10 ms·

Scientists create first billion-atom biomolecular simulation

89 points by respinal 7y ago

jayalpha 7y ago
Sounds great. Again, how many mols are that? ;-)
AllegedAlec 7y ago
While very impressive, I'm not sure about their goal: > Modeling genes at the atomistic level is the first step toward creating a complete explanation of how DNA expands and contracts, which controls genetic on/off switching. That does not work in isolation like this: McGuffee and Elcock did some fantastic work in 2010 where they showed that protein stability is quite dependent on that the cytoplasm is insanely densely packed. The same is also true for the nucleus of a cell. You can't make any serious claims of a bottom-up explanation of gene regulation and expression unless you can model the entire nucleus, which is out of our reach for years and years to come. And that is before I start on whether this is an actually useful way to research gene expression in general, which I'd very much argue against.
- cjslep 7y ago
  Depending on how they model the simulation's fields, long range intra-atomic forces, and boundary conditions, I'd still say this kind of molecular dynamic simulation is better than doing nothing and waiting to simulate the whole nucleus. It could have accounted for these sorts of effects in a way that is cruder than whole nucleus simulation, but I haven't read any papers about this simulation.
  - thrax 7y ago
    Also the stochastic nature of the processes involved would seem to allow for some shortcuts in simulation.
  - jpvelez 7y ago
    Fascinating discussion! Could any of you knowledgeable folks say a bit more about 1. why whole-nucleus simulation is so hard and out of reach, 2. what role simulation currently plays in understanding gene expression, 3. to what extent will simulation be important for understanding how genes translate to phenotypes over the long term? I know next to nothing about this field or the relevant biology, but would love to learn more!
    - saalweachter 7y ago
      Time is the problem. Imagine you're doing a straight-forward computer simulation. You have a state-of-the-world, a time-step, and rules for how the world evolves during that time-step. You might write your own orbital simulation; the positions of the various planets and asteroids are your state, you might use a time-step of a minute or an hour or a day, and your rules for how the state evolves during each time step might just be Newton's laws of gravity and motion. You can probably update your state in a fraction of a second, even if you are simulating hundreds or thousands of planetary bodies, and you can simulate eons in hours. The problem with simulating biology on an atomic level is that your time-step needs to be in the femtosecond to picosecond range, because atoms can move fast, and small differences in their position make big differences, but interesting things like protein folding can take minutes of real time. So to simulate anything interesting, you need to step through quadrillions of time steps. Oh, and you're running this on more than one processor, so you need to do smart things so your simulation isn't trying to synchronize all of its processors on every step; oh, and we can't tell you the exact position of every atom in a cell to start with, or even necessarily all of the different types of molecules that are present. Oh, and all of these cells -- or pieces of cellular machinery -- behave in ways that are dependent upon the environment they are immersed in, so you also need to simulate that somehow. There's a reason Randall Monroe described the difficulty of protein folding as "we may one day find a harder problem".
  - AllegedAlec 7y ago
    I think that the entire enterprise is a red herring. It's nigh-impossible to understand this system by working from the bottom up. I don't see what kind of insight you'd get from this that you wouldn't get from a more understandable simplification in a minute fraction of the time. For example: yeah, if a gene is contained in densely packed nucleosomes it'll be harder to express. But knowing that is enough; the exact details of how that works atomically is not the issue here. There are much larger issues with gene expression we don't understand which can much more easily give us much more insight into the workings of the system.
- throwaway_law 7y ago
  >And that is before I start on whether this is an actually useful way to research gene expression in general Haven't we already reproduced existing bacteria/DNA from atoms which has allowed us to more easily study gene expression? Moreover, we take a bacteria with 200 genes ( we understand a lot of the gene functions/expressions, but we don't understand what all these 200 genes do). Thus, we synthetically reproduce the bacteria from the atoms up while removing nearly half of the genes that we determine are not needed to sustain the living bacteria, and the resulting synthetic bacteria we created in the lab allows us to more readily isolate and study the individual genes we didn't previously understand.
  - deugtniet 7y ago
    bacteria =/= eukaryotes. Gene expression in bacteria is very far away from gene expression in humans.
    - throwaway_law 7y ago
      Of course gene expression in bacterial isn't the same as humans...obviously when building synthetic life from atoms up we aren't going to just reproduce a human in our first effort, we start with a simple bacteria, and because of the complexity (and our lack of understanding of the gene expression in even simple bacteria) we purposely reproduced the bacteria with about 1/2 the genes for the sole purpose of studying those genes. But maybe I am wrong and this is in no way helpful for studying gene expression.
  - AllegedAlec 7y ago
    > Haven't we already reproduced existing bacteria/DNA from atoms which has allowed us to more easily study gene expression? I don't think so. I think they just took a small existing bacteria and by pruning its genome came to something which was minimally viable.
    - throwaway_law 7y ago
      >pruning its genome came to something which was minimally viable That sounds something like CRISPR or gene modification of an existing organism genome, maybe even taking genes from one species and inserting them into another (taking bioluminescent jelly fish and putting certain genes into cats or plants to make them glow). But, I'm talking about more recent successes of actually reproducing the DNA (synthetic DNA) from the atomic level up not modifying existing "natural DNA". Sorry, I don't have enough time to search for recent studies, but here is old article about first "synthetic DNA" we created and inserted into cells that took off and self-replicated back in 2008: https://www.wired.com/2010/05/scientists-create-first-self-replicating-synthetic-life-2/ https://www.wired.com/2010/05/scientists-create-first-self-r... The link seems to reference the first success of fully synthetic DNA, but I have recently been listening to podcasts about more recent experiemnts/studies, specifically reproducing bacteria with full synthetic DNA (which is stripped down as much as possible to maintain viability) for the purpose of studying gene expression and hopefully begin to identify what the currently unknown genes sequences do.
- dekhn 7y ago
  you can get 95% of the effects of macromolecular crowding by just shoving PEG into the system (the very first paper we covered in grad school showed that proteins in packed cells have elevated kinetic rates and free enegry of ATP hydrolysis isn't the value measured at STP, by using PEG as a proxy). If I were to attempt to do gene regulation studies by MD (I share your skepticism!) I would just put a bunch of PEG in as a water replacement. In fact that's a paper right there- "Effect of increasing macromolecular crowding on the accuracy of DNA simulations for regulation prediction" :)
callesgg 7y ago
"130,000 processor cores with 1 ns/day" Still a bit to slow... I guess it is progress :)
- saalweachter 7y ago
  This is the "hard part" with atom-level biological simulations. You need extremely tiny timesteps -- like, sub-picosecond, even -- but many interesting molecular events happen on the timescale of minutes.
- krzat 7y ago
  Damn I hope they can optimize this away eventually. Game of life can be sped up with hash tables.
  - safsafsafa 7y ago
    Game of life doesn't have long range interactions, and doesn't have to deal with electrostatic interactions (which, with parallel B.C., diverge and cannot be calculated in real space).
- dekhn 7y ago
  when I wrote my thesis in 2001 I got 1ns/week on a much smaller system (1M atoms running on a T3E w/ ~256-512 processors). It's not really clear whether MD simulations like this are true contributions or whether they will ever produce useful results compared to well-parameterized neural nets.
  - bocklund 7y ago
    Are NNs producing useful results in this area? I'm on the density functional theory side of things, so much smaller scales than MD. To me, it's not clear whether we are good at quantifying when a neural net is well-parameterized in atomistic simulations. The limitations of MD simulations and all the built in assumptions are well understood. We can reason about where our simulations might fail, even for very large systems. It's always a question about whether the underlying model can capture all the physics that occurs. With a NN or other black box model, there's no way to reason about it except for benchmarking test/validation sets that (hopefully) capture the physics that you care about, but you still cannot really reason about how well the model will extrapolate to multiple interacting physics with different magnitudes at different length scales.
moonbug 7y ago
must be Gordon Bell prize time again.
- safsafsafa 7y ago
  1E9 atoms / 130 000 cores = 8000 atoms/core in 2011 I was building 40 000 atom models on a 2001 SGI Fuel simulating them on a single 8 core node at 1ns/week -> 8000 atoms/core. They're about 7 times faster then me 8 years later. So from a computation point, meh. From a tools point this is cool. It isn't trivial to build or analyze a model of 40 000 atoms, never mind 1E9 atoms.
  - moonbug 7y ago
    weak scaling is easy.
jryb 7y ago
Biologist's perspective: this paper is not about the biology. The simulation performed here has zero biological interest - the point of the paper was to show how efficient and scalable their software is. This article about the paper is terrible, but honestly I feel like they should be given a pass - it's hard to justify to a lay audience that understands neither DNA nor memory bandwidth why you would choose to study something that is of no use to the field. There's an inherent tension when doing a method development paper - if your result is too fantastical it's hard to know whether it's an artifact of your (potentially faulty) technique, and if the thing you're studying is well-understood then it serves as a good control, but it's less interesting. I suspect they chose the latter path since it requires no validation via existing methodologies.
- stochastic_monk 7y ago
  I'm not familiar with this method, but NAMD and CHARM are a great way to inspect biological systems at greater resolution than experimental methods can measure. There was really cool work on fibrinogen about a decade ago showing how it provides elasticity. Reference: https://www.ks.uiuc.edu/Highlights/?section=2008&highlight=2008-02 https://www.ks.uiuc.edu/Highlights/?section=2008&highlight=2...
  - dekhn 7y ago
    That's a PR narrative twisted to make the results sound much more useful than they really were :(
- phkahler 7y ago
  Maybe the problem is that these scientists aren't doing science, they did some engineering. That's great, but it doesnt fit their publishing model.
  - thatcat 7y ago
    What is the engineer publishing model?
tlobes 7y ago
I’m curious to know how much room there is for optimization in their code

89 points by respinal 7y ago

Sounds great. Again, how many mols are that? ;-)

While very impressive, I'm not sure about their goal: > Modeling genes at the atomistic level is the first step toward creating a complete explanation of how DNA expands and contracts, which controls genetic on/off switching. That does not work in isolation like this: McGuffee and Elcock did some fantastic work in 2010 where they showed that protein stability is quite dependent on that the cytoplasm is insanely densely packed. The same is also true for the nucleus of a cell. You can't make any serious claims of a bottom-up explanation of gene regulation and expression unless you can model the entire nucleus, which is out of our reach for years and years to come. And that is before I start on whether this is an actually useful way to research gene expression in general, which I'd very much argue against.

cjslep 7y ago
Depending on how they model the simulation's fields, long range intra-atomic forces, and boundary conditions, I'd still say this kind of molecular dynamic simulation is better than doing nothing and waiting to simulate the whole nucleus. It could have accounted for these sorts of effects in a way that is cruder than whole nucleus simulation, but I haven't read any papers about this simulation.
- thrax 7y ago
  Also the stochastic nature of the processes involved would seem to allow for some shortcuts in simulation.
- jpvelez 7y ago
  Fascinating discussion! Could any of you knowledgeable folks say a bit more about 1. why whole-nucleus simulation is so hard and out of reach, 2. what role simulation currently plays in understanding gene expression, 3. to what extent will simulation be important for understanding how genes translate to phenotypes over the long term? I know next to nothing about this field or the relevant biology, but would love to learn more!
  - saalweachter 7y ago
    Time is the problem. Imagine you're doing a straight-forward computer simulation. You have a state-of-the-world, a time-step, and rules for how the world evolves during that time-step. You might write your own orbital simulation; the positions of the various planets and asteroids are your state, you might use a time-step of a minute or an hour or a day, and your rules for how the state evolves during each time step might just be Newton's laws of gravity and motion. You can probably update your state in a fraction of a second, even if you are simulating hundreds or thousands of planetary bodies, and you can simulate eons in hours. The problem with simulating biology on an atomic level is that your time-step needs to be in the femtosecond to picosecond range, because atoms can move fast, and small differences in their position make big differences, but interesting things like protein folding can take minutes of real time. So to simulate anything interesting, you need to step through quadrillions of time steps. Oh, and you're running this on more than one processor, so you need to do smart things so your simulation isn't trying to synchronize all of its processors on every step; oh, and we can't tell you the exact position of every atom in a cell to start with, or even necessarily all of the different types of molecules that are present. Oh, and all of these cells -- or pieces of cellular machinery -- behave in ways that are dependent upon the environment they are immersed in, so you also need to simulate that somehow. There's a reason Randall Monroe described the difficulty of protein folding as "we may one day find a harder problem".
- AllegedAlec 7y ago
  I think that the entire enterprise is a red herring. It's nigh-impossible to understand this system by working from the bottom up. I don't see what kind of insight you'd get from this that you wouldn't get from a more understandable simplification in a minute fraction of the time. For example: yeah, if a gene is contained in densely packed nucleosomes it'll be harder to express. But knowing that is enough; the exact details of how that works atomically is not the issue here. There are much larger issues with gene expression we don't understand which can much more easily give us much more insight into the workings of the system.
throwaway_law 7y ago
>And that is before I start on whether this is an actually useful way to research gene expression in general Haven't we already reproduced existing bacteria/DNA from atoms which has allowed us to more easily study gene expression? Moreover, we take a bacteria with 200 genes ( we understand a lot of the gene functions/expressions, but we don't understand what all these 200 genes do). Thus, we synthetically reproduce the bacteria from the atoms up while removing nearly half of the genes that we determine are not needed to sustain the living bacteria, and the resulting synthetic bacteria we created in the lab allows us to more readily isolate and study the individual genes we didn't previously understand.
- deugtniet 7y ago
  bacteria =/= eukaryotes. Gene expression in bacteria is very far away from gene expression in humans.
  - throwaway_law 7y ago
    Of course gene expression in bacterial isn't the same as humans...obviously when building synthetic life from atoms up we aren't going to just reproduce a human in our first effort, we start with a simple bacteria, and because of the complexity (and our lack of understanding of the gene expression in even simple bacteria) we purposely reproduced the bacteria with about 1/2 the genes for the sole purpose of studying those genes. But maybe I am wrong and this is in no way helpful for studying gene expression.
- AllegedAlec 7y ago
  > Haven't we already reproduced existing bacteria/DNA from atoms which has allowed us to more easily study gene expression? I don't think so. I think they just took a small existing bacteria and by pruning its genome came to something which was minimally viable.
  - throwaway_law 7y ago
    >pruning its genome came to something which was minimally viable That sounds something like CRISPR or gene modification of an existing organism genome, maybe even taking genes from one species and inserting them into another (taking bioluminescent jelly fish and putting certain genes into cats or plants to make them glow). But, I'm talking about more recent successes of actually reproducing the DNA (synthetic DNA) from the atomic level up not modifying existing "natural DNA". Sorry, I don't have enough time to search for recent studies, but here is old article about first "synthetic DNA" we created and inserted into cells that took off and self-replicated back in 2008: https://www.wired.com/2010/05/scientists-create-first-self-replicating-synthetic-life-2/ https://www.wired.com/2010/05/scientists-create-first-self-r... The link seems to reference the first success of fully synthetic DNA, but I have recently been listening to podcasts about more recent experiemnts/studies, specifically reproducing bacteria with full synthetic DNA (which is stripped down as much as possible to maintain viability) for the purpose of studying gene expression and hopefully begin to identify what the currently unknown genes sequences do.
dekhn 7y ago
you can get 95% of the effects of macromolecular crowding by just shoving PEG into the system (the very first paper we covered in grad school showed that proteins in packed cells have elevated kinetic rates and free enegry of ATP hydrolysis isn't the value measured at STP, by using PEG as a proxy). If I were to attempt to do gene regulation studies by MD (I share your skepticism!) I would just put a bunch of PEG in as a water replacement. In fact that's a paper right there- "Effect of increasing macromolecular crowding on the accuracy of DNA simulations for regulation prediction" :)

callesgg 7y ago

"130,000 processor cores with 1 ns/day" Still a bit to slow... I guess it is progress :)

saalweachter 7y ago
This is the "hard part" with atom-level biological simulations. You need extremely tiny timesteps -- like, sub-picosecond, even -- but many interesting molecular events happen on the timescale of minutes.
krzat 7y ago
Damn I hope they can optimize this away eventually. Game of life can be sped up with hash tables.
- safsafsafa 7y ago
  Game of life doesn't have long range interactions, and doesn't have to deal with electrostatic interactions (which, with parallel B.C., diverge and cannot be calculated in real space).
dekhn 7y ago
when I wrote my thesis in 2001 I got 1ns/week on a much smaller system (1M atoms running on a T3E w/ ~256-512 processors). It's not really clear whether MD simulations like this are true contributions or whether they will ever produce useful results compared to well-parameterized neural nets.
- bocklund 7y ago
  Are NNs producing useful results in this area? I'm on the density functional theory side of things, so much smaller scales than MD. To me, it's not clear whether we are good at quantifying when a neural net is well-parameterized in atomistic simulations. The limitations of MD simulations and all the built in assumptions are well understood. We can reason about where our simulations might fail, even for very large systems. It's always a question about whether the underlying model can capture all the physics that occurs. With a NN or other black box model, there's no way to reason about it except for benchmarking test/validation sets that (hopefully) capture the physics that you care about, but you still cannot really reason about how well the model will extrapolate to multiple interacting physics with different magnitudes at different length scales.

moonbug 7y ago

must be Gordon Bell prize time again.

safsafsafa 7y ago
1E9 atoms / 130 000 cores = 8000 atoms/core in 2011 I was building 40 000 atom models on a 2001 SGI Fuel simulating them on a single 8 core node at 1ns/week -> 8000 atoms/core. They're about 7 times faster then me 8 years later. So from a computation point, meh. From a tools point this is cool. It isn't trivial to build or analyze a model of 40 000 atoms, never mind 1E9 atoms.
- moonbug 7y ago
  weak scaling is easy.

jryb 7y ago

Biologist's perspective: this paper is not about the biology. The simulation performed here has zero biological interest - the point of the paper was to show how efficient and scalable their software is. This article about the paper is terrible, but honestly I feel like they should be given a pass - it's hard to justify to a lay audience that understands neither DNA nor memory bandwidth why you would choose to study something that is of no use to the field. There's an inherent tension when doing a method development paper - if your result is too fantastical it's hard to know whether it's an artifact of your (potentially faulty) technique, and if the thing you're studying is well-understood then it serves as a good control, but it's less interesting. I suspect they chose the latter path since it requires no validation via existing methodologies.

stochastic_monk 7y ago
I'm not familiar with this method, but NAMD and CHARM are a great way to inspect biological systems at greater resolution than experimental methods can measure. There was really cool work on fibrinogen about a decade ago showing how it provides elasticity. Reference: https://www.ks.uiuc.edu/Highlights/?section=2008&highlight=2008-02 https://www.ks.uiuc.edu/Highlights/?section=2008&highlight=2...
- dekhn 7y ago
  That's a PR narrative twisted to make the results sound much more useful than they really were :(
phkahler 7y ago
Maybe the problem is that these scientists aren't doing science, they did some engineering. That's great, but it doesnt fit their publishing model.
- thatcat 7y ago
  What is the engineer publishing model?

tlobes 7y ago

I’m curious to know how much room there is for optimization in their code