
Heroku for Science - irollboozers
http://dennyluan.tumblr.com/
======
L_Rahman
I studied biomedical engineering at Hopkins. Before I started there, research
was the promised land. I dreamt of spending my time thinking about how to
solve critical problems and testing solutions.

What I saw instead were people spending the vast majority of their time
pipetting. All the way up the ladder, upto and including postdocs. I sometimes
thought our PI had it worse for having to spend most of her time applying for
grants.

The AWSification of synbio research would be a game changer. Some labs at
Hopkins have tried to build robots but with limited success. Given how cheap
labor is at research institutions competing on price will be incredibly
difficult.

~~~
bignaj
I also thought research was the promised land. Went to Cornell for undergrad
studying biological sciences and was amazed at the research opportunities...
but then worked in a lab studying type II diabetes. I pipetted, cleaned
beakers, measured out chemicals to prepare solutions, sucked up cell cultures
and extracted DNA all day making $8/hour in extreme boredom. There were
postdocs with Ph.Ds and loads of experience from prestigious schools doing the
same grunt work alongside me, often confessing they were completely miserable
and wishing they could start their life over again. They worked 7 days a week
and long, long hours each day. I got the hell out of research and am enjoying
my life much better now in tech. I also feel like the work I do is a lot more
impactful. All of my peers did the same and went to consulting, finance or
tech. In my view the basic research field is in total crisis...

~~~
henryaj
So depressing. This (and the parent comment) is the reason I didn't go into
research -- it's a life of pipetting and manual labour that no-one's
interested in automating, either because it's too complex or because labour is
so cheap that there's no financial incentive to do so.

I'm happy to leave someone else to do that. I'd rather be in a job I actually
enjoy the day-to-day of.

And that's to say nothing of the problems of PhDs: namely that there are ten
times more PhD positions than there are postdoc positions. That ten-to-one
crunch when it comes to finding a job sure does sound fun...

~~~
ejstronge
With the utmost respect to you and the post you responded to, research is
about answering questions to things you find interesting; for a biologist,
pipetting is simply the means you take to get there.

If you want to contribute to Firefox or any other non-trivial open source
project, you need to spend time creating a development environment and it
likely will take weeks to months before you can make a substantive
contribution.

If anyone is reading the comment I'm responding to or its parent comment, keep
in mind that the manual labor is in pursuit of a goal.

~~~
innguest
I think your parent is alluding to the fact things could be automated in
biological research but aren't because of the disincentives; and that at the
same time things in tech are more amenable to automation and often parts of it
are indeed automated.

Yes, it's all a means to an end, but how much time one wants to spend in the
"means" (which can get extremely repetitive, apparently) is what counts for
the parent (I'm supposing).

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Eliezer
Hi! This is the Yelling Math Fairy and THE WORD EXPONENTIAL DOES NOT MEAN
MORE. IT IT IS A MATH WORD. IT MEANS e^kx. IT MEANS THE SOLUTION OF y' = ky.
DOES EACH NEW ASSAY DOUBLE THE TOTAL AMOUNT OF WORK? NO IT DOES NOT. THE ADDED
OVERHEAD FOR EACH ASSAY IS LINEAR GROWTH. NOT EXPONENTIAL GROWTH. I KNOW IT
SEEMS LIKE A LOT TO YOU BUT THAT DOES NOT MAKE IT EXPONENTIAL. EXPONENTIAL IS
NOT A SYNONYM FOR BIG AND EXPONENTIAL GROWTH IS NOT A SYNONYM FOR FAST. THANK
YOU.

~~~
emmett
Keep fighting the good fight, Eliezer.

To add on: Some words have valuable and specific meanings! We don't have a
good substitute word for "exponential" that means the same thing. Please make
an effort not to do this!

~~~
irollboozers
Sorry, but in this case, it's actually N^x. Even running PCR and gel analysis
requires steps where the time is dependent on the number of samples (e.g.
pipetting, walking to the centrifuge, making multiple gels)

~~~
emmett
Generally speaking, running double the number of samples requires
approximately double the amount of work, maybe a little less since you're
doing it in bulk.

Are you really saying that going from 10 samples to 11 samples causes a
doubling (or 1.2x-ing or tripling or whatever) of the time/work required?
That's what exponential means.

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alejoriveralara
I believe this startup is as close as it gets (for now!) to what you're
describing, [https://www.transcriptic.com/](https://www.transcriptic.com/).

(I don't work there or anything)

~~~
irollboozers
There's been quite a few efforts in this. See Transcriptic and Emerald
Therapeutics
([http://www.emeraldcloudlab.com/](http://www.emeraldcloudlab.com/)), while
there's the more traditional suppliers for things like short oligos or
expression vectors ([https://www.dna20.com/](https://www.dna20.com/) and
[http://www.idt.com/](http://www.idt.com/)).

I think there's also been a lot of independent academic attempts at this (see:
[http://klavinslab.org/](http://klavinslab.org/) which is CS/BioE at UWash),
but all kind of waded around in the shallow water.

The reason why I think this is compelling because I think almost every
synthetic biologist has an _existing_ workflow. It's basically design using
some sort of CAD software, order from IDT, receive materials next day, run
test by hand, ship to Genewiz for sequencing, etc. That's just one example of
a workflow involving 4-5 specialized 'steps'. As the steps get
cheaper/faster/better, consolidating and automating this is just a no brainer.

~~~
frisco
Emerald doesn't really exist yet, and I believe they misrepresent their
automation. They've posted a very pretty site with a bunch of mockups. They
have a set of workflows that work for their internal antiviral research, but
"Heroku for Science" is a completely different game.

Transcriptic, on the other hand, started taking orders six months ago and has
customers at Stanford, Caltech, Harvard, and more.

~~~
irollboozers
There are probably others much more familiar with cloud infrastructure who can
chime in, but the AWS of science and the Heroku of science are two very
different challenges, and I feel the analogies probably cross over pretty
well.

Definitely having the infrastructure 'warehouse' layer that Transcriptic is
building (with a real API! wow!) will be valuable. And like you hint at, power
users won't need hand-holding, but 99% of the market of users will. That's
where packaging, ease of use, and limited configuration seem to be the
difference maker (Heroku starting exclusively with Rails).

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aheilbut
The problem with this fantasy is that easily automated and distributed tasks
are not the rate-limiting steps in most biomedical research. The hard parts
(in addition to designing the right experiments and analyzing data..) are in
constructing and validating relevant model systems and doing the specific
experiments to address questions of interest.

These are extremely dependent on the question being studied and often are not
amenable to automation, and may require very rare, expensive, and difficult-
to-handle samples. For example, my collaborators work with transgenic mice
that are a model for a particular disease, and these mice have to be bred then
aged to 12 weeks until they exhibit the phenotype before we can even start
doing an experiment. In another model, they have to do brain surgery on each
mouse and then wait several weeks for the phenotype.

The 'easy' parts, such as DNA synthesis and sequencing, are already highly
standardized and automated, and there is fierce competition to improve the
technology and bring costs down.

~~~
cing
This is certainly the bottleneck in my research. I ran thousands of core years
of computer simulation in the first year of my PhD. I have all the data I need
to write a PhD thesis but I'm still years from graduating due to the
aforementioned bottlenecks. An arsenal of software I've written in
numpy/scipy/pandas saves time, but only goes so far when you're trying to
carve out stories from your data to write papers.

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CHY872
A big problem is that scientists are traditionally very secretive. This would
increase the possibility of leaks, and there'd need to be some way of assuring
that the experiment was conducted correctly. Good idea, though.

~~~
pdenya
If this was available convenience would trump security for many people.

~~~
tkschmidt
I just have experience working in 3 different labs in Germany and I can tell
you: secrecy is the holy grail and just using privat repos (for code not data)
on github/sourcefore is forbidden. But this is perhaps just small sample size
and a german habit ;)

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bbgm
Most worthwhile research is about the mundane. One of the first research
projects I did required painstakingly adjusting and modifying conditions to
the point that I could actually start collecting data. That process took
weeks, but the day it worked was insanely satisfying. In the process I became
a master at making small incremental changes, recording them, and learning
exactly what didn't work. Years later, as a computational scientist, the
process was much the same, except that there were no pipettes and beakers
involved.

Any worthwhile work I have ever done has mostly been about grunt work. Along
the way there have been cool things (after all Leno made fun of our research
[1] once) and insanely fun times. I may not be in research now, but every day
I apply the lessons learned from patiently repeating and iterating.

1\.
[http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=723226...](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=723226&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel4%2F5858%2F15610%2F00723226.pdf%3Farnumber%3D723226)

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maciejgryka
I've taken a _very tiny_ first step towards something like this for computer
graphics and vision: trying to make user studies as easy as possible:
[http://www.imcompadre.com](http://www.imcompadre.com) It's not a ready
product by any means, but two paper submissions have been made with it so far.

It's a difficult problem to solve, because these pesky researchers are always
trying out new things that you didn't anticipate - who would've thought! But
still, for the mundane things that can be automated, something like this is
definitely the way to go. Of course, as other people here point out, figuring
out what to actually test is always the hardest part.

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skosuri
In our lab today we are consistently dealing with the opposite problem. The
experiments themselves are easy in comparison with the design and analysis.

~~~
irollboozers
The startup equivalent would be "it's easy to build the app, it's hard to get
users". Not all apps can yield users, but lowering the bar to launch will
certainly yield more opportunities for successful apps.

~~~
skosuri
You are right. I think we would all appreciate making more stuff that we do
automatable and outsourceable. I guess where I was trying to go with the
comment is that it would be nice if there were similar tools for design and
analysis. Oh automated paper writing would also be much appreciated.

~~~
irollboozers
Experiment is working on that. :)

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stanzheng
The Center for Open Science poses itself as something similar to what you
describe. [http://centerforopenscience.org/](http://centerforopenscience.org/)

I believe they call themselves more of the Github of Science for scientific
collaboration. Adding hooks to 'push' the tasks and 'checkout' the findings
could be maybe extensible on their platform.

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hikari8
Experimenting with a simulation is a great time saver as far as it goes,
however, all models are just models, subject to the assumptions that went into
them.

There is a great deal we do not know about cellular biology. Any simulation
would be a fairly gross approximation. The point of many experiments is to
further our understanding of the model of cellular mechanics.

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yeukhon
From my experience (having done a similar project as an undergrad) is that the
first problem is convincing people to switch and take the risk/time to use
your new workflow, even if your workflow allows them to continue to use their
existing infrastructure / machines.

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stillsut
DIYbio has already started on this: Experimental Robot for $4k:
[http://www.opentrons.com/](http://www.opentrons.com/)

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mgberlin
[http://www.synthego.com/](http://www.synthego.com/)

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brianbreslin
There could be some synergies with (yc12) science exchange

