Synthetic Biology: End-to-End and Also in the Middle

Dear reader, you must not, of course, expect this run of publication posts to extend indefinitely, but for the moment we're hitting pretty well and experiencing a good bit of synchronicity as all my different projects seem to have long gestating papers maturing all at once.

The latest in the series: over the past week, we've had not one but two of our synthetic biology papers accepted for publication, both in ACS Synthetic Biology, as part of a special issue on design tools.

The first, "An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications," is essentially a summary of the key results of the whole TASBE project---the "Tool-Chain for Acceleration of Synthetic Biology Engineering" that I led  last year, with Ron Weiss and Doug Densmore collaborating.  The big result: coming up with a series of models and tools for turning high-level computer programs into DNA for controlling the behavior of cells.  And we made it work, at least for our version of "hello world"---a program that fluoresces one color when it detects a signal molecule, and a different color when it's not there.  Not the most complicated thing in the world, and there's a lot of duct tape and baling wire used to hold the tool-chain together, but it took a lot to sort this much out and it's a good starting point.  Moreover, now that we've showed it's possible, we (and others) can expand and do more and more complicated things---the hardest part is getting anything at all to go reliably from end to end, and now that we've got that, we've got a foundation to build on top of.

The other paper, "Automated Selection of Synthetic Biology Parts for Genetic Regulatory Networks,"
is all the details for one of the new key pieces of the tool-chain, for selecting which DNA parts can actually be used to correctly implement a computation.  This was led by my colleague Fusun Yaman, who worked with the folks over at BU and came up with a nice mapping of the problem onto a graph problem (NP-complete, of course), and a good set of heuristic algorithms for solving it.


I'm quite proud of both of these papers, since I think they're an important milestone in the progress of synthetic biology, and especially of raising the level of abstraction at which it's actually practical to design organisms.  There's a long, long way to go, but we're making progress and (eventually) reporting and publishing it as well.