Synthetic Biology 4.0 : highlights and reflections

Update: The videos of the talks from Synthetic Biology 4.0 are here !

Around three weeks ago I attended the Synthetic Biology 4.0 meeting in Hong Kong, hosted by the Hong Kong University of Science and Technology. I’ve taken a little time to allow all the new and exciting ideas to sink in. I really enjoyed the meeting, and while it was a little short it was an effective way to quickly sample the current developments in synthetic biology, as it stands.

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Synthetic biology 4.0: reflections on the state of play

Note to the reader: I wrote most of this in October 2008, then revised it a little in January 2010. It never really turned into the insightful and coherent essay I envisioned, but I’ve decided to publish it here anyhow, for posterity.

Some opinion, analysis

This is a blog, so a post like this wouldn’t be complete without some opinionated analysis :). My daily work involves molecular biology, structural biology encompassing some basic protein engineering, and some bioinformatics. I don’t currently practise anything I would consider bona fide synthetic biology, although I’m comfortable with the technology, concepts and language used. Synthetic Biology is a relatively new field (or as some would argue, a relatively new term for the intersection of some established fields), and as a result things are developing rapidly, both on the technology side, and on the ethical/social/legal side. The push to acquire mindshare and funding for exciting but speculative biotechnologies has generated some big claims, and some big expectations.

Modified from Jeremy Kemps version at Wikipedia, used under Creave Commons Attribution-ShareAlike 3.0 license.

Gartner’s hype cycle (modified version used under a CC Share-alike license, 3.0, original image by Jeremy Kemp).

My overall feeling is that we are just passing the peak of inflated expectations. Although to be fair, it’s a little tough to pick a single point on the hype curve for “Synthetic Biology” at this point. It seems like there are lots of “micro-hype curves” already playing out, as different groups use the the new tools that have become accessible to engineer something cool and useful and test their design. Sometimes things work reasonably well, other times it becomes clear that the biological systems that have evolved under natural environmental pressure are not always modular and easily abstracted. That is not to say the once those systems are adopted, tweaked and tested by engineers (potentially via directed evolution), that they will not become predictable, modular and abstractable components; this is the assumption that the success of synthetic biology as an engineering disipline is relying on. Tweaking and characterization of basic components is still a work in progress. The outcome of the ongoing standardization of parts and subsequent predictability of completed devices should reveal just how soon and how deep the impending “Trough of Dissillusionment” will be.

(it should be noted that the common cry from biologist that ‘biology is not aways predictable’ and ‘what about emergent behaviour’ can be simply avoided by not using the parts that don’t behave. This could be a severe limitation, where parts with certain functions are scarce or unavailable due to their unpredictable nature. The same biologists dismissive of the claim that biological components can be abstracted and standardized will turn around and defend their use of GFP / FLAG and HA-tags as a standard method for detecting the location of proteins in cells. Unpredictable behaviour in these systems often occurs, but is often downplayed).

Overheard at the banquet: “I wish people would just stop trying to define it”.

As with any new field in the process of carving out an identity, there have been various attempts to define “Synthetic Biology”, and as expected not everyone agrees. In fact, I get the sense that people working on bona fide synthetic biology projects seem to have become a little sensitive about it, to the point that I half expect to be ‘flamed’ for speculating about the technology hype curve above, and chastised for dredging up the definition debate again (which has probably become pretty stale and repetitious for some).

I think the definition from the NEST High Level Expert Group is reasonable:

‘Synthetic biology is the engineering of biology: the synthesis of complex, biologically based (or inspired) systems, which display functions that do not exist in nature. This engineering perspective may be applied at all levels of the hierarchy of biological structures—from individual molecules to whole cells, tissues and organisms. In essence, synthetic biology will enable the design of ‘biological systems’ in a rational and systematic way’

The key term here is engineering … not as in ‘genetic engineering’ which historically has been more haphazard due to the limitations of the tools available, but engineering based on strict engineering principles. Compared with most other scientists who are using effectively the same technologies and protocols in molecular biology, the ‘synthetic biology engineer’ aims not only to understand the system that they are studying, but pragmatically take well understood biological components and systems and put them to good use.

From large parts of the scientific program from the SB 4.0 conference, you could be lead to believe it was all about metabolic engineering. Or maybe whole genome synthesis, or genetic circuits. The SB 4.0 website defines it as:

Synthetic Biology is a new approach to engineering biology, with an emphasis on technologies to write DNA. Recent advances make the de novo chemical synthesis of long DNA polymers routine and precise. Foundational work, including the standardization of DNA-encoded parts and devices, enables them to be combined to create programs to control cells.

It goes on to list examples of some real-world applications for synthetic biology: BioEnergy, Drug Production, Materials and Medicine (aka programmed cells as therapeutics). All of these examples, other than “Medicine”, ultimately relate to metabolc engineering … heavily tweaking (or entirely rebuilding) a biosynthetic pathway in a host cell, usually E. coli or yeast, to produce something we currently dig up from the ground and transform into fuel, chemicals or materials through the petrochemical industry.

Another angle is Luis Serrano’s distinction between Biotechnology vs. Synthetic Biology:

Thus, improving the production of a certain metabolite by tinkering with some of the components of a metabolic network will fall within the realm of Biotechnology. On the other hand, the introduction of several exogenous enzymes in an organism to produce a new compound will fall within the scope of Synthetic Biology.

I find it hard to agree with this particular division. The introduction of heterologus genes, and the production of a ‘new compound’ seems like a logical extension of advanced metabolic engineering, as a subfield of ‘biotechnology’, and barely warrant reclassification into an entirely new field. Unless you need a name change for marketing purposes …

There is a proven model for attracting interest (and hence funding) to a field by changing the name in response to new possibilities through some enabling technology; think gene sequencing vs. genomics, protein identification vs. proteomics, studying metabolism and cell signalling pathways vs. systems biology. In this case, cheap high-throughput sequencing and relatively cheap DNA synthesis are allowing engineers to do things they couldn’t before. So the biotechnology and genetic engineering of the past becomes … synthetic biology.

Names and definitions aside, (synbio)technology has got the the point where we are able to do some pretty cool things using biological systems … and if a little re-definition helps people acknowledge that, then all the better.

First Online EMBL PhD Symposium

This looks interesting … the First Online EMBL PhD Symposium, a sort of ‘online’ conference for the life sciences. Everybody with a scientific background is invited to participate. Registration is free.

The programme (Career Development Session, Omics Session / Systems Biology, Scientific Communication 2.0 and Participant’s Contributions) and speakers list makes it look sort of like a “Biology 2.0” conference.

Apart from the (possible) IRC sessions, hopefully the fact that everything is stored as video/audio + comments on their content managment system means the ‘inconvenient’ timezone in Australia won’t limit my participation too much.

(via the worldwide bioinformatics cabal :), Neil via Pedro, Roland and Stew)

Combio 2006, last day roundup

Yesterday I breezed into Brisbane for the last day of the Combio 2006 meeting, to catch some talks, and make a showing to accept an award from the ASBMB.

Neal Saunders has been posting summaries of this meeting in Brisbane on his blog, so I thought I’d give my take on the last day too.

Here’s are my highlights:

In David Claphams talk on transient receptor potential (TRP) ion channels, I learnt that menthol feels cold because it binds to an activates a TRP channel involved in cold sensing. Think about that next time you taste that cool minty freshness. (I woke up a 4 am to fly to Brisbane. The brain wasn’t really kicking over just yet).

In the “Molecular Basis of Disease and Drug Design” session, K. Krause gave a very honest and entertaining talk on what he termed his “Night Science”. (“Day Science” is the stuff that works out nicely, shows logical progression with no nasty inconsistencies or loose ends and gets talked about at plenary lectures. “Night Science” is the stuff that doesn’t work out as well as we’d like .. it’s confusing, there are loose ends and inconsistencies, despite carefully doing all appropriate controls. Not to be confused with “Bad Science“). Krause and his group were unlucky enough to find that a lead compound discovered through an in silico screen, which initially appeared to be a great inhibitor of alanine racemase, turned out to in fact be a potent inhibitor of another enzyme in their coupled assay. I wasn’t inhibiting their target well at all (doh!).

There were actually a few examples of some somewhat disturbing results from in silico screens in this session, which I’ve seen similar examples of a few times before. Researchers do an in silico screen, and find some top-ranking hits, one or two of which are also good inhibitors in an assay. The co-crystal structure is solved, and reveals that the compound is not actually binding in anything like the conformation that the computational docking predicted (sometimes not even the same site). What is going on here ? Is it just the fact that in twenty random compounds one will turn out to be a weak inhibitor ? Unlikely, since then high-throughput real-world screens would have a much higher hit rate. Is it that the computational docking is half right, fitting one fragment of the compound which has high affinity well, and the other non-binding or weak binding half doesn’t matter ? Probably more likely, but it still doesn’t explain the cases where the compound binds in a completely unpredicted site. Food for thought: maybe many docking scoring functions for small molecules are good at selecting generally sticky molecules …… (I don’t do this kind of work directly, so I’m really an ignoramus on the issue).

I also went to the “Cancer – Emerging Drug Targets” session. Andrew Scott from the Ludwig Institute for Cancer Research presented some really encouraging results of early clinical trails for an EGFR antibody, and Michelle Haber of the Children’s Cancer Institute Australia presented some results from two cell based assays, where ‘high-throughput’ screens have identified some inhibitors of the N-myc oncogene, and a drug efflux pump (MRP) inhibitor. I’d never really thought about it, but apparently those pesky cancer cells up regulate this efflux channel and actively pump out anti-cancer drugs, in a similar way to some parasites that become multi-drug resistant.

In the final plenary lecture, Nick Proudfoot told us about his work on transcriptional termination. It’s still too early for the textbooks, but it looks like transcriptional terminators bind at the termination site and near the promoter regions in a lot of cases, turning genes into physical ‘loops’. Whether this helps the RNA polymerase jump from the end of a gene straight back to the start to make the next mRNA transcript is still not proven, but it’s an attractive model.

Combio is always a bit of an eclectic mix, but if you take it in the right frame of mind it can be good fun, and a nice way to broaden the scientific horizons a little. Needless to say, I slept like a log after all that.