10 ms·

Biology Will Be the Next Great Computing Platform

128 points by joubert 8y ago

ItsMe000001 8y ago
Occasionally I see posts, for example on reddit but also discussions here, about what other programming languages a programmer could learn as the next (and higher) step in ones ongoing education. My suggestion for what to do after the CS degree, a road I took myself during the last few years, is to go to edX (or Khan Academy for any missing basics) and at the very least take MITs "Introduction to Biology", which actually is an introduction to genetics (when you learn biology you first have to understand cells). Also, neuroscience, for which there are many courses (longest one on Coursera, "Medical Neuroscience", with an excellent teacher), from the basics (https://www.mcb80x.org/ https://www.mcb80x.org/) to computational neuroscience (https://www.edx.org/course/computational-neuroscience-neuronal-dynamics-of-cognition https://www.edx.org/course/computational-neuroscience-neuron...). I found understanding the basics of biology a lot more informative than learning new twists about some functional programming concept. It introduces you to a massively(!) parallel world where statistics rules, and errors/outliers are actually essential to functioning biological systems. You may find that if you remove all errors, for example that a protein is made that is not supposed to be made because it's not supposed to be needed, actually is essential, because only by making it does the cell notice that a different (better) fuel option has become available (that is from a concrete example, see the linked course :-)). So definitely add plenty of statistics courses until you learn to think "big". Each time you think about a problem think about that thing happening a trillion times instead of individual cases, until this becomes a habit. This helped me a lot, because most people focus their mind on single examples, for example when making suggestions how to improve society (hint: suggestions that sure work for anyone, but if everyone tried it would quickly unravel). Okay, in the last two sentences I'm going out on a limb (claiming that learning biology and statistical thinking helped), but definitely try some biology, statistics and neuroscience guys. It's all free and most of it high quality. And the statistics knowledge is just as good for machine learning so you need that anyway. Also recommended: "Principles of Biochemistry" (https://www.edx.org/course/principles-biochemistry-harvardx-mcb63x-1 https://www.edx.org/course/principles-biochemistry-harvardx-...), although I would say you should not try to do all the exercises because it will be way too much work (and highly demotivating, looking at how the discussion forum shows how the nr. of students very quickly grows thinner from course section to section) to try to learn all that stuff by heart. Also: "Cell Biology: Mitochondria"( https://www.edx.org/course/cell-biology-mitochondria-harvardx-mcb64-1x-1 https://www.edx.org/course/cell-biology-mitochondria-harvard...) to understand where the power comes from. Also check out individual universities, some have a lot of free courses (sometimes using the edX platform software, which is open source, e.g. Stanford) hat they don't put on 3rd party platforms like edX.
- devgutt 8y ago
  IMHO these courses only scratch the surface, good to learn a thing or two, without much applicability. I am right now searching for a degree path in Biochemistry or Molecular Biology online. I want to spend time on it, but be able to actually apply my knowledge. There are so few of them (maybe because of the lab classes, idk). I've found online degrees in ASU (https://asuonline.asu.edu/online-degree-programs/undergraduate/biological-sciences-bs https://asuonline.asu.edu/online-degree-programs/undergradua...) and UF (https://ufonline.ufl.edu/degrees/undergraduate/biology https://ufonline.ufl.edu/degrees/undergraduate/biology), but they are really expensive. Biology is the future, but unlike the article, I think it will make software obsolete altogether.
  - kharak 8y ago
    Do you by any chance did come across an online course for biochemistry or something similiar? I've wanted now for quite some time to study biology and change my career in that direction, but no relevant degree seems to be designed for remote study.
    - ItsMe000001 8y ago
      Look at the "Principles of Biochemistry" course I linked to. Even when OP says "it's scratching the surface", that was in comparison to a complete several years study, that course by itself is extremely involved and pure biochem. It's "only" a single course, but let's wait what you say after unit 3 - because judging by the forum participation, about 99% of people who join that course won't even make it past the 2/3rd mark. So if you can stomach that one course it would be a good sign. I read that about chemistry in general, should be the same for biochem, that if you study in those fields the load is quite extreme.
  - ItsMe000001 8y ago
    > IMHO these courses only scratch the surface Of course - that is what I recommend to programmers and CS majors, working in those jobs, on the side, not as a career path. But in any case, those are "real" courses, so "scratching the surface" not because they are dumbed down but because those are the freshman courses. Of course year 2+ students will get more advanced courses not usually found on edX (although they have quite advanced topics in physics, for example https://www.edx.org/course/mastering-quantum-mechanics-part-3-entanglement-and-angular-momentum https://www.edx.org/course/mastering-quantum-mechanics-part-...). As I said, an alternative to learning yet another only mildly different programming language (that runs on the exact same pieces of silicon as the other ones they already know, so it cannot be fundamentally different by definition).
simonebrunozzi 8y ago
Typo in the title: "conputing" should be "computing".
- arketyp 8y ago
  A serendipitous typo though, putting emphasis on the parallellism. It's the same prefix anyway.
tristanj 8y ago
This is analog computers all over again. It suffers the same problems, namely lower reliability and a lack of precision. I don't see how this will stack up to the millions of man-years humanity has invested into silicon based computers.
- ItsMe000001 8y ago
  > This is analog computers all over again. No it is not. Analog computers were used before good digital computers could be produced, for the same roles and straightforward problems. Life solves problem at scales that you cannot even touch with "precision" and in completely different ways. The problem is thinking that those systems are supposed to be used instead of computers. I reality, they are to tackle much different problems. Also see my longer comment here. Whenever you have a problem that you can nicely solve with a silicon-based computer you don't need a biological system. Also don't forget that biological systems are still settings the targets for things like AI or robotics, from which our silicon-based systems still are very far away from reaching. They do that with added features of self-replication and self-healing.
  - SanityIsRare 8y ago
    Life doesn't solve "problems" though - it just creates random order from chaos. What kind of problems do you even have in mind? What would you even solve if we could somehow understand or control these bigger forces of organization? I agree with the OP, we are not replacing silicon with DNA or anything - this article is just another "tech" article that shouldn't exist.
    - ItsMe000001 8y ago
      > Life doesn't solve "problems" though Wait, what? Biological systems are not solving problems? It does so all the time! Seriously? > it just creates random order from chaos. You get that with just physics, on a large scale for example the formation of galaxies and stars, or when things self-sort by weight in a gravity field (heavy stuff goes to bottom of a solution, lighter stuff goes to the top).
agumonkey 8y ago
At that point I flee anything with computing. I feel it's a reflex fad that invades any field.
- goatlover 8y ago
  Surprised there isn't a major branch of philosophy called computational philosophy. Give how some computer scientists treat computation as central to existence.
  - tim333 8y ago
    There's this https://en.wikipedia.org/wiki/Computational_theory_of_mind https://en.wikipedia.org/wiki/Computational_theory_of_mind
dnautics 8y ago
As someone who has a PhD in biochemistry and has been coding since age 5, I find the headline rediculous. You won't be computing in biology. Consider, a transistor is about 50-100 atoms wide. A protein, which cannot itself even be a minimal unit of compute, (maybe a molecular transistor like NiFe hydrogenase has a shot) is already bigger than that. The things described in the article are not biology used for computation, but principles from CS applied to biology which has some validity. In one case I achieved strides in protein design by reducing the engineering cycle from months to days. Imagine hitting "compile" and having to wait a 72 hours to know your result. That's biology, even with a fast organism like yeast or e coli.
- pjmlp 8y ago
  > Imagine hitting "compile" and having to wait a 72 hours to know your result. You mean, something like giving a deck of cards to a computing center operator and eventually getting back a printout of the outcome a few days later?
  - dnautics 8y ago
    Yes, but e coli really is close to the limit of how fast you can go. They're rather like tribbles, before they are done splitting, the next generations dna has already started duplicating. The DNA copy time is ~40 minutes and the dividing time is prot close to 30.
- Gatsky 8y ago
  Forgive me, but did your message not spring from a biological computing device which is arguably the most marvellous material object to exist in the known universe?
  - whataretensors 8y ago
    Not exactly human made. Also I suspect the article is wrong and we'll be outclassed by AI.
    - 8y ago
cup-of-tea 8y ago
Conputing? A Freudian slip maybe?
tim333 8y ago
I wonder if the headline was even written by the author rather than some copy editor. It doesn't seem to match the article which is quite interesting on how computation is being used in biological research. "Biology Will Be the Next Great Conputing Platform" is kind of silly.
- klmr 8y ago
  > I wonder if the headline was even written by the author rather than some copy editor Almost certainly not, and they rarely are.
throwaway84742 8y ago
Huh? I thought it’s been the computing platform for roughly the past billion years.
biridir 8y ago
Synthego custom crispr kit seems like a very useless service. Guide RNA design for knockout is extremely trivial. You just need a couple of 20bp+NGG sequence which is unique to the gene.. Who would pay to do that?..
- toufka 8y ago
  That doesn't interfere with any other part of the genome... That's a hard compute problem.
  - biridir 8y ago
    Do you mean the effect of the actual gene knockout, crispr specificity or off-spot mutations?
    - klmr 8y ago
      CRISPR specificity. Designing good gRNAs isn’t quite trivial, although it’s not as hard as the article makes it seem. I’m not sure about the claim of reducing the time of experiments by “month”.
      - biridir 8y ago
        http://crispr.mit.edu/about http://crispr.mit.edu/about Designing a good gRNA is quite straightforward if you compare it to any other bioinformatics tasks. Even designing a degenerate primer is more complex than this.
mherrmann 8y ago
* CoMputing (typo in the title)
alfonsodev 8y ago
Is there any project or discipline aiming to model our biology in software so that we can simulate things on a computer? (Edit) I found this https://en.m.wikipedia.org/wiki/Modelling_biological_systems https://en.m.wikipedia.org/wiki/Modelling_biological_systems I guess that is it
- klmr 8y ago
  This is one of the main subfields of bioinformatics.
gm-conspiracy 8y ago
Please fix Conputing.
TOSsedAway09876 8y ago
"Conputing"?
sedachv 8y ago
This article reads like a PR press release. You can't talk about biocomputers in 2018 without mentioning Tom Knight[1] and iGEM[2]. Knight not only pioneered this "next great computing platform," he also pioneered the current one: Knight was involved in early ARPANET work, Lisp Machines (MIT was the second organization after PARC to build networked personal workstations), and massively parallel SIMD (ie GPU) work on the Connection Machine. [1] http://people.csail.mit.edu/tk/ http://people.csail.mit.edu/tk/ [2] http://igem.org/Main_Page http://igem.org/Main_Page

128 points by joubert 8y ago

Occasionally I see posts, for example on reddit but also discussions here, about what other programming languages a programmer could learn as the next (and higher) step in ones ongoing education. My suggestion for what to do after the CS degree, a road I took myself during the last few years, is to go to edX (or Khan Academy for any missing basics) and at the very least take MITs "Introduction to Biology", which actually is an introduction to genetics (when you learn biology you first have to understand cells). Also, neuroscience, for which there are many courses (longest one on Coursera, "Medical Neuroscience", with an excellent teacher), from the basics (https://www.mcb80x.org/ https://www.mcb80x.org/) to computational neuroscience (https://www.edx.org/course/computational-neuroscience-neuronal-dynamics-of-cognition https://www.edx.org/course/computational-neuroscience-neuron...). I found understanding the basics of biology a lot more informative than learning new twists about some functional programming concept. It introduces you to a massively(!) parallel world where statistics rules, and errors/outliers are actually essential to functioning biological systems. You may find that if you remove all errors, for example that a protein is made that is not supposed to be made because it's not supposed to be needed, actually is essential, because only by making it does the cell notice that a different (better) fuel option has become available (that is from a concrete example, see the linked course :-)). So definitely add plenty of statistics courses until you learn to think "big". Each time you think about a problem think about that thing happening a trillion times instead of individual cases, until this becomes a habit. This helped me a lot, because most people focus their mind on single examples, for example when making suggestions how to improve society (hint: suggestions that sure work for anyone, but if everyone tried it would quickly unravel). Okay, in the last two sentences I'm going out on a limb (claiming that learning biology and statistical thinking helped), but definitely try some biology, statistics and neuroscience guys. It's all free and most of it high quality. And the statistics knowledge is just as good for machine learning so you need that anyway. Also recommended: "Principles of Biochemistry" (https://www.edx.org/course/principles-biochemistry-harvardx-mcb63x-1 https://www.edx.org/course/principles-biochemistry-harvardx-...), although I would say you should not try to do all the exercises because it will be way too much work (and highly demotivating, looking at how the discussion forum shows how the nr. of students very quickly grows thinner from course section to section) to try to learn all that stuff by heart. Also: "Cell Biology: Mitochondria"( https://www.edx.org/course/cell-biology-mitochondria-harvardx-mcb64-1x-1 https://www.edx.org/course/cell-biology-mitochondria-harvard...) to understand where the power comes from. Also check out individual universities, some have a lot of free courses (sometimes using the edX platform software, which is open source, e.g. Stanford) hat they don't put on 3rd party platforms like edX.

devgutt 8y ago
IMHO these courses only scratch the surface, good to learn a thing or two, without much applicability. I am right now searching for a degree path in Biochemistry or Molecular Biology online. I want to spend time on it, but be able to actually apply my knowledge. There are so few of them (maybe because of the lab classes, idk). I've found online degrees in ASU (https://asuonline.asu.edu/online-degree-programs/undergraduate/biological-sciences-bs https://asuonline.asu.edu/online-degree-programs/undergradua...) and UF (https://ufonline.ufl.edu/degrees/undergraduate/biology https://ufonline.ufl.edu/degrees/undergraduate/biology), but they are really expensive. Biology is the future, but unlike the article, I think it will make software obsolete altogether.
- kharak 8y ago
  Do you by any chance did come across an online course for biochemistry or something similiar? I've wanted now for quite some time to study biology and change my career in that direction, but no relevant degree seems to be designed for remote study.
  - ItsMe000001 8y ago
    Look at the "Principles of Biochemistry" course I linked to. Even when OP says "it's scratching the surface", that was in comparison to a complete several years study, that course by itself is extremely involved and pure biochem. It's "only" a single course, but let's wait what you say after unit 3 - because judging by the forum participation, about 99% of people who join that course won't even make it past the 2/3rd mark. So if you can stomach that one course it would be a good sign. I read that about chemistry in general, should be the same for biochem, that if you study in those fields the load is quite extreme.
- ItsMe000001 8y ago
  > IMHO these courses only scratch the surface Of course - that is what I recommend to programmers and CS majors, working in those jobs, on the side, not as a career path. But in any case, those are "real" courses, so "scratching the surface" not because they are dumbed down but because those are the freshman courses. Of course year 2+ students will get more advanced courses not usually found on edX (although they have quite advanced topics in physics, for example https://www.edx.org/course/mastering-quantum-mechanics-part-3-entanglement-and-angular-momentum https://www.edx.org/course/mastering-quantum-mechanics-part-...). As I said, an alternative to learning yet another only mildly different programming language (that runs on the exact same pieces of silicon as the other ones they already know, so it cannot be fundamentally different by definition).

simonebrunozzi 8y ago

Typo in the title: "conputing" should be "computing".

arketyp 8y ago
A serendipitous typo though, putting emphasis on the parallellism. It's the same prefix anyway.

tristanj 8y ago

This is analog computers all over again. It suffers the same problems, namely lower reliability and a lack of precision. I don't see how this will stack up to the millions of man-years humanity has invested into silicon based computers.

ItsMe000001 8y ago
> This is analog computers all over again. No it is not. Analog computers were used before good digital computers could be produced, for the same roles and straightforward problems. Life solves problem at scales that you cannot even touch with "precision" and in completely different ways. The problem is thinking that those systems are supposed to be used instead of computers. I reality, they are to tackle much different problems. Also see my longer comment here. Whenever you have a problem that you can nicely solve with a silicon-based computer you don't need a biological system. Also don't forget that biological systems are still settings the targets for things like AI or robotics, from which our silicon-based systems still are very far away from reaching. They do that with added features of self-replication and self-healing.
- SanityIsRare 8y ago
  Life doesn't solve "problems" though - it just creates random order from chaos. What kind of problems do you even have in mind? What would you even solve if we could somehow understand or control these bigger forces of organization? I agree with the OP, we are not replacing silicon with DNA or anything - this article is just another "tech" article that shouldn't exist.
  - ItsMe000001 8y ago
    > Life doesn't solve "problems" though Wait, what? Biological systems are not solving problems? It does so all the time! Seriously? > it just creates random order from chaos. You get that with just physics, on a large scale for example the formation of galaxies and stars, or when things self-sort by weight in a gravity field (heavy stuff goes to bottom of a solution, lighter stuff goes to the top).

agumonkey 8y ago

At that point I flee anything with computing. I feel it's a reflex fad that invades any field.

goatlover 8y ago
Surprised there isn't a major branch of philosophy called computational philosophy. Give how some computer scientists treat computation as central to existence.
- tim333 8y ago
  There's this https://en.wikipedia.org/wiki/Computational_theory_of_mind https://en.wikipedia.org/wiki/Computational_theory_of_mind

dnautics 8y ago

As someone who has a PhD in biochemistry and has been coding since age 5, I find the headline rediculous. You won't be computing in biology. Consider, a transistor is about 50-100 atoms wide. A protein, which cannot itself even be a minimal unit of compute, (maybe a molecular transistor like NiFe hydrogenase has a shot) is already bigger than that. The things described in the article are not biology used for computation, but principles from CS applied to biology which has some validity. In one case I achieved strides in protein design by reducing the engineering cycle from months to days. Imagine hitting "compile" and having to wait a 72 hours to know your result. That's biology, even with a fast organism like yeast or e coli.

pjmlp 8y ago
> Imagine hitting "compile" and having to wait a 72 hours to know your result. You mean, something like giving a deck of cards to a computing center operator and eventually getting back a printout of the outcome a few days later?
- dnautics 8y ago
  Yes, but e coli really is close to the limit of how fast you can go. They're rather like tribbles, before they are done splitting, the next generations dna has already started duplicating. The DNA copy time is ~40 minutes and the dividing time is prot close to 30.
Gatsky 8y ago
Forgive me, but did your message not spring from a biological computing device which is arguably the most marvellous material object to exist in the known universe?
- whataretensors 8y ago
  Not exactly human made. Also I suspect the article is wrong and we'll be outclassed by AI.
  - 8y ago

cup-of-tea 8y ago

Conputing? A Freudian slip maybe?

tim333 8y ago

I wonder if the headline was even written by the author rather than some copy editor. It doesn't seem to match the article which is quite interesting on how computation is being used in biological research. "Biology Will Be the Next Great Conputing Platform" is kind of silly.

klmr 8y ago
> I wonder if the headline was even written by the author rather than some copy editor Almost certainly not, and they rarely are.

throwaway84742 8y ago

Huh? I thought it’s been the computing platform for roughly the past billion years.

biridir 8y ago

Synthego custom crispr kit seems like a very useless service. Guide RNA design for knockout is extremely trivial. You just need a couple of 20bp+NGG sequence which is unique to the gene.. Who would pay to do that?..

toufka 8y ago
That doesn't interfere with any other part of the genome... That's a hard compute problem.
- biridir 8y ago
  Do you mean the effect of the actual gene knockout, crispr specificity or off-spot mutations?
  - klmr 8y ago
    CRISPR specificity. Designing good gRNAs isn’t quite trivial, although it’s not as hard as the article makes it seem. I’m not sure about the claim of reducing the time of experiments by “month”.
    - biridir 8y ago
      http://crispr.mit.edu/about http://crispr.mit.edu/about Designing a good gRNA is quite straightforward if you compare it to any other bioinformatics tasks. Even designing a degenerate primer is more complex than this.

mherrmann 8y ago

* CoMputing (typo in the title)

alfonsodev 8y ago

Is there any project or discipline aiming to model our biology in software so that we can simulate things on a computer? (Edit) I found this https://en.m.wikipedia.org/wiki/Modelling_biological_systems https://en.m.wikipedia.org/wiki/Modelling_biological_systems I guess that is it

klmr 8y ago
This is one of the main subfields of bioinformatics.

gm-conspiracy 8y ago

Please fix Conputing.

TOSsedAway09876 8y ago

"Conputing"?

sedachv 8y ago

This article reads like a PR press release. You can't talk about biocomputers in 2018 without mentioning Tom Knight[1] and iGEM[2]. Knight not only pioneered this "next great computing platform," he also pioneered the current one: Knight was involved in early ARPANET work, Lisp Machines (MIT was the second organization after PARC to build networked personal workstations), and massively parallel SIMD (ie GPU) work on the Connection Machine. [1] http://people.csail.mit.edu/tk/ http://people.csail.mit.edu/tk/ [2] http://igem.org/Main_Page http://igem.org/Main_Page