Stanford phd candidate Calvin schmidt posts about investing opportunities in synthetic biology. Available for consulting on potential investments in this area.

Most popular synthetic biology technologies at SEED 2017

Late last June, synthetic biology researchers and companies gathered from all over the world in Vancouver to share their work at SEED (Synthetic Biology: Engineering, Evolution & Design) 2017. Their presentations and posters brought to light the amazing progress that has been made in technologies that enable the engineering of biological systems. To show which techniques are receiving the most attention, I analyzed the most popular words in the list of posters presented at SEED:

Engineer: 41
Gene: 30
Synthetic: 29
Protein: 19
System: 16
Product: 16
Cell: 14
Design: 12
Biology: 12
Biosensor: 12

Most of these are fairly generic, but there are some that stand out and give some information on the types of technologies that are advancing.

System and Design

The first example of synthetic biology, Cohen and Boyer’s production of insulin, utilized just one recombinant gene. It has been 40 years since that development, and most synthetic biology systems are not much more complicated. It is difficult to engineer a complicated biological system, but researchers are working hard at making the design and implementation of these complex systems easier. Coral: Python Abstractions for Genetic Design and DNA Construction Using DIVA (Design, Implementation, Validation Automation): Wet-Lab Workflows and Software Platform from University of Washington and JBEI are useful for design and construction at the systems level. In addition, David Baker at University of Washington had a whole talk on the efforts of his group to design novel proteins for a variety of uses. As our ability to engineer biology in a more reliable manner improves, we will be able to create more complex and useful systems.


Programming biological systems requires that these systems be able to sense and respond to their immediate environment. This requires biosensors: proteins and nucleic acids that bind to compounds of interest with high sensitivity and specificity and transduce that binding into a change in gene activity. These biosensors often take the form of allosteric transcription factors or riboswitches and most are derived from existing natural sensors. To enable sensing of unique compounds of interest, researchers (myself included) are engineering custom-made, novel biosensor systems.

In addition to new methods for developing biosensors, many groups showed the different ways in which biosensors can be implemented to create useful biological systems. For the engineering of biology, a group from the University of British Columbia used biosensors to screen enzymes for lignin-digesting enzymes. Alternatively, a group from Amyris displayed results from their efforts to synthesize farnesene, in which a maltose-sensitive biosensor improved the stability of their strain by repressing synthesis during growth phase. This sensor kept the farnesene-producing strain from being outcompeted by mutant strains which had lost the ability to produce farnesene. Biosensors are invaluable tools for those looking to engineer biology, and I am excited to see increased interest in this area.

Takeaways from SynBioBeta SF 2017

Perceptions of cellular agriculture: a New Harvest report