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Wouldn't it be hard to achieve deep consistency between experiments, in so many labs around the world, with so different conditions/cultures/etc , and when the

by petra 10y ago

Wouldn't it be hard to achieve deep consistency between experiments, in so many labs around the world, with so different conditions/cultures/etc , and when the experimenters aren't experts in consistency , but in their science ? Wouldn't it be better to use something like a cloud biology model - where you define experiments via code, CRO's compete on consistency(and efficiency and automation) and since they probably do a much larger volume of experiments than the regular lab, they would have stronger incentives to develop better processes and technologies ?

hyperion2010 10y ago
1. Yes, but the idea would be that if you provide a way to communicate the variables that actually matter for consistency then you can increase the robustness of a finding. If you have one lab that can _always_ produce a result, but no one else can, then clearly we do not really understand what is going on and one might not even be willing to call the result scientific. 2. Maybe not better, but certainly more result oriented. Core facilities do exist right now for things like viral vectors and microscopy (often because you do need levels of technical expertise that are simply not affordable in single labs). If there were a way to communicate how to do experiments more formally then the core facilities could expand to cover a much wider array of experiment types. You still have to worry about robustness, but if you have multiple 'core' facilities that can execute any experiment then that issue goes away as well. The hope of course is that individual labs as they exist today (perhaps with an additional computational tint) would be able to actually replicate each other's result, because we will probably end up needing nearly as many 'core' facilities as we have labs right now, simply because the diversity of phenomena that we need to study in biology is so high.
wuschel 10y ago
There are already approaches in this direction e.g. such as providing a standardized experimental hardware-software interface with Antha [1]. The complexity of the problems in questions (biological domain, biophysical, biochemical) is daunting - we do not understand many things, "there is plenty of room at the bottom". [1] https://www.antha-lang.org/ https://www.antha-lang.org/
- deleted 10y ago
  [deleted]
escot 10y ago
I work at a cloud bio lab. We run all of our experiments on automation, and all protocols must be defined in code. The latter is both the power and the difficulty -- when your protocol is defined in code it is explicit. However, writing code is both new and sometimes difficult for the scientists that we currently work with (molecular biology, drug discovery). I believe what we are doing is the right model. But it comes with this overhead of transitioning assays to code, so there is that against it. This is mostly just a matter of time though. Another nice thing about code is that you can't tweak it once it's running. You can define your execution and analysis up front to guard against playing with results down the road. Now that being said, there still needs to be a significant change to how research is funded and viewed by the public because pure tech solutions can't solve everything. Our tech can't decide what you pick yo research. It can't dish out grants to the truly important research. So it will take many angles to really solve any portion of this problem.
- petra 10y ago
  I do agree, it seems like right model, and will have a large impact. Between automating labor, economies of scale in purchasing, and access to more efficient technology(like acoustic liquid handling) ,etc - isn't it just a matter of time before cloud biology becomes quite cost effective and combined with other benefits - it would be the only way that makes sense to do research, so funding will naturally go there? Also - do you see a way to add the extreme versatility of the biology lab into a cloud service ?
  - escot 10y ago
    > it would be the only way that makes sense to do research There will certainly be more than just one way, although I hope cloud labs are the front runner. Also cloud and automated are two separate concepts. We do both, but there's no reason that you can't just do one or the other. The automation is critical for reproducibility for many reasons. But I think the cloud aspect is mostly helpful from a business perspective -- it makes it easier on everyone to get up and running on our system. But there are many in lab automation solutions that are helping fight the reproducibility crisis. And on the flip side, there are cloud labs that aren't automated. > do you see a way to add the extreme versatility of the biology lab into a cloud service We let you run any assay that can be executed on the set of the devices that we have in our automated lab. So in that sense, yes its very flexible. Also, there's no need to run your entire workflow in the cloud. You can do some at home, some in the cloud. Some people even string together multiple cloud services into a workflow. See https://www.youtube.com/watch?v=bIQ-fi3KoDg&t=1682s https://www.youtube.com/watch?v=bIQ-fi3KoDg&t=1682s That being said, biology labs can be crazy places. Part of what we do is put constraints on what can be encoded in each protocol to reduce the number of hidden variables. Every parameter that counts must be encoded in the protocol, because once you hit "go" on the protocol, it could run possibly on any number of different devices each time it runs. The only constant is that the exact instructions specified in the protocol will be run on the correct device set.

by petra 10y ago

hyperion2010 10y ago

1. Yes, but the idea would be that if you provide a way to communicate the variables that actually matter for consistency then you can increase the robustness of a finding. If you have one lab that can _always_ produce a result, but no one else can, then clearly we do not really understand what is going on and one might not even be willing to call the result scientific. 2. Maybe not better, but certainly more result oriented. Core facilities do exist right now for things like viral vectors and microscopy (often because you do need levels of technical expertise that are simply not affordable in single labs). If there were a way to communicate how to do experiments more formally then the core facilities could expand to cover a much wider array of experiment types. You still have to worry about robustness, but if you have multiple 'core' facilities that can execute any experiment then that issue goes away as well. The hope of course is that individual labs as they exist today (perhaps with an additional computational tint) would be able to actually replicate each other's result, because we will probably end up needing nearly as many 'core' facilities as we have labs right now, simply because the diversity of phenomena that we need to study in biology is so high.

wuschel 10y ago

There are already approaches in this direction e.g. such as providing a standardized experimental hardware-software interface with Antha [1]. The complexity of the problems in questions (biological domain, biophysical, biochemical) is daunting - we do not understand many things, "there is plenty of room at the bottom". [1] https://www.antha-lang.org/ https://www.antha-lang.org/

deleted 10y ago
[deleted]

escot 10y ago

I work at a cloud bio lab. We run all of our experiments on automation, and all protocols must be defined in code. The latter is both the power and the difficulty -- when your protocol is defined in code it is explicit. However, writing code is both new and sometimes difficult for the scientists that we currently work with (molecular biology, drug discovery). I believe what we are doing is the right model. But it comes with this overhead of transitioning assays to code, so there is that against it. This is mostly just a matter of time though. Another nice thing about code is that you can't tweak it once it's running. You can define your execution and analysis up front to guard against playing with results down the road. Now that being said, there still needs to be a significant change to how research is funded and viewed by the public because pure tech solutions can't solve everything. Our tech can't decide what you pick yo research. It can't dish out grants to the truly important research. So it will take many angles to really solve any portion of this problem.

petra 10y ago
I do agree, it seems like right model, and will have a large impact. Between automating labor, economies of scale in purchasing, and access to more efficient technology(like acoustic liquid handling) ,etc - isn't it just a matter of time before cloud biology becomes quite cost effective and combined with other benefits - it would be the only way that makes sense to do research, so funding will naturally go there? Also - do you see a way to add the extreme versatility of the biology lab into a cloud service ?
- escot 10y ago
  > it would be the only way that makes sense to do research There will certainly be more than just one way, although I hope cloud labs are the front runner. Also cloud and automated are two separate concepts. We do both, but there's no reason that you can't just do one or the other. The automation is critical for reproducibility for many reasons. But I think the cloud aspect is mostly helpful from a business perspective -- it makes it easier on everyone to get up and running on our system. But there are many in lab automation solutions that are helping fight the reproducibility crisis. And on the flip side, there are cloud labs that aren't automated. > do you see a way to add the extreme versatility of the biology lab into a cloud service We let you run any assay that can be executed on the set of the devices that we have in our automated lab. So in that sense, yes its very flexible. Also, there's no need to run your entire workflow in the cloud. You can do some at home, some in the cloud. Some people even string together multiple cloud services into a workflow. See https://www.youtube.com/watch?v=bIQ-fi3KoDg&t=1682s https://www.youtube.com/watch?v=bIQ-fi3KoDg&t=1682s That being said, biology labs can be crazy places. Part of what we do is put constraints on what can be encoded in each protocol to reduce the number of hidden variables. Every parameter that counts must be encoded in the protocol, because once you hit "go" on the protocol, it could run possibly on any number of different devices each time it runs. The only constant is that the exact instructions specified in the protocol will be run on the correct device set.