Calling future leaders in Synthetic Biology

SynBio LeAP: Synthetic Biology Leadership Accelerator Program
October 1-5th, 2012
Airlie Center, near Washington, DC

• Do you have great ideas for advancing synthetic biology in the public interest?

• Do you want time, tools and partners to develop your ideas into action?

• Do you want to build a community working to best advance biotechnology?

Then join us as one of twenty emerging leaders who will spend a week developing plans for how they – and others – can best advance synthetic biology for the public good.

• Work with your peers, a professional creative facilitation team, and guest experts across sectors in biotechnology.

• Explore frameworks for assessing how biotechnologies can create public value.

• Develop leadership skills for engaging across diverse organizational contexts shaping biotechnology.

• Create actionable plans for mobilizing your ideas for best advancing synthetic biology.
Share your plans with individuals and organizations that can support your goals beyond LeAP.

• Relax in Airlie’s beautiful grounds, enjoy great food and drink, network, and benefit from focused time to develop your ideas.

LeAP is shaped by your ideas and goals. If you want to lead a great future for – and through – synthetic biology, LeAP with us. We welcome participants across career stages, disciplines and sectors.

LeAP participation is fully sponsored by an open consortium of community funders and organizers, including the NSF, Alfred P. Sloan Foundation, SynBERC, BioBricks Foundation, and the Woodrow Wilson Center’s Synthetic Biology Project. If you or your organization is interested in supporting LeAP, please contact us.

Applications are now being accepted on a rolling basis. Limited spots are available and will fill up soon. Tell your friends and don’t wait to apply!

More information: synbioleap.org
Contact us: info AT synbioleap.org
Spread the word: #synbioLEAP

If I understand it, can I build it?

It depends on your definition of “understand”… and possibly your definition of “build”.  Thanks to Pam Silver, I’ve belatedly become aware of the CAGEN competition.  CAGEN, which somehow trips off the tongue less elegantly than iGEM (but perhaps I’m just not used to it yet) stands for Critical Assessment of Genetically Engineered Networks, and the competition sets challenges for the synthetic biology community that “if achieved, would imply that significant improvements in the state of the art have been made”.  This year, the challenge is to design a circuit that provides a robust gene response: rapid expression of a fluorescent protein at a controlled level (moving rapidly from 1x to 10x) upon the introduction of a chemical inducer, with minimal variation in expression between cells.  It should work both in E. coli and S. cerevisiae, be sustained over time, and have minimal temperature-dependent variation.  Specifics, including the metrics to be used, are on the Challenge page.

Think you have a design that will work?  You have until June 15 to submit it.

High-probability successes, by design

Boy, it’s hard to get back into the rhythm of blogging once you stop.  It’s been a busy few weeks — if you read the Initiative in Systems Pharmacology post you know a little bit about why, but also there have been a number of grant and fellowship deadlines, and on top of that we’re recruiting this year.  In short, the day job has been taking up (even) more of the evening than it usually does.  I like to be busy, but there is such a thing as going too far. However, somewhat to my surprise, I find myself missing blogging — the rest of my job doesn’t require me to read papers and think about them, so thinking about science can fall by the wayside if I’m not careful.  In some ways it’s like missing the pain from a nagging tooth, but any kind of absence can make the heart grow fonder.  (Have you missed me?)

So, to get back into the swing of things, here’s a paper that I read a while ago but never finished writing about (Barnes et al. 2011. Bayesian design of synthetic biological systems, PNAS doi.10.1073/pnas.101792108).  It deals with ways to do a better job of designing biological systems.  A dominant argument in synthetic biology has been that the job of synthetic biologists is to make biology more modular, to take inspiration from the standardization of engineering parts such as screws and nuts and bolts (which were once wildly varied, but now have standard sizes and screw threads) and attempt to develop similar standardizations of biological parts.  This direction has had some successes, but it’s clear that there are challenges; and the challenges loom ever larger as the system one is trying to design becomes more complex.   Biology is implemented in probabilistic chemical reactions, not cold steel, and the analogy of mechanical engineering can only take us so far.  And so Barnes et al. argue that we should pay more attention to tools from statistics, specifically to Bayesian analysis.

Here’s their argument, which personally I find quite compelling.  Bayesian analysis is used in biology to try to pull network information out of large, noisy data sets.  The general idea is that, given observed data,  Bayesian analysis allows you to infer a range of possible network structures that are consistent with the data.  More importantly, it gives you a rigorous way of ranking how likely it is that a given network produced the data you observed.  (For a slightly less general idea, see this nice Primer by Sean Eddy — you can also find it here).  Tellingly, we call this “reverse engineering”.  The process of designing a system to produce the output you want could be viewed as the reverse of this (“forward engineering” or just “engineering”, perhaps?).  You can define the output you want to see in response to a signal, pretend that you just collected a dataset with the desired characteristics, and ask what kind of network might be able to produce those data.  You could even add experimental error to your pretend data, though that might seem perverse.  What you would get out of this exercise would be a rank-ordered list of network designs that could have produced the data you “observed”, in order of probability: in other words, a list of designs that can give you the output you desire, ranked according to how easy it should be to get the desired result.  Seems useful, no? Barnes et al. comment that “[t]he ability to model how a natural or synthetic system will perform under controlled conditions must in fact be seen as one of the hallmarks of success of the integrated approaches taken by systems or synthetic biology”.

Designed by iGEM: implemented by nature

I’ve been thinking recently about this year’s iGEM Jamboree, which is coming up soon. For those of you who don’t know, iGEM, the international Genetically Engineered Machines competition, challenges undergraduate students and high school students to make useful machines out of biological parts and implement them in living cells. The ideas are always interesting — usually somewhere between creative and wild, actually — and the Jamboree is where the different teams (165 of them this year) share their results, celebrate the new parts they’ve characterized, and generally have a good time. iGEM has turned out to be a major way for students from engineering and the quantitative sciences to get their first taste of biology.

iGEMmers are always on the lookout for biological modules that can be re-used for other purposes, and quorum sensing is something of a favorite. The system that produces bacterial gas vesicles that allow bacteria to float also seems to be ripe for re-engineering. And so a recent paper that identifies a gas vesicle system controlled by quorum sensing caught my eye (Ramsay et al. 2011. A quorum-sensing molecule acts as a morphogen controlling gas vesicle organelle biogenesis and adaptive flotation in an enterobacterium. PNAS doi:10.1073/pnas.1109169108). Ooh, I thought — that looks interesting. You could target bacteria to something you want to float up — the Titanic, say — and turn on the gas vesicles when you have enough bacteria. And indeed, it’s a natural iGEM project; so much so, that the 2008 Kyoto team already tried to do it. They did not, in fact, raise the Titanic, but they did show [pdf] that their engineered bacteria could move a ~10µm bead. One must start somewhere.

Many bacteria produce gas vesicles to regulate their buoyancy, but we don’t know all that much about how the production of these vesicles is regulated.  Ramsay et al.’s paper is the first to show that quorum sensing can control the production of these vesicles in nature.  Changes in the availability of light or oxygen have also been shown to increase vesicle production in  some cases. Thus, it’s thought that the vesicle-producing cells may turn up the gas when they find themselves drifting too far away from the air-water interface. It’s an alternative to turning on flagellum formation (which would allow swimming towards the surface), and under some circumstances appears to be a more energetically favorable option.

More on the photosynthetic fish

As I’ve mentioned before, the Silver lab (with help from the Megason lab) have been working on a quirky-but-cool project to induce zebrafish to accept cyanobacteria as an intracellular symbiont.  And stage one has gone surprisingly well: as Christina Agapakis points out in her blog post on the project, when they injected millions of cyanobacteria into a single-cell zebrafish embryo “the biggest surprise was that nothing happened. The embryos developed normally into a happy, swimming fish when we injected them with cyanobacteria”.  And the cyanobacteria survive too.  In contrast, if fish embryos are injected with E. coli, the embryo dies within 2 hours.

Precision tools for DNA editing

Do you long for an easier way to manipulate genomes?  Better methods may be on their way.  In some organisms, such as yeast, it’s relatively easy to introduce or remove specific genes.  In others, it’s tedious and difficult.  Recently, new methods of “genome editing” have been emerging that promise to broaden our ability to manipulate specific genes.  These methods rely on the ability to create a double-strand break at a specific location within a genome; once you’ve managed to introduce a break, you can either let it be repaired and look for mutants in which the repair was performed incorrectly, leading to disruption of the targeted gene.  Alternatively, you can introduce DNA that includes an area of homology to the broken area and look for recombination events, allowing gene repair, gene addition, or specific gene tagging.  But how do you specifically introduce a double-stranded break into the genome just where you want it?

In the last decade or so, it’s become possible to create engineered nucleases that use zinc fingers to target specific genomic sequences.  An individual zinc finger binds specifically to a three-base-pair DNA sequence, and by stringing several different fingers together you can make a protein that will recognize a much longer sequence.  Once you’ve assembled 8 or 9 zinc fingers, the recognition sequence is long enough that there’s a pretty good chance there will be only one such sequence in the genome.  This approach has been used in quite a large number of model organisms that don’t otherwise have excellent genetic tools, and it seems to work quite well; there are even clinical trials of various applications of the nucleases, for example using them to knock out one of the HIV co-receptors in T cells.  But, the tricky bit is getting the zinc fingers to target the sequence you’re interested in.  There’s a fairly large set of zinc fingers that target specific 3-base-pair sequences, but it’s not a perfect list.

Pam Silver pointed out a recent paper that takes a new approach, using a DNA-binding motif from the TALE proteins (transcriptional-activator-like effector proteins) of Xanthomonas bacteria (Miller et al. A TALE nuclease architecture for efficient genome editing.  Nat. Gen. 29 143-148).  The tale of the TALEs [sorry, I was trying not to do that] is interesting.  The Xanthomonas bacteria are plant pathogens, and they use the TALEs to activate specific genes in their hosts to maximize the susceptibility of the host to infection. Unlike the zinc fingers, the DNA-binding domains of these proteins appear to bind specifically to just one base pair — so in principle you only need 4 different kinds to be able to target anything you want in the genome. (Mind you, you’ll need to string a lot of domains together).  The specificity of the ~34 amino acid binding domain is determined by just two amino acids, making it very easy to change which base pair an individual domain binds to.  One can imagine that a plant being targeted by a TALE-expressing pathogen would be under strong selection for mutations that change the TALE target sequence; perhaps the reason that this particular DNA-binding structure evolved was to make it easy for the pathogen to respond by changing the TALE to match the new sequence.  [My mind is slightly boggled by the structural implications of a series of 34-amino acid domains that each recognizes a single base pair — especially since TALEs apparently work as dimers.  I’ll be fascinated to see the structure once it comes along.]  In any case, the modularity and editability of the TALE DNA-binding domain is a boon to would-be gene engineers.

Events have been moving rapidly in this field: the TALE code was deciphered just over a year ago by two groups simultaneously, and the first brief report of specific TALE-based nucleases came about 6 months ago; this paper now shows that such nucleases can be used to modify endogenous human genes.

From bench to barn

Has synthetic biology ever gone further than this?  If so, I’m not aware of it.  A recent paper (Kemmer et al. 2010. A designer network coordinating bovine artificial insemination by ovulation-triggered release of implanted sperms. J. Controlled Release PMID: 21108977) starts by engineering a simple hormone-response circuit and ends with pregnant cows.

First, a little motivation.  Huge industries (milk, cheese, beef) depend, basically, on getting cows pregnant.  But successfully inseminating a cow can be quite tricky, because there’s no easy way to know exactly when a cow is ovulating.  Since the oocyte isn’t available for fertilization for very long, and sperms are also fairly fragile, it’s easy to miss the perfect moment.  There have been attempts to encapsulate sperm in protective coats to extend their lifespan, but — although this does seem to preserve the viability of the sperm — the challenge of releasing them at the right time remains.

Bacterial electricity

What is electricity?  It’s moving electrons.  Every living thing moves electrons around, not just in nerves (for those of us that have them) but also in metabolism (oxidize one thing, reduce another). Is it possible to use this metabolic electricity to communicate with man-made devices?  If you could, you might be able to make very sensitive biosensors, or even use bacteria to charge your batteries.  The first question you would need to address is whether you could get the electrons generated by metabolism out to the surface of the cell where they could be captured by a metal electrode.

News on transgenic silks

This just in from Oscillator:

There was some big news yesterday in transgenic silk from Notre Dame and the University of Wyoming, where scientists have genetically engineered silkworms to produce silk that is a mixture of spider silk and the regular silkworm stuff.

Now read on…

see also yesterday’s post for a recently discovered extra-extra-strong spider silk.

Spider silk: the new Kevlar?

A recent article in PLoS One (Agnarsson et al. 2010. Bioprospecting finds the toughest biological material: extraordinary silk from a giant riverine orb spider. PLoS One 5 e11234 doi:10.1371/journal.pone.0011234) claims to have discovered the toughest known biomaterial: silk from “Darwin’s bark spider”, Caerostris darwini.  The reason they looked at the properties of silk from this particular spider was the extraordinary size and shape of the webs it builds.  C. darwini likes to build its webs across streams, rivers and even small lakes, and this means that the lines supporting the webs have to be long enough to bridge from one bank to another.  The authors measured bridgelines 10-14 meters long, and webs of 2.8 m(2).  [How the spider gets across the river in the first place is an area of active investigation.]