Bringing an end to synthetic biologys semantic debate

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Tim Gardner, founder of Riffyn, has recently been working with the Synthetic Biology Working Group of the European Commission Scientific Committees to define synthetic biology, assess the risk assessment methodologies, and then describe research areas. I caught up with Gardner for this Radar Podcast episode to talk about the synthetic biology landscape and issues in research and experimentation that hes addressing at Riffyn.

Among the areas of investigation discussed at the EUs Synthetic Biology Working Group was defining synthetic biology. The official definition reads: SynBio is the application of science, technology and engineering to facilitate and accelerate the design, manufacture and/or modification of genetic materials in living organisms. Gardner talked about the significance of the definition:

The operative part there is the design, manufacture, modification of genetic materials in living organisms. Biotechnologies that dont involve genetic manipulation would not be considered synthetic biology, and more or less anything else that is manipulating genetic materials in living organisms is included. Thats important because it gets rid of this semantic debate of, this is synthetic biology, thats synthetic biology, this isnt, thats not, that often crops up when you have, say, a protein engineer talking to someone else who is working on gene circuits, and someone will claim the protein engineer is not a synthetic biologist because theyre not working with parts libraries or modularity or whatnot, and the boundaries between the two are almost indistinguishable from a practical standpoint. Weve wrapped it all together and said, It basically advances in the capabilities of genetic engineering. Thats what synthetic biology is.'

Riffyn is a lab automation company that was born out of Gardners 20 years working in biotech and his frustrations with the experimentation process, and the non-transparency and mythical lore that exists in labs. He explained how his engineering background is informing solutions to these problems:

The problem is theres an oral tradition in labs of how information is kept, transferred, and taught. Its much more like apprenticeship in a blacksmith shop than like a precision engineering and training operation. Its not like that uniformly, it varies, but there is a sense of that. This problem is widespread. I called 50 different labs and companiesThey all had the same problems. Ryffins goal is to bring the solution to problems of noise and experimentation to as many people as we possibly can, and the solution is derived from manufacturing. Manufacturing has been solving this problem for years. It became very well understood and popularized starting in the 1950s in the auto industry and has spread just about every field of manufacturing uses these principles.

Its just quality systems engineering, and its esoteric to the average person in the R&D environment; I wouldnt expect them to understand it, but it doesnt mean its not valuable. Our goal is to figure out how to bring that to a scientist without inflicting the pain of having to spend a whole career understanding it. Thats our challenge: how do you bring the goodness of quality without the constraints that might be applied in a manufacturing site, how do you free them to be creative, exploratory, and reactive to the information that you get in a laboratory environment but also maintain the quality that you get in a very linear manufacturing environment, and I think we can do that with software.

The problem of reproducibility is coming to the forefront of discussions in the scientific community. Gardner said the problem itself isnt difficult and that the solution lies in improved education and a change in culture:

Its not difficult. [It just] doesnt happen by itself. [Experiments are] not going to be reproducible automatically. You have to actually engineer them to be reproducible. What makes it difficult is nobody knows how to do that because theyve never been taught . Its literally like if youve never played basketball before and somebody asks you to dribble a ball and score some baskets, its going to be hard to do. From a technical perspective, obstacles are the lack of blueprints of how experiments are done. Step number one, if you want quality, you have to write down what you did in a way thats unambiguous. Step number two, you have to collect and aggregate all the data on how that performs from a variety of instruments and formats into one place so that you can statistically analyze it across weeks and months and years. That never happens because people basically copy and paste stuff into spreadsheets and manually aggregate it from time to time. Its just too hard with the tool sets that are out there to do that.

Theres also an incentive structure that breeds a culture in science where the only thing thats rewarded is talking about your science, which means it doesnt actually have to work; you just have to be able to present it and talk about it in a way that gets people excited. Theres no incentive, theres no supply chain where you are punished by your customers for producing a product that doesnt work. All you do is get rewarded for more publications in the higher profile journals, and in that world, theres no reason to try to make your experiments reproducible because nobody really has to reproduce them. Theres no peer pressure to deliver. Fortunately, thats changing for a number of reasons, and Im really excited about those changes.

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Bringing an end to synthetic biologys semantic debate