmental biology works than the status quo of most "experts" I read 80 years later [0]. [0] https://en.wikipedia.org/wiki/The_Chemical_Basis_of_Morphogenesis https://en.wikipedia.org/wiki/The_Chemical_Basis_of_Morphoge...
x3blah 6y ago
"It's not super productive to try and understand this stuff through analogy to computers and algorithms." You mean like this: https://ds9a.nl/amazing-dna https://ds9a.nl/amazing-dna Having worked in both industries I prefer working with wet science people. For some reason they generally have a much healthier perspective on life. Their work is humbling because it is, and will forever be, full of unsolved mysteries, not simply because it is challenging. The other folks, whether they call themselves "scientists" or "engineers" or "developers" or "coders" or whatever, are working with something that as far as I can see has no inherent connection to the natural world, other than being a production of the human mind. Perhap that affects the perspective many of them have on life. For example, how common among them is this belief that all things, not simply computers, can be thoroughly understood and mastered. Note this is pure opinion, not fact, and I am generalising; there are exceptions to every generalisation.
- rtkaratekid 6y ago
  I moved into programming from Neuroscience. The first thing programmers do when they learn this is talk to me about how their neural network does xyz. I don’t know if it’s ignorance, naïveté, or hubris, but it’s amazing to me that these programmers think the world/universe/reality is a complex problem that could be easily understood eventually. When working with “wet” scientists I found that attitude was almost non existent. The complexity is just so high and there are so many unknowns that many of them are very comfortable saying “I don’t know” or “we may never know.” One of my favorite examples to give is when I was still in undergrad, endocannabinoid research was getting hot in the Neuroscience field because it challenged the mental model that neurons communicate in a “linear” or “feed forward” fashion. Are neural networks going to implement that? Probably not, and it’s probably not worth it because at this point it introduces unneeded complexity. Try replicating the biochem of an entire cell for each cell in a NN and you _might_ be half way to achieving the complexity of the human brain. I’m not saying this is impossible, all I’m saying that I find it remarkable how quickly programmers seem to think of themselves as “expert” on outside fields as if they’re the smartest people in the world. I will say, crusty old systems programmers tend to have more of the familiar characteristics of when I was in life sciences (Neuroscience and Genetics).

sankha93 6y ago

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?

smallnamespace 6y ago
Computer code is written by humans and based on abstractions that are designed to be comprehensible to the human mind. You can say that computer programs are memes that undergo two types of selection pressure: they need to be understandable to both humans and computers. Genetic code only needs to work, therefore it’d be surprising if neatly grokable abstractions fall out of the system.
AareyBaba 6y ago
"It is not so simple" These are the biochemical pathways that we know within a cell which give a picture of cellular complexity. Zoom out to see the high-level block diagrams. http://biochemical-pathways.com/#/map/1 http://biochemical-pathways.com/#/map/1 http://biochemical-pathways.com/#/map/2 http://biochemical-pathways.com/#/map/2
- acqq 6y ago
  The background about these posters: https://www.roche.com/sustainability/philanthropy/science_education/pathways.htm https://www.roche.com/sustainability/philanthropy/science_ed... The book (2012): https://www.amazon.com/Biochemical-Pathways-Biochemistry-Molecular-Biology-dp-0470146842/dp/0470146842/ https://www.amazon.com/Biochemical-Pathways-Biochemistry-Mol... From the the accompanying text of the paper posters (all emphasis mine), it can be seen that the paper posters showed just a convenient selection of all the relevant knowledge: "In the wall chart “Biochemical Pathways” the following principles were applied:" "The selection of reactions has to be made arbitrarily. Of course, no discussion is necessary about e. g. glycolysis, protein biosynthesis and other central reactions. Peripheral reaction pathways are preferably selected if they are of high interest in biochemical, medical or biological research (receptors, vitamins, antibiotics, compounds of importance in regulation etc.), if they are of interest in medicine (e.g. blood coagulation, complement system), if they lead to important end products (e.g. microbiological fermentations) or if they enable comparison of phylogenetic development (anaerobic/aerobic respiration or photosynthesis in various species). Some indication on the degree of selection can be taken from the fact that in the present “Pathways” about 1000 enzymes are shown, while the 1984 “Enzyme Nomenclature” with its 2 supplements names 2859 enzymes. Estimations of the number of proteins (with and without enzymatic activity) in a single mammalian cell are in the order of magnitude of 30000." "e) In general, we desisted from showing detailed reaction mechanisms. Only in cases where discrete steps (e. g. in multi-enzyme systems) are involved or well-characterized intermediates exist, single steps are given. The same holds true for receptor-activation steps etc. f) The interrelationships of metabolic pathways cause the biggest technical problem of graphical representation. Since many compounds take part in various pathways, one would obtain a “spider web” of lines criss-crossing the whole chart. In order to avoid this, one has to “cut” connections. The respective compounds, which reoccur in other places of the chart, are written here in sharp-edged boxes."
akiselev 6y ago
I disagree with the other commenters: code and computers are a great analogy for biology. The first poor sod we'd call an "engineer" started writing the code of life four-ish billion years ago and a trillion trillion trillion engineers followed, giving new meaning to "reverting to the mean." Lacking any kind of version control or even a method of communication with adequate error correction, they kept copying the software over and over again, each one modifying a few bits at random, until their were trillions of tiny variations all competing for attention. The only commonality between all the engineers was nucleotides and amino acids - hardly a universal language capable of supporting comments - and a few billions years later, these nerds discovered sex and a new level of technical debt was born. Here we are, a few mass extinction events and genetic bottlenecks later, trying to decrypt code with no history because it has a half life of a few hundred years. Oh and the worst part? The computer architecture can only be programmed using a bootstrapped compiler - and we've lost billions of years of releases. That's why every program basically looks like a chicken before linking.
- acqq 6y ago
  Even if that narration is something easily relatable by software developers, the simplification is too big to allow the reasonable comprehension of the subject. So I still suggest anybody interested to really try to learn more about the actual science topics, instead of comforting themselves falsely believing they "understood" anything. For the start, I would suggest the nicely produced courses: https://www.thegreatcourses.com/courses/biology-the-science-of-life.html https://www.thegreatcourses.com/courses/biology-the-science-... https://www.thegreatcourses.com/courses/understanding-genetics-dna-genes-and-their-real-world-applications.html https://www.thegreatcourses.com/courses/understanding-geneti... Just to illustrate how non-intuitive our "common sense" is, the current estimate of the number of human cells in human body is 30,000,000,000,000 (30e12). The current estimate of the number of bacteria in human body (in the mouth and guts) is 3 times more. The size of human genome (present in each human cell) is around 3 billion (3e9) base pairs (encoding information units of DNA https://en.wikipedia.org/wiki/Base_pair https://en.wikipedia.org/wiki/Base_pair ). But not all the information is in the DNA alone. The biochemical reactions happen in parallel even when a single cell is observed. The reconstruction of gene transcription, also played in the speed it actually happens: https://www.youtube.com/watch?v=7Hk9jct2ozY&t=248 https://www.youtube.com/watch?v=7Hk9jct2ozY&t=248
Jabbles 6y ago
A better analogy would perhaps be studying how a machine-learning model works, once it has been fully trained. Code is written by humans for humans. The way models are trained has some similarities with the way evolution has created our biology.
TeMPOraL 6y ago
As a programmer with a side interest in molecular biology, my take is this: Yes, protein synthesis via RNA translation, as explained in high-school biology, does look like reading Assembly opcodes with 3-pairs-long window, with some opcodes being redundant (translating to the same protein). But then you learn (probably not in school) that they're not redundant at all; some organisms (like bacteria) actually translate RNA with different offsets of the read window, so the same RNA string will code for different proteins simultaneously. Sure, we did things like these back in the heyday of the industry, but that's just the tip of the iceberg. Then you discover things like epigenetics, or that protein functions are determined by how they fold (something we can't simulate just yet), or that horizontal gene transfer (direct exchange of genetic material between cells, instead of through reproduction) is not only a thing, it's a very important (if not the primary) way by which bacteria evolve. You zoom into how electron transport chains work in chloroplasts (i.e. how cells power themselves by light), and you see a series of complexes that are tuned in dimensions to enable quantum tunneling of the electrons. And now think of how all of these is like a bag of sand - everything is there next to each other, bumping against each other all the time, and not only it works this way, a lot of things in the cells depend on that random walk to work. In the end, I think our programming experience is useful for viewing some of the biology in terms of high-level systems and their interplay. On the mathy side, perhaps the fundamentals of information theory can inform some aspects of biological research. But beyond that, these things are just different. It's like you took a piece of code, run it through an optimizing compiler, and then through a magical demoscene compressor that makes the code self-modifying, re-encodes opcodes on top of each other by exploiting misaligned reads, makes the binary use PC register as arithmetic input everywhere, and ensure lock-free parallelism by abusing delays from cache misses as synchronization mechanism. And then you give that to someone else to reverse-engineer. That's like 1% as difficult as the stuff biologists have to deal with.
pugio 6y ago
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 6y ago
  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.

yrb 6y ago

The mismatch is expectations about what would be known from sequencing DNA doesn't speak to the validity of the analogy but to the poor mental models about the reality of code, computation and systems we have. Though I agree that the analogy doesn't really buy you much useful leverage when seeking useful understanding.

lend000 6y ago
Indeed. Turing arguably had a better intuition for how developmental biology works than the status quo of most "experts" I read 80 years later [0]. [0] https://en.wikipedia.org/wiki/The_Chemical_Basis_of_Morphogenesis https://en.wikipedia.org/wiki/The_Chemical_Basis_of_Morphoge...

x3blah 6y ago

"It's not super productive to try and understand this stuff through analogy to computers and algorithms." You mean like this: https://ds9a.nl/amazing-dna https://ds9a.nl/amazing-dna Having worked in both industries I prefer working with wet science people. For some reason they generally have a much healthier perspective on life. Their work is humbling because it is, and will forever be, full of unsolved mysteries, not simply because it is challenging. The other folks, whether they call themselves "scientists" or "engineers" or "developers" or "coders" or whatever, are working with something that as far as I can see has no inherent connection to the natural world, other than being a production of the human mind. Perhap that affects the perspective many of them have on life. For example, how common among them is this belief that all things, not simply computers, can be thoroughly understood and mastered. Note this is pure opinion, not fact, and I am generalising; there are exceptions to every generalisation.

rtkaratekid 6y ago
I moved into programming from Neuroscience. The first thing programmers do when they learn this is talk to me about how their neural network does xyz. I don’t know if it’s ignorance, naïveté, or hubris, but it’s amazing to me that these programmers think the world/universe/reality is a complex problem that could be easily understood eventually. When working with “wet” scientists I found that attitude was almost non existent. The complexity is just so high and there are so many unknowns that many of them are very comfortable saying “I don’t know” or “we may never know.” One of my favorite examples to give is when I was still in undergrad, endocannabinoid research was getting hot in the Neuroscience field because it challenged the mental model that neurons communicate in a “linear” or “feed forward” fashion. Are neural networks going to implement that? Probably not, and it’s probably not worth it because at this point it introduces unneeded complexity. Try replicating the biochem of an entire cell for each cell in a NN and you _might_ be half way to achieving the complexity of the human brain. I’m not saying this is impossible, all I’m saying that I find it remarkable how quickly programmers seem to think of themselves as “expert” on outside fields as if they’re the smartest people in the world. I will say, crusty old systems programmers tend to have more of the familiar characteristics of when I was in life sciences (Neuroscience and Genetics).