Getting close to a SynBio StackExchange!

Synthetic Biology StackExchange is one step closer to launching! We've now passed the definition phase for the site, and as soon as we have a critical mass of people committing to participate in the beta, the site will be officially launched. 

I'm very excited about getting this site going, since I think it will be an extremely valuable resource for many thousands of folks working in the area. I know even as an expert, I'm quite naive about a lot of details in the laboratory and issues outside of my own areas of expertise, and really look forward to asking as well as answering questions.

If you're interested too, please go to the site and commit to the beta today!

Come help define a Synthetic Biology StackExchange!

In the actual practice of working in synthetic biology, there are so many pragmatic details that don't get captured well in scientific papers. Right now, they are basically passed around by word of mouth, through oral tradition amongst students in a lab or hallway conversations at conferences. But there is a better way.

Pretty much everybody who programs makes use of StackOverflow, which provides well-curated answers for programming questions. The greater network of StackExchange sites spun off of it provides a great one-stop-shop for lots of other communities as well, from math, physics, and chemistry to travel, cooking, and personal finance. The iGEM Engineering Committee, as part of its educational mission, is trying to do the same for synthetic biology. 

Synthetic biology field is rapidly growing, highly cross-disciplinary (which means none of us can be experts at everything), and we think it could use a good, universal database of questions and answers. It will be good for students learning the field, and also good for professionals who need to know things outside of their personal expertise.  

Right now, we're in the "definition" phase at StackExchange, and need about 100 more people to add example questions and vote for example questions they like. Just follow this link or click on the imagebelow, make an account, and you can start asking and voting too!

From art to engineering in synthetic biology

 Two weeks ago, I gave a talk called "From art to engineering in synthetic biology" at the Build-a-Cell seminar series. This talk pulls together a lot of different threads of research that I've been working on, and there were some really enjoyable questions to answer afterwards, and Build-a-Cell has put the video recording of it up online, so if you're interested I hope you will enjoy and share!

Autonomy in Synthetic Biology

A lot of work is going into laboratory automation and design tools, but how far is it actually getting and what are the real roadblocks? We examine these questions in a new article, "Levels of autonomy in synthetic biology," out today in Molecular Systems Biology.

I'm a big believer that a better toolkit is eventually going to radically transform the way that we engineer biological organisms. And I've certainly preached the gospel of data integration, design tools, reproducibility, etc. But somehow, two decades after the field began, we still find ourselves with lots of barriers to effective deployment of standards and automation to actually increase routine productivity. Way too much is still slow, manual, artisanal. So what's going wrong and what do we need to fix in order to get that transformation into a world of rapid, routine, and reliable engineering?

To understand this, we first developed a six-level framework for analyzing efficacy of automation, analogous to the one used for discussing autonomous vehicles. We don't necessarily need to go crazy-high in autonomy in order to get a lot of benefit. What I really want is at least some good Level 2 scientific "driver assistance" features to help me with the lab equivalents of lane-keeping, checking my blind spot, and parallel parking.

State of the art in autonomy for design-build-test-learn cycle, as shown in our article.

The problem is that right now, there's a bunch of good work being done on specific challenges in the prototypical "design", "build", "test", and "learn" stages of the engineering cycle, but not enough investment in the "glue" of standards that will allow things to connect together between stages or in curation tools that decrease the burden in setting up tools. We know from a number of demonstrations that we can do much better, but the marketplace is still too fragmented and just not enough work has been done on stringing these pieces together yet.

At the same time, I have a lot of hope.  The work we've been doing in partnership with lots of others on the DARPA SD2 program is producing a lot of interesting tools for lowering barriers to curation and making it easier to use automation. I'm also seeing a big push in iGEM, where we've been spreading the word on measurement and engineering methods. And a lot of folks I talk to in government, industry, and around the world all seem to be seeing similar needs and trends, so I hope we're building momentum toward a phase change, and I'm going to see if I can do my part to help.

Check out the full details of our discussion of autonomy in our open access article.

Robust estimation of bacterial cell count from optical density

The iGEM 2018 interlab article is published today in Nature Communications Biology! This article, which I wrote about last October when we posted on bioRxiv, presents a cheap and easy protocol for estimating cell count and per-cell fluorescence on plate readers. This is so cheap (reagents <$1) and easy, as validated by hundreds of iGEM teams around the world, that I believe no paper should ever be accepted again if it has plate reader data in uncalibrated units, any more than we would accept a paper that measured length in cubits.

The core idea is pretty simple: basically, you calibrate against dilutions of silica microspheres with similar size and optical properties to cells. As long as your cells are in liquid culture that's not too opaque and the cells aren't doing anything really odd optically themselves, this should give a good relationship between optical density and cell count.

We validated this by combining our microsphere protocol and previously published fluorescence estimation protocol to get a close match between flow cytometer and plate reader estimates of per-cell fluorescence, which wouldn't work if either of the protocols was problematic.

 

Per-cell fluorescence from flow cytometer and plate reader (article Figure 5)

Ironically, the most challenging part of the whole publication was the author list. All the data was supplied by a lot of iGEMers: our consortium author list included approximately1400 people from about 250 teams all around the world.  I had to write scripts to manage the author list and to format and reformat it as we went back and forth with the journal to figure out how to match their formatting requirements. All said and done, however, I wouldn't have it any other way: I am proud to have been able to work with so many capable collaborators and with so many eager young contributors to synthetic biology.

Plate reader & flow cytometry tutorials

Over the last two weeks, I've given two measurement tutorials in the iGEM Summer Webinar Series, one focused on plate readers and the other focused on flow cytometry. Both are posted in a repository on GitHub, along with example data and code to help people get started with effective calibration and interpretation of these instruments.

• The first tutorial, "Quantifying fluorescence and cell count with plate readers," starts with a general introduction to fluorescence and OD, including a comparison of plate readers and other types of instruments, factors affecting fluorescence, and how to pick colors based on excitation and emission spectra. The second block focuses on calibration of measurements for fluorescence and OD, and on debugging such measurements. Finally, the session ends with a discussion of how to interpret and debug calibrated plate reader data.
• The second tutorial, "Quantifying fluorescence and cell phenotypes with flow cytometry," starts with an introduction to flow cytometry, including how these instruments operate and the types of data that they produce. The second block focuses on calibration of measurements for fluorescence and cell size, and on debugging such measurements. Finally, the session ends with a discussion of how to interpret and debug calibrated flow cytometry data.

Under the hood of a flow cytometer, showing its optical path.

I hope that you will find these useful and redistribute them to others who may find the same!

Closing in on fast, cheap, point-of-care testing for COVID-19

In mid-March, as the COVID-19 pandemic slammed down on America, it just so happened that our group at BBN had just finished sending DARPA a proposal for fast, cheap point-of-care testing for emerging diseases.  So rather than wait for a response on the proposal, we just organized things up ourselves and started working on testing.

The test plan has evolved a bit as we've worked through details, as more information about the virus has emerged and as we've made sure manufacturing will be able to roll these things out at scale. In the end, as has just been announced, it looks like we'll be able to just have people spit out a bit of saliva for the test (no more nasal swabs!) and give accurate answers in less than an hour.

My own role in the project has been on the bioinformatics: the FAST-NA software I've written about here a few times before has been critical for fast and effective design of our detection targets, both ensuring that we will be able to detect all known variants of the virus and that we won't get false positives from other organisms.  And I still love that FAST-NA's core is technology that has been repurposed from hunting for computer viruses to hunting for real ones.

It's not in the field yet, but we're on a good track, and I hope we'll be able to make a real contribution to helping manage the pandemic...

Making biosecurity more agile

Just out in Science, a new article on making biosecurity more agile: "Embrace experimentation in biosecurity governance" is a perspective piece summarizing the position developed at a workshop I attended last summer.  In essence, right now the processes by which our nations and communities deal with biosecurity are slow, political, and isolated. We argue that we all need much better connected and flexible ways to deal with emerging threats in our more connected world, and to manage these processes in a way that makes it easier to study and learn from successes and failures.

We didn't realize this would be so apropos at the time that we were writing this piece, but we're in the thick of this problem right now, and I think this is a good piece to read for anybody who wants to help prevent the next biological disaster.

Guest Post: "Shining Winter"

On a peaceful and relaxing note, I offer this guest post from my daughter Harriet, who has asked me to share this poem on her behalf:

Snow falls from the sky outside the window like if the clouds were dancing to the ground.

Hot cocoa sits by the warm fire place in the quiet, loving room.

In the silent, quiet room lays a sleeping cat.

Next to the cat is a pale brown couch

Inside the fire place lighted, quiet, brown room everything is relaxed.

Next to me lays little, brown, leather book.

Glimmering in the light of the fire lies a shelf of elegant, glittering, glass cups.

When the cat wakes up it climbs on to the couch and purrs satisfyingly.

Innocent silence fills the room again.

Near the fire, I sleep in peace, with a beautiful aroma of candles.

Tenderly, the cat purrs again.

Enjoying the hot cocoa, I pet the cat.

Resting my feet on a pillow, I fall into a deep, relaxing sleep.

Looks like we found something significant in the coronavirus...

It looks like the unique sequences we found in the 2019-nCoV coronavirus were indeed significant!

In this article in last week's Science, the authors found key differences between this virus and SARS, focused most strongly on the N-terminal domain (NTD) and receptor binding domain (RBD) regions of the viruses spike glycoprotein. This is important to understand, because this protein is what the viruses uses to actually infect cells, and also a primary target for antibodies to identify or neutralize the virus.

These regions are also right where we pointed our spotlight in our bioRxiv paper, with the surface glyoprotein region of interest that we identified! In particular, we identified the region from amino acids 9 to 275 as the largest unique sequence, and found it was part of a cluster spanning from amino acids 9 to 883. In the Science paper, the key NTD sequence goes from amino acids 17 - 305, nearly a perfect match to our largest unique sequence, and the RBD sequence goes from amino acids 330 to 521, meaning that together the two cover the majority of our identified cluster!

Now, these folks went a lot deeper than we could (not being protein modelers ourselves), and I'm sure they didn't use our research, given they were likely starting their investigation at the same time we started ours. That said, it's a nice confirmation of our methods and their potential significance to have rapidly and independently identified these regions with our FAST-NA method.

My next question for other researchers, however, is this: what about the other two domains we found?