Founder here; working on software that exploits the full potential of engineered microbes; we're using them to make therapeutics, plastics, materials, etc. happy to answer any questions.
When I was studying chemical engineering in university, I tried to get faculty interested in engineering yogurt-producing bacteria (like L. bulgaricus) to produce Vitamin A (or an equivalent retinoid or carotenoid): a "golden yogurt" scheme like Vitamin A producing golden rice [1]. But, they weren't having any of it.
This could be useful for the 670,000 children who die [2] and 250,000 to 500,000 children who go blind from Vitamin A deficiency [unsourced].
The yogurt could be produced from dairy and plant stock (which is presumably easier for subsistence farmers to procure).
I had the good fortune of switching careers into software, so I don't have the wherewithal to do this myself.
If you happen to have time, I think you could produce pretty good results from engineering a Vitamin A producing strain.
[1] http://www.goldenrice.org/ [2] http://www.thelancet.com/journals/lancet/article/PIIS0140-67...
These guys are working on a more ambitious scheme to engineer bacteria to produce vitamin A in intestines: http://link.springer.com/article/10.1007/s12602-013-9133-3
Shame, but understandable.
With 20n, we had to tread more carefully. As monocasa implied, open source here means either code, or dna data. While code stays on the computing platform, dna's platform is every living entity.
Lets assume you could predict precisely what a piece of DNA will do -- we are not there yet, but will be at some time in the future. Are you advocating that all of that should be freely available? There are lots of benefits to open source and we understand a lot of it in the context of code, but IMHO, we will need to discuss more before we fully comprehend what it means to open source dna.
There are tons of opportunities, e.g., enabling bioproduction, and hopefully the beneficial nature of that (cheaper/carbon negative/environmentally-friendly) will be the leading argument in the conversation than anything else.
On the wetware side, there is of course a similar incentive to keep the genetic sequences that result from your work private, in addition to perhaps ethical concerns regarding the dissemination of arbitrary DNA.
As a fellow software guy considering going into drug development research though, I do wonder: Given your mentioned open source credentials, it's probably plausible that you are at least sceptical about software patents? If so, what is your opinion on patents on drug molecules and DNA sequences? I'm struggling a bit with this since the only way to a big exit seems to involve protecting any findings as IP (because of the costs of the regulatory process), but I'm not sure I'd want my work to be patent-walled.
Do you know what you would do if you are one day faced with the choice of whether or not to patent one of your molecules or sequences, knowing that not doing so would have significant adverse financial effects on your company?
(This all may sound somewhat critical, but I'm really just curious. My email is in my profile if you prefer to respond privately.)
EDIT: after checking the paper, it looks like he qualified himself far better than I remembered. Maybe I was thinking of a different paper? In any case the lower-left part of the last page is a good partial list of reasons why reverse engineering DNA is not trivial.
[1] http://web.iitd.ac.in/~nkurur/2012-13/IIsem/cyl110/protfoldp...
What is the possibility of an engineered organism escaping into the environment and over producing a beneficial substance, insulin, acetaminophen, Lysergic acid, etc.
[0] http://www.cracked.com/article_18503_how-biotech-company-alm...
Indeed. We like our simple to manipulate (relatively speaking) microbes, but would be happy to see what avenues there are in other customer domains.
Most of the time changes do nothing! But if our models are accurate enough, the engineering can be predictable. We are continuously improving our models -- from more data mining, and from feedback from experiments.
That said, what our algorithm predicts right now is still a huge space (5000-10000 products). That is the tree image in the article. For context, the chemical industry centers around 70,000 products.