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$4,829-per-hour supercomputer built on Amazon cloud to fuel cancer research

arstechnica.com

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Re: $4,829-per-hour supercomputer built on Amazon cloud to fuel cancer research

#31

Post by JunkDNA »

marshallp wrote: post #27
rabidsnail wrote: post #25

Earlier quoted context omitted.

(Naive layperson here). Could the manual microsocpes-and-pipets work being done by lab biologists be mechanized, so that you're generating drug candidates in software, testing them in living cells, and using automatically-gathered observations to generate new candidates?

It is already done to an extent (high-throuhput machines), however, there's still a lot of old timers in biology who spent too long pipetting and haven't invested yet.

While I agree with you that there is a ton of automation, it's a bit flippant to suggest the manual work in biology is because of old-timers. You can't exactly automate necropsy on a rat liver to see if the compound you just gave it caused liver failure. There are a ton of experiments that are not automatable with current robotics technologies. Plus, even when you can automate, biology can't be rushed. If you're waiting for a tumor to grow in a mouse model, you have to wait real wall clock time.

Re: $4,829-per-hour supercomputer built on Amazon cloud to fuel cancer research

#32

Post by michaelgrosner »

xaa wrote: post #12
JunkDNA wrote: post #9

While it's nice from a technical perspective, this is unlikely to lead to a cancer cure. Having worked in cancer drug development, I can tell you, there is no shortage of cancer targets. Researchers have a list of targets they want to hit, and chemists are pretty darn good at designing small molecule compounds to hit them. The problem in cancer is not that we don't understand individual proteins or the way that drugs…

(Bioinformatician here). Although I think bench work is the most obvious route and the most likely way these problems will be solved, there are in principle some computational ways they could be addressed. If we had good computational models of how perturbations would affect transcription networks, for example, we could predict these "side pathways" that so often occur in humans but not in mice. But you're right, the…

The main message I got from talking to people involved in systems biology is that we don't even get qualitative agreement between simulation and experiment, nevermind any sort of quantitative data, on extremely well studied systems like the lac operon. It seems like we're at least 25 years away from decent models.

Re: $4,829-per-hour supercomputer built on Amazon cloud to fuel cancer research

#33

Post by michaelgrosner »

xaa wrote: post #17

How efficient is this compared with running your own cluster? 2x slower? 10x? In addition to latency between nodes, surely you're paying some kind of performance penalty for virtualization?

Our department which currently runs a 1000+ core machine and 2500+ core machine for MD simulations is still waiting on jumping onto the Amazon/Cloud bandwagon mostly because it's still not cheaper than owning a cluster for a few years. Granted, being at a large research university means they're not the only clusters on campus and there's an infrastructure already in place to maintain it.

In terms of speed, I would assume Amazon to be faster than most clusters since they probably offer the latest and greatest computers. Lastly, MD is an embarassingly parallel problem (don't ask me how it is) so latency isn't a major issue.

Re: $4,829-per-hour supercomputer built on Amazon cloud to fuel cancer research

#34

Post by rabidsnail »

JunkDNA wrote: post #31
marshallp wrote: post #27

Earlier quoted context omitted.

It is already done to an extent (high-throuhput machines), however, there's still a lot of old timers in biology who spent too long pipetting and haven't invested yet.

While I agree with you that there is a ton of automation, it's a bit flippant to suggest the manual work in biology is because of old-timers. You can't exactly automate necropsy on a rat liver to see if the compound you just gave it caused liver failure. There are a ton of experiments that are not automatable with current robotics technologies. Plus, even when you can automate, biology can't be rushed. If you're wait…

I'm in no position to argue about the mechanics of rat autopsy. But I do know how to get high throughput when you have high latency: shotgun parallelism. Try tons of things, the vast majority of which you assume will turn up with nothing, starting at the same time, in parallel.

Re: $4,829-per-hour supercomputer built on Amazon cloud to fuel cancer research

#35

Post by anusinha »

JunkDNA wrote: post #9

While it's nice from a technical perspective, this is unlikely to lead to a cancer cure. Having worked in cancer drug development, I can tell you, there is no shortage of cancer targets. Researchers have a list of targets they want to hit, and chemists are pretty darn good at designing small molecule compounds to hit them. The problem in cancer is not that we don't understand individual proteins or the way that drugs…

I think it's important to note that although biological systems are still well out of our reach, chemical systems (using quantum chemical methods) is being done increasingly better, and we can learn a lot about various systems, ranging from graphene to organic semiconductors to metal-clusters in enzymes using computational methods. So one day, we'll slowly get there, and we shouldn't write it off. Though yes, the current state of the art is hopeless for cancer drug development--but the field is dynamically changing.

Re: $4,829-per-hour supercomputer built on Amazon cloud to fuel cancer research

#36

Post by sabraham »

xaa wrote: post #12
JunkDNA wrote: post #9

While it's nice from a technical perspective, this is unlikely to lead to a cancer cure. Having worked in cancer drug development, I can tell you, there is no shortage of cancer targets. Researchers have a list of targets they want to hit, and chemists are pretty darn good at designing small molecule compounds to hit them. The problem in cancer is not that we don't understand individual proteins or the way that drugs…

(Bioinformatician here). Although I think bench work is the most obvious route and the most likely way these problems will be solved, there are in principle some computational ways they could be addressed. If we had good computational models of how perturbations would affect transcription networks, for example, we could predict these "side pathways" that so often occur in humans but not in mice. But you're right, the…

Sorry to be late to the party-- xaa, could you explain a little more what you mean by

"Ultimately I believe the breakthroughs will come fastest if we can "close the feedback loop" by automating a lot of bench biology, and then have computers both generate and test hypotheses."

I don't know anything about bioinformatics, so I'm trying to see how this differs from vanilla automated model selection. I'm really interested, so please feel free to send me an email if you feel that's more appropriate.

Re: $4,829-per-hour supercomputer built on Amazon cloud to fuel cancer research

#37

Post by gsmith8 »

rgc4 wrote: post #7

And yet, no results from their massive computation. Schrodinger is well known for being a company of liars and frauds, and unfriendly to open source and other ideals of our community.

Doesn't Schrodinger own PyMOL, which is open source?
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