27 posts tagged “biosysbio 2009”
Adam Arkin
UC Berkeley
People have been doing "Old School" synbio for a long time, of course: take corn (which came from Teosinte), dogs. But is selective breeding actually equivalent, in some sense, to "old school" synthetic biology? He argues that they are like synbio because they are human-designed. He further argues that the main difference is that in synbio, you know what you're doing. Non-synthetic biology: artifical introduction of cane toads in Australia, which is a gigantic mess. His point is that the biggest threat to biodiversity and human health is general things that already exist.
So the point of synbio is that it could make things more transparent, efficient, reliable, predictable and safe. How can we reduce the time and improve the reliability of biosynthesis? standardized parts, CAD, methods for quickly assembling parts, etc. But is design scalable? Applications will always have application-specific parts, but there are sets of function common or probable in all applications.
Transcriptional Logics. Why RNA transcripts? There are lots of different shapes, it avoids promoter limitations (physical homogeneity), and many are governed by Watson-Crick base pairing (and therefore designable). You can put multiple attenuators in series. You can also put different antisenses together to make different logic gates.
Protein Logics: Increasing flux through a biosynthetic pathway. Different activities of various enzymes - different turnovers. Loss of substrate through runoff to other pathways. Solution: build a scaffold tolocalize the enzymes and substrates (import from eukaryotes). Then he spent some time describing recombinases and invertase dynamics.
Evolved systems are complex and subtle. Synbio organisms need to deal with the same uncertainity and competition as the existing organisms. Spent some time talking about treating cancer with bacteria. Why do bacteria grow preferentially in tumors? Better nutrient concentrations, reduced immune surveillance, differential growth rates, and differential clearance rates. In humans, the bacteria that have been tried are pathogens, which make you sick, and you needs LOADS of it in the body. There is one that's used for bladder cancer, and has an 85% success rate.
Wednesday Session 3
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
T Ben Yehezkel et al.
Weizmann Institute of Science
When looking at the number of clones needed to sequenced in order to get one error-free molecule, the proportion of perfect molecules decrease exponentially with length. They have an error-correction system that has drastically improved this situation. They don't look for an error-free clone: they look at all of them, and the error-free ones are dispersed randomly among the clones. They PCR'ed out the error-free parts - they get an error-free sequence from looking at low-error clones. But still, cloning is a major bottleneck. So, how will in vitro clonal amplification make lives easier? In contrast with in vivo, it scales well, it automates well, it is 3-4 hours rather than 1-2 days, and it costs much less.
smPCR can integrate into recursive and other construction technologies. However, there are a few challenges. For instance, primer selection is crucial in smPCR. They construct the DNA completely automatically.
Personal Comment: The video of the automatic dna construction was a great addition to the talk.
Wednesday Session 3
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
J A Pachebat et al.
University of Cambridge
HGP finished 2004, and took $300 million. Same method in 2007 for $10 million. However, there is a new generation of techniques that are much cheaper and faster. Very nice hierarchy, or family tree, of sequencing technologies. However, there are 3rd-generation machines on the horizon (2010-ish?). He works with the Solexa (Illumina) sequencer. In terms of cost, this sequencing method is much cheaper (0.000214 pence/base rather than 0.006 for 454 and 1.56 for sanger sequencing).
What is the use of your 2G sequencing? genome re-sequencing, metagenomics, transcriptional regulation, multi-locus amplicon... In genome resequencing you align your reads against a reference genome. This allows you to look for SNPs or indels. Its affordable, but so far has been used mostly on bacteria. Resequencing of small-medium size bacterial genomes are nicely possible. He uses as an example Dictyostelium discoideum, or an amoeba with interesting properties under different circumstances: amoebae -> aggregation -> mound -> slug -> tipped mound -> spores -> back to amoebae. Originally published in 2005 in Nature and original took 5 years. They resequenced it, and looked at a number of lab strains, and sequenced: the AX4 strain specifically together with the DdB parental strain, and a couple of others.
They managed to sequence at leasst 96.9% of the genome in each strain (the sequencing was hard as it is AT rich). They found a number of errors (at least 4000) in the original sanger-sequenced AX4 genome. They did this by id'ing SNPs common to all 3 strains, and then compared things. You can also identify gene duplications. Showed that there was a bias to G/C-rich reads. Coverage improves with the depth of sequencing - the median depth of coverage was 13. What percentage of the genomes are "solexa-resistant"? around 280,504 bp (0.83 %) between AX2, DdB and AX4. However, when look at all 6 strains, this goes down to 0.41%.
WT amoebae eat bacteria - the AX strians derived from DdB, which was grown on a layer of bacteria, and when preparing for genomic dna, not all bacteria was washed away. Because of this, they got a "serendipitous genome" of non-pathogenic Klebsiella.
Each strain has about 4000 SNPs specific to that strain. Depending on mutation rate, do you still have the same strain you started with after a few months?
Is it $1000? Almost. In practice, you get 4-7X coverage for $1100, but the tech is improving fast.
Personal Comment: I agree with Dan Swan and his tweets: I think Dictyostelium discoideum is a great organism, and glad to see it in this talk. A fun talk in general.
Wednesday Session 3
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
Drew Endy
Stanford University, and BioBricks Foundation
Overview: Puzzle related to SB and informing some of his engineering work. Then a ramble through the science of genetics. Last part is a debrief on BioBrick public agreements.
Part 1. If SB is going to scale, we really need to think about the underlying "physics engine", you could do worse than look to Gillespie's work on a well-mixed system. This underlies much of the stochastic systems that underly SB, such as the differentiation of stem cells. A lot of work is based on this idea. Another good system is phage lambda: a phage infects a cell, leading to two outcomes: lysogen + dormancy, or lysing of the cell. If you infect 100 cells with exactly 1 phage molecule each, you get a distribution of behaviour. How is the physics working here? How does an individual cell decide which fate is in store? About 10 years ago, A Arkin took this molecular biology and mapped it to a physics model. From this model it became clear how this variability arises. Can you predetermine what cell fate will occur before lamba infects it? Endy looked into this. They collected different types of cells: both tiny and large (e.g. with the latter, about to divide and with the former just after division). They then scored each cell for the different fates. In the tiny cells, lysogeny is favored 4 to 1, whereas in big cells, lysis is favored 4 to 1. In the end, this is a deterministic model. There might be some discrete transition where certain parts of the cell cycle favor certain fates. They found, however, that there was a continuous distribution of lysis/lysogeny. Further examination found that there was a third, mixed fate. This is that the cell divides before it decides what to do, and the daughter cells will then decide what to do.
They have looked at this process in time, and how it works at the single-cell level. N is a protein made almost immediately upon infection - its activity is not strongly coordinated with cell fate. Cll *is* strongly associated, however. Q protein also studied. In a small bacterium, 100 molecules of repressor are constrained more in the physical sense, so you need 400 of Cro to balance; while in a bigger bacterium there is more space and only 100 Cro are needed. However, this theory may not work as the things may take too long to be built.
Part 2. How much DNA is there on earth? Well, it must be finite. he's not sure about these numbers1E10 tons bacteria (5% DNA)... 5E35 bp on the planet. How long would it take us to sequence it? A conservative estimate - and a little out of date - is about 5E23 months - one mole of months! If current trends hold, a typical RO1 (grant) in 2090 could have: sequence all DNA on earth in the first month of project. :)
If there is a finite amount of dna on the planet, could we finish the science of genetics or SB? If true, could we then finish early? Is genetics bounded? Well, if these three things hold true, perhaps yes: genomes have finite lengths; Fixation of rates of mutants in poopulations are finite; Atrophy rates of functional genetic elements are > 0.
Is the underlying math equal to perturbation design? Take the bacteriophage T7 (references a 1969 paper about it from Virology): in that, 19 genes have been identified by isolating the mutants and expect 10 more. By 1989 the sequence came out, and there were acutally 50 genes. So, mutagenesis and screening only got some of the genes. About 40% of the elements didn't have a function assigned.
Could a biologist fix a radio? Endy's question is: could an engineer fix an evolved radio (see Koza et al.)?
Part 3. Who owns BioFAB? What legal things do we need to do for BioBricks? Patents are slow and expensive, copyright is cheap but does not apply, and various other things have other problems. Therefore they have drafted the BioBrick Public Agreements document. He then showed the actual early draft document. They're trying to create a commons of free parts. Open Technology Platform for BioBricks.
Personal Comments: Best statement from Endy: "Really intelligent design would have documentation." (Not sure if it is his statement, or attributed to someone else).
Wednesday Session 3
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
Piers Millett
Biological Weapons Convention Implementation Support Unit, UN
Biology is inherently dual-use: can be used for beneficial and malignant purposes. Synbio is value neutral - it's the purpose it's put to that determines if it is bad or good. So, the focus of the solution is also on intent. The global ban on malign biology covers intent: covers all biological agent irrespective of how they're made. 10 years were spent on trying re-write the bioweapons ban, but the answer was inconclusive. Can we police every single synbio center in the world? Do we narrow our view somehow (production capacity, research area, funding type). Neither way is satisfactory. Hence, for now, top-down control is not practical at the moment - and wouldn't be until things are stable.
Kofi Annan: "Preventing bioterrorism requires innovative solutions specific to the nature of the threat. Biotechnology is not like nuclear technology ... The approach to fighting the abuse of biotechnology ... will have more in common with measures against cybercrime than with the work to control nuclear proliferation." This approach is user-centric rather than top-down.
In contrast to the top-down, a bottom-up approach is possible but difficult. Security people and biologists need to work together. The BWC is ready to help with information, access to expertise, and more.
Personal Comment: A very engaging speaker who has really nice pacing. On a ligher note, I liked: the audience participation, the videos, the pop culture references (Dr. Evil, Jurassic Park, Spiderman..).
Wednesday Session 2
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
Joyce Tait
ESRC Genomics Network
In her view, genetic engineering is to the 21st century what evolution was to the 20th century. There is a non-linear progression from science to the marketplace. It used to be linear (wait for a product to be ready). Governance and Regulation: presumption of regulation for a novel area of life science: how do gov'ts decide on regulatory approaches? What precedents to they invoke? Will GM crops be the precedent for synbio and where will that lead? Feedback loops: regulation is what makes development expensive; venture capital won't invest without a regulatory system in place.
Upstream engagement promises: promisory agenda from socail scientists; more democratic approach; scientific research will not be adversely affected; citizens will be come more accepting of new tech. Downsides of upstream engagement: most people have better things to do; those who do engage might have an "axe to grind", or may develop concerns that they didn't have before; some research areas will be discouraged; we can't always predict what will come out of basic research happening now - this would be speculation on a very large time scale; even when that information is available we can't predict the products coming from that; most really innovative product developments require combined contributions from more than one area of fundamental science, but we won't know what we are missing; even doing this, you still won't avoid conflict later on or mistakes; can't control what happens privately, so you're only inhibiting public work; we're asking today's citizens to decide for the people in the future; under what circumstances is it legitimate to allow one societal group to foreclose options for others?
These aren't hypothetical situations - it really may block off certain areas of research. Public dialogue - rather than engagement - is an excellent thing. Helps manage expectations. She suggests standards related to public engagement in terms of willingness to listen to alternative vews and not knowlingly presenting biased information to support their views. We need to avoid domination of dialogue by ideological views that are not amenable to negotiation.
Wednesday Session 2
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
C Smolke
Caltech
This talk is more focused on synbio. There are many natural chemicals and materials with useful properties, and it would be great to be able to do things with them. Examples are taxol from pacific yew, codeine and morphine from opium poppies, and butanol from clostridium, spider silk and abalone shell, and the rubber tree. It is much more efficient to get these useful chemicals grown inside a bacterium rather than its natural source. These microbial factories are a useful application area for synbio. Similarly, intelligent therapeutics is another application area for synbio. In IT, two biomarkers together would (via other steps) produce a programmed output. You could link these programs to biosensors, or perform metabolic reprogramming, performed programmed growth and more. The ultimate goal is to be able to engineer systems. These systems generally need to interface with their environment.
Synbio *also* has circuitry, sensors and actuators, just like more traditional forms of engineering has. Foundational technologies (synthesis) -> Engineering Frameworks (standardization and composition) -> Engineered Biological Systems (environment, health and medicine). An information processing control (IPC) molecule would have three functions, as mentioned earlier: sensor, computation (process information from sensor and regulate activity of the actuator), and actuator. There are variety of inputs for sensor (small molecules, proteins, RNA, DNA, metal ions, temperature, pH, etc). The actuator could link to various mechanisms like transcription, translation, degradation, splicing, enzyme activity, complex formation, etc. Key engineering properties to think about are scalability, portability, utility, composability, and reliability.
What type of substrate should we build this IPC systems on? What about RNA synthetic biology? You'd go from RNA parts -> RNA devices -> engineered systems. Experimental frameworks provide general rules for assembling the parts into higher order devices. Then you organize devices into systems, which use in silico design frameworks for programming quantitative device performance. Why RNA? The biology of functional RNAs is one reason: noncoding regulatory RNA pathways are very useful. You can also have RNA sensor elements (aptamers), which bind a wide range of ligands with high specificity and affinity. Thirdly, RNA is a very programmable molecule.
They've developed a number of modular frameworks for assembling RNA devices, and she then gave a good explanation of one of them. In this explanation, she mentions that the transmitter can be modified to achieve desired gate function. The remaining nodes (or points of integration) can be used to assemble devices that exhibit desired information processing operations. A sensor + transmitter + actuator = device. The transmitter component for a buffer gate works via competitive binding between two strands. As the input increases in the cell a particular conformation is favored and gene expression is turned on. An inverter gate is the exact opposite. They wanted to make sure these sorts of frameworks are modular. They can do this by using a different receptor for the sensor to make it responsive to a different molecule.
You can also build higher-order information processing devices using these simpler modular devices. For instance, you might want to separate a gradient of an input signal into discrete parts. Another example would be the processing of multiple inputs, or cooperativity of the inputs.
The first architecture they proposed (SI 1): signal integration within the 3' UTR - multiple devices in series. They can build AND and NOR gates, as well as bandpass signal filters and others. In the output signal filter device, devices result in shifts in basal expression levels and output swing. Independent function is supported by matches to predicted values - the two devices linked in tandem are acting independently.
SI 2: a different type of architecture where signal integration is being performed at a single ribozyme core through both stems. You can make a NAND gate by coupling two inverter gates.
SI 3: Two sensor transmitter components are coupled onto a single ribozyme stem. This allows them to work in series. You can perform signal gain (cooperativity) as well as some gate types. With cooperativity, input A will modulate the second component which allows a second input A to bind to the second component.
Modularity of the actuator domain: using an shRNA switch - this exhibits similar properties to the ribozyme device.
How do we take these components and put them into real applications? One application is immune system therapies, where RNA-based systems offer teh potential for tight, programmable regulation over target protein levels. She had a really nice example of how she used a series of ribozymes to tune t-cell proliferation with RNA signal filters. After you get the right response, you need to create stable cell lines. Showed this working in mice.
Personal Comments: A very clear, very interesting talk on her work. Thanks very much!
Wednesday Session 1
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
O Kahramanogullari et al.
Imperial College London
He works on improving the modelling and inference step. He makes use of SPiM, which is a process algebra by Microsoft. Process algebra is used to study complex reactive systems, and therefore are well-suited to modelling biological systems. They have used this technique to build a process model of Rho GTPases with GDIs (Kahramanogullari et al. 2009 Theoretical Computer Science, in press). They also created a process model for actin polymerisation (Kahramanogullari et al. 2009, Proc of FBTC08, Elsevier). Such structures can be written in process algebra when they would be extremely difficult with differential equation techniques.
Process algebra is very difficult for anyone to use directly. So, they've developed an intuitive language interface for modelling with SPiM. The assumption in this is that biochemical species are stateful entities with connectivity interfaces to other species. Further, a species can have a number of sites through which it interacts with other species, and changes its state as a result of these interactions. So, they allow descriptions of the model in a natural-language-like narrative language. Their tool is available for download from their website: http://www.doc.ic.ac.uk/~ozank/pim.html .
Wednesday Session 1
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
L Dupuy et al.
Scottish Crop Research Institute
Plants are the ideal models for the engineering of synthetic multicellular systems, however there is a need for tools to measure, process and design such systems.
Quantitative analysis of plant multicellular kinematics. The segment cell architectures approach: grow a region of pixels incrementally by raising the intensity of the pixels. The basin (a set of pixels) are initiated at cell centers, and expand when neighbors have a lower intensity. The balloon approach: ballons are initiated at cell centres, there is contact search initialization, and then "physical" inflation of balloons under certain circumstances. What is the application to cell growth kinematics? You can find really clear geometric rules which drive where/when the cell divisions occur.
Integrating molecular and computational tools. Automated analysis of cell growth involves labelling plasma membrane and nucleus simultaneously, which allows combining algorithms, automation of cell search, and facilitates 3d segmentation. Standardization of biological parts on a cell basis includes normalizing gene expression; there is also a ImageJ plugin for automation of ratiometric analyses; and more.
Computational models for tissue growth and development. There are computational tools and molecular tools that can help out. Modelling tissue growth is a multiscale problem. You also have to take into account the mechanics of growth, such as: cells are closed-walled structures maintained in tension by turgor pressure; permanent deformation of cell wall material enables cell expansion; the cell genetic activity influences the cell wall material properties. CellModeller is a tool for data analysis, visualisation, simulation, and segmentation reconstruction. It uses an XML exchange format, a Python interface, and a data structure described in C++. He then described how CellModeller works by giving a trichome pattern system example. Trichomes are root hairs that form on the root. The pattern of these types of cells are not random.
Multicellularity is key for building complexity of a system. Plant systems are ideal for engineering cell-cell interactions. There is a whole group of tools to create models, from bytes to molecules.
Personal Comment: These notes are a bit scattered, as it was just after I gave my talk, and I wasn't completely in zen note-taking mode :) However, there were some great pictures of plant cells and models, and it was a well-structured talk. It was nice to see modelling tools for multiple cells.
Wednesday Session 1
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
Panel is:
Caitlin Cockerton (Chair)
Julian Savulescu
Matthew Harvey
Piers Millet
Drew Endy
Each panelist starts by giving a 10 minute talk.
Drew Endy: An Engineer's Perspective on Synthetic Biology
He's interested in synbio because: work into sustainability, among other things. The basics of genetic engineering hasn't changed in 30+ years. However, synbio equates to a tools revolution. But, do we need to "manage" people who are trying to "hack" the genome in their garage? Could you actually file patents on what's in the biobricks registry? Yes, but expensive. Will there be a cultural synchronization or a continued disconnect in future between genetic engineering researcher and the anti-GMO sections?
Personal Comments: A drawing of Rama (I think) - great sci-fi link! Also, a slick slide presentation with very few words and lots and lots of pictures - I like it.
Matthew Harvey: Synthetic biology and public engagement
Matthew Harvey is the Senior Policy Adviser, Science Policy Centre, Royal Society, UK.
One aspect of public engagement (PE): we shouldn't force people to be engaged if they don't care. Many of the PEs for GM started out adversarial: people assumed that scientists were "automatically" for GM. Unlike GM, there aren't a series of products queueing up to be sold. However, the risk assessment part remains vital. The Woodrow Wilson Center did a tentative PE study about synbio. They found that even with a very low awareness of synbio, 2/3 of adults are willing to express an initial opinion regarding the tradoff between potential benefits and risks. People also had questions way beyond risks and benefits (who what when where how etc). Based on this, institutions have been trying to move the PE upstream, before products were available. This is pitched as social intelligence gathering, and may try to anticipate problems that don't exist yet (for good or ill).
Julian Savulescu: Two Concerns about Synthetic Biology
From the University of Oxford. Benefits are already well-covered, but he wants to raise 2 concerns: synbio poses risk of malevolent use; synbio might undermine the moral use of living things. These concerns can be understood as variants of a common concern about promoting future wrongdoing.
Wrt the first concern, Cello et al in 2002 wrote about the de novo synthesis of poliovirus. Rumpey et al in 2005 reconstructed the 1918 spanish influenza virus. For the second concern, people are worried that synbio will contribute to a feeling that life no longer has a "special status". For a more thorough look, see Cho, Magnus, Caplan and McGee (1999). But where, on the nebulous scale of "moral status" do the products of synbio belong?
A reformulation of the 2nd concern is that: synbio beings are assigned great moral status, which cause a sacrifice of the human/animal status for the sake of the synbios, which could lead to humans/animals being harmed.
Suppose we correctly assign a great moral status to sybios: human/animals could get permissibly harmed. Alternatively, we incorrectly assign this status: humans/animals get wrongly harmed.
Some arguments: scientific inquiriy is justified by the intrinsic value of the knowledge it produces, but this assumes that the value of knowledge trumps other moral values. The second is the gunmakers' defence: a scientist is not responsible for malevolent uses, but wrongs for which we are not responsible can still be relelvant to the ethical assessment of our conduct. Additionally, we can't predict the future, so any principle which requires us to do so is unworkable, but it may well be possible to identify predictors of malevolent use - we haven't even tried.
The two main concerns can be understood as variants of a moral general concern about bringing about wrongdoing. The most popular way of dissolving these concerns - scientific isolationism - fails.
Challenges for regulators: minimise the risk of malevolent use. For scientists: make better predictions about how research will be used. For philosophers: ascertain criteria for moral status, and determine how to weigh risk of future wrongdoing against benefits of pursuing research in synbio.
Personal Comment: I don't agree that an increase in moral status (if that's the way it goes) of synbios would necessarily lead to a drop in the status of humans/animals.
Piers Millet
Personal Comment: Piers generously dropped his talk so that the panel discussion could begin. That was very nice, and very timely, as there's only 15 minutes left and the discussion hadn't started yet! A real shame to miss it, especially since we tantalizingly saw his first slide, a gigantic UN symbol with the words "Biological Weapons Convention Implementation Support Unit" underneath. Made me feel like we were in a secret meeting or something. However, smart move. A tip of the hat to him.
General Discussion
Q: Are there any occasions when a political decision has been needed in terms of prioritization of types of science (including synbio) when upstream PE has been attempted? Matthew isn't aware of any such occasion. Of course, this conference and the community itself is an example of upstream discussions in general.
Q: Comment for Julian: applications in practice aren't always influenced by whether or not it was originally developed for military purposes. (Personal Comment: I believe the example provided was the laser.) Drew mentioned that of course you could spend loads of time thinking about military/non-military applications. It is also good to engage in taking action early, as things are still being figured out. One example is the creation of iGEM as a cooperative community contest, as opposed to creating something more aggressive such as a "bug wars" game. :)
Piers: There are at least two approaches to doing diy bio: one is people doing biology on their kitchen tables, and the other is a community model where you don't expect to have your lab in your house, but you could have a community lab in a central location that can meet regulations and where people can do things. The latter is quite interesting.
Q: is synbio the end of evolution? How does it fit? Drew: evolution is the most successful design framework for biology, but we don't know how to deploy it yet! Can't go forward with existing frameworks for things like patents - would overload the current system.
Overall Personal Comments: The twitter #biosysbio feed has been quite interesting for this section.
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot - just let me know!
