How fresh are your bananas? An exploration of functionality in biological computing

A recent conversation got me thinking about how to explain quantification of "function" in biological circuits.  This is a critical issue and one of the key inhibitors to engineering complex biological systems, but it's not easy to explain because it involves a lot of technical electrical engineering / computer science concepts like signal-to-noise ratio, input/output transfer curves, non-linear amplification and threshold matching.  I think, however, that there may be nice biological metaphor that can make this concept easier to understand.

You see, a biological computing device is like a banana.

Let's say that I want something to eat, so I go into my kitchen and find a banana.  Is my banana "functional" as food? Well, it's very important for food to be fresh and healthy, so I'd better make sure my banana is fresh. But just how fresh is "fresh enough"?  Let us consider some different ways that my banana might look:

A spectrum of banana freshness (credits: yellow, spotted, brown, rotted)

The banana on the left is beautiful: no question that it's fresh, and indeed so perfect that I am sure that my three-year-old daughter would eat it without the slightest protest.

The second banana is getting on in age and starting to develop spots.  It might be a bit mushy inside, and my daughter will definitely not eat it, but I'd be happy enough to chow down.

The third banana probably won't taste good to eat on its own, but it's just perfect for making banana bread or other recipes that transform a banana from centerpiece to simply tasty flavoring.

And as for the fourth... I don't think I'd even want to feed that melted mess to livestock.

As you can probably see, the notion of a "fresh banana" is not a fixed concept, but a spectrum, and "fresh enough" depends entirely on what exactly we want to do with that banana.

Biological computing devices are the same way: a device has to be very high-performance in multiple dimensions (uniform yellow) to be safe and useful for complex circuits or applications like precision medical therapy, while simpler and less safety-critical circuits can tolerate some problems (spotted yellow), some applications just need a nudge or two in the right direction (brown), and some devices probably aren't good for anything at all (rotten).

Right now, the vast majority of our available biological devices are metaphorical brown bananas, with just a few spotted bananas available.  Understanding that fact is the first step, and getting on with building some fresher bananas is the second.

A week on the computer science / synthetic biology interface

Back in August, I spent a week in Seattle playing several different roles at the interface between computer science and synthetic biology.  It was all built around a conference that I've been involved with and participating in for a number of years now, the International Workshop on Bio-Design Automation (IWBDA) (amusingly, given my now geographic location, it has only recently displaced the Iowa Wholesale Beer Distributors Association as the top Google hit for that acronym).

This past year, I was the Publication Chair for IWBDA, which meant I ran herd on the paper submission and peer review process, making sure that we actually get clear judgement, as unbiased as possible, of which submissions are strong scientific contributions worthy of putting on stage as talks. In the end, I think we had a quite strong program, with some very exciting results from a lot of good groups (I gave a talk of my own as well, on circuit design using signal to noise ratio, which I will leave to others to judge), and I'm hoping the associated special journal issue will come out strongly as well.

In addition to IWBDA, two other events attached meetings, taking advantage of their overlap in interests with the IWBDA community.  Just before IWBDA (and, in fact, part of its "pre-conference" schedule) was the SBOL community meeting, aiming to disseminate information and support adoption of our data exchange standards for biological designs, as well as to get more input from more different groups into its development.  In that meeting, in my role as an editor of SBOL---one of the community's elected leadership---I presented some material and helped to facilitate discussion and organize plans for the community.  

Before that was a two-day meeting of the SemiSynBio Roadmap project, an effort sponsored by the semiconductor industry, which has gotten keenly interested in synthetic biology as a possible direction of expansion as Moore's law winds down, and is trying to identify the key directions of research that can set up a similar exponential expansion of capabilities and markets in its relationship with the biological world.  It's fascinating, unclear whether it will turn out meaningful or merely hopeful, and I sit on the Executive Committee of this project, trying to help ensure that we end up with a clear and productive vision out of the several working groups studying different aspects of that interface, an invited position that doesn't fit cleanly into any of my well-defined job responsibilities and yet is clearly a good use of my time and effort as a scientist.

In between, in the corners of my time, I pursued yet other pieces of my scientific life on the interface, including standards development with NIST, the 2015 iGEM interlab study, and various relationships and collaborations with other interesting characters who I enjoy and who live in similarly strange niches to myself.

Interesting things happen at interfaces, both in physics and in society, and scientific communities are no different.  It's an uncomfortable and delicate place to stand, when you're not really at the heart of any of the communities that you're trying to participate in and affect, but it also feels like home to me.

An Introduction to SBOL Visual

Following up on our recent publication on SBOL Visual, an important next step is to make it nice and easy for anybody and everybody who wants to illustrate a genetic construct to do so using SBOL Visual.

To that end, I've now posted "Introduction to SBOL Visual," a short set of slides intended to give all you need to know about making genetic construct diagrams in one simple and easy to digest package.

Please share, enjoy, and send feedback on adjustments that you think would improve the document!

Download [673.14 KB]

Beginning an ambitious (NSF) Expedition: Evolvable Living Computing

Today the official announcement is out and I can finally talk openly about what I've known for nearly two months now: we are beginning a major new synthetic biology project in partnership with Boston University and MIT, led by Doug Densmore over at BU.  "Evolvable Living Computing" is an NSF Expeditions in Computing project, a class of large-scale long-term basic research project that puts $10 million dollars into focused research on a deep challenge.

In the case of our project, that challenge is the computational side of synthetic biology.  Our goal, over the next few years, is to create the intellectual and scientific foundations for truly general computation in biological organisms.  Computation has long been an important goal in synthetic biology, dating back at least to the 1997 "Cellular Gate Technology" paper by Tom Knight and Gerry Sussman.  This vision has been elusive, however, partly because much of the funding available has been heavily focused on applications rather than foundations, and partly because we are only now beginning to overcome barriers to effective design design methods and develop metrics that accurately assess the computational power of biological devices.

In this project, we will be tackling those issues head-on, directly tackling the questions of performance, limitations, and scope of various biological computation models.  BBN's anticipated role in the project is foundations for the foundations, and I am excited to be funded on these works:

• First, I am in charge of connecting the work in the project to ongoing standards development efforts, including design representation with Synthetic Biology Open Language (SBOL) and measurement protocols being developed under the auspices of the NIST Synthetic Biology Standards Consortium.  
• Second, I will be working to make our flow cytometry analytics methods more widely available and effective, first within the project and then to the broader community as well.
• Finally, I will be extending the modeling, analysis, and design methods that we have developed for doing high-precision predictions and quantification of device performance, and creating libraries of high-performance biological computing devices.

I expect that this will be an exciting several years indeed, and hope to build off this foundation in many new directions and collaborations.

Science is an endless sequence of paths not taken

In the turning of the year, I have been going over old records, catching up on the neglected aspects of my scientific, family, and personal life.  One of the things I've spent time doing in this process was going over the past several years of my calendar, trips, and other records as I updated the list of invited talks I've given.  Yes, it's a bit of a mindless thing to do, and rather dry on record-keeping, but walking through all those dates and records produced an interesting set of observations as a side effect for me.

Month by month, year by year, my calendar is filled with paths not taken.  Collaboration discussions that were pleasant and interesting, but ultimately went nowhere.  Pilot projects begun, carried through to a useful starting point, but then never moving forward past that first beginning. Discussions about funding, white-papers and proposals written, most never leading to a funded project.  And yet, I did not feel a failure: woven around and within this set of paths not taken were the strains and threads that have indeed succeeded, grown and prospered and become the basis for the work I now am doing.

Years previous, I certainly did feel like a failure, when I was first starting out on my own to seek funding and hadn't landed any yet.  From the time that I finished my postdoc and joined BBN, my first time to seek funding truly under my own name and not with a professor serving as my PI and responsible adult supervision, it took nearly two years to land my first funding and more than two years to land the first piece of funding with myself as a primary investigator.  Two years of talking with program managers and pitching ideas, writing white-papers and proposals, building collaborations and having them shot down.  When I got those first contracts, I totaled up all of the attempts I'd made before and counted 11 misses before the first hit.  Some of those were cheap misses---a few emails, a couple of hours of research and preparation, a day in Washington to find a missed connection---while others were quite expensive, working with several others to put many weeks of effort into a big proposal that ultimately got turned down.  My record since, I think that I would count as little better, though I hope that I have improved my efficiency in terms of cost per try, if not on number.

Every one of those misses is a path not taken, a might-have-been that usually will never be.  A new and different twist building off my core work, to connect it to a particular opportunity, a particular set of interests of a funder and a place and time in where the science stands. Complementarily, every path that is taken bends the arc of research somewhat: the main themes at the core remain the same, but different applications build infrastructure and results and expertise in different directions, exercise different relationships and strengthen different collaborations, opening up new paths that might have never been able to exist before.

In my CV, I keep a list of projects that I have been funded on.  I do not keep a list of paths not taken. But perhaps I should.  It would be fascinating to know more about the might-have-beens of other scientists as well.  If you spend time talking to a scientist, you'll often start to hear about their paths not taken, especially if you are discussing some sort of possible work together or a new proposal: "We did a little bit of that, but then we ended up going in another direction...", "We started on that, but then the project ended...", "We wrote a proposal to do that, but it wasn't funded...", "We tried that once before and it didn't work, but now I think that the technology is better..."

I don't think that we teach this aspect of the life in graduate school enough.  The myths of science speak of only the core of the research, and only in retrospect, when the moral of the tale is clear. But I think false starts and paths not taken are the true tale, an endless sequence of paths not taken: so long as you are a creative and intelligent researcher, you will generate ideas and possibilities faster than you can find time to adequately pursue and to persuade others to sufficiently support them.  It's hard to learn to not take the failures-to-launch personally, however, to not suffer heartbreak from each glorious potential you describe that never comes to pass.  And yet, of course, to succeed you must take the failures seriously, and learn from them, and build your work yet stronger on the pieces that succeed.

Science is an endless sequence of paths not taken, and survival as a scientist means learning to delight in the paths that you can take and to let go of the paths that ultimately turn out not to be available.