My second year at SynBioBeta and I must say that this year was an improvement on last years conference. SynBioBeta is truly a conference embracing the bleeding edge of cultural and technological change that is happening in the biological sciences. This is illustrated by the diverse background of participants and attendees: there were biohackers, Open Science proponents, regulators, industry incumbents and FBI. This year featured talks about the new organized framework about regulation in the industry, using machine learning to engineer organisms, and user driven innovation in biology.
Regulators have been scrambling to reframe regulations to be sensitive to the amazing leaps and bounds synthetic biology has made in recent years as well as an unprecedented diversification of end use applications. The regulators are pressured by incumbent industry and GMO lobbyists to reform regulation, but it is the desire of academics and small players for the product and not the process to be regulated. As I see it, when you regulate processes you regulate progress. There are certain processes that should be regulated, but morals should be parsed from utility arguments. We all agree that the current regulations do not meet the need of society or scientific progress. I am interested in hearing your comments on this topic.
Machine learning and data science are now being applied to organism design. This is truly exciting. There has been a disconnect between research, development, and scale up. And this results in wasted time and resources when taking a great proof of concept to the production line. Right now, I would try to maximize the production of a desired product in small scale, where I have the most control over environmental parameters. This control element is not scalable to the same degree and this issue can compromise production. It leads to unforeseen crashes of an ideal organism due to unideal environmental conditions and months of troubleshooting only to find out that I must redesign the organism from scratch. Lame. Machine learning can take preliminary data from the proof of concept stage to create a pipeline for smart design. I could learn that I want to design an organism to produce my product at a little bit lower efficiency but a broader range of tolerance for certain variable environmental parameters. Equally good is that my predictive model can adjust to real time data gathered in development. I can't wait to learn how I can apply this tool to help with my bootstrapped experiments.
User driven biology in the cultural change that is happening at colleges and high schools, hackerspaces and garages. It is the cultural change that I talked about at the conference and the core belief behind this site. Computers were the tool which vitalized our economy and revolutionized our society 30 years ago. When the personal computer was first introduced, however, it was thought that it would be primarily used for personal finance. Synthetic biology is at this stage. Synthetic biology is accessible to anyone, due to new informal places that provide access. Videos online, reagents and equipment from Amazon and ebay, open source journal articles, and public labs like Counter Culture Labs where you can do science with real people. But these first adopters are using synthetic biology for practical things, like medicine and sustainability. This is very much how we used computers at first, but things change. We need to make changes in how we do things on and to our planet. We need to define baselines of our whole ecosystem so we can design it once again for space travel and if we need to on our home planet. We need more researchers, we need more scientists for the innovation we need to further the future of humanity here and everywhere. Go to my project page to see what I am doing with this tool. Synthetic biology is now in your hands. What are you going to do with it?