I think there's also been a lot of independent academic attempts at this (see: 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.
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.
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).
I think there's also been a lot of independent academic attempts at this (see: 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.