Scientists create first billion-atom biomolecular simulation

https://www.lanl.gov/discover/news-release-archive/2019/April/0422-atom-biomolecular-simulation.php

by respinal • 7 years ago

89 34 7 years ago

lanl.gov

jayalpha• 7 years ago

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

AllegedAlec3• 7 years 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• 7 years 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• 7 years ago

Also the stochastic nature of the processes involved would seem to allow for some shortcuts in simulation.

jpvelez• 7 years 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!

AllegedAlec3• 7 years 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_law3• 7 years 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• 7 years ago

bacteria =/= eukaryotes. Gene expression in bacteria is very far away from gene expression in humans.

AllegedAlec3• 7 years 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.

dekhn5• 7 years 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• 7 years ago

"130,000 processor cores with 1 ns/day"

Still a bit to slow... I guess it is progress :)

saalweachter2• 7 years 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• 7 years ago

Damn I hope they can optimize this away eventually. Game of life can be sped up with hash tables.

safsafsafa2• 7 years 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).

dekhn5• 7 years 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• 7 years 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.

moonbug2• 7 years ago

must be Gordon Bell prize time again.

safsafsafa2• 7 years 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.

moonbug2• 7 years ago

weak scaling is easy.

jryb2• 7 years 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• 7 years 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=2...

dekhn5• 7 years ago

That's a PR narrative twisted to make the results sound much more useful than they really were :(

phkahler2• 7 years 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.

thatcat2• 7 years ago

What is the engineer publishing model?

dekhn5• 7 years ago

I agree this paper isn't really useful for biology. LANL maintains team of pseudo-biologists who scale up MD simulations and publish papers like this periodically (https://www.lanl.gov/projects/karissa/Ribosomes.html shows some previous work).

I wish this was a field where you could just apply more CPU and get better results (I say that with multiple papers published under my name which claim exactly that) but I'm skeptical. The force fields we use are far too approximate (point charges, assumption of transferrability) and it really does seem like we don't actually need most of this information to answer the questions we want to answer- those can be answered using more parsimonious techniques.

vo2maxer• 7 years ago

I don’t see how this would be of “zero biological interest.” Is it because presently it is not that useful or because it will never have any applicability?

jryb2• 7 years ago

Because what they were examining is already very well understood.

tlobes• 7 years ago

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

Scientists create first billion-atom biomolecular simulation

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