The greatest impact is achieved by catalyzing the research of others
Understanding Evolution: Systematic Exploration
PRANCE is a robotic system for the high-throughput directed evolution of biomolecules using synthetic phage-bacteria ecosystems. By systematically varying any or all of the key evolutionary parameters and observing the effects on the evolution of populations in real-time across many replicates, we can better understand natural evolution and how best to generate new molecular tools.
Understanding Evolution: Simulations and Testing
Even PRANCE cannot explore as many experimental variations as we might desire. By simulating a fitness landscape – whether using one of the handful of mapped examples, predicting one via machine-learning, or developing better ways to map them ourselves – we can better understand how molecular evolution occurs and identify superior solutions to evolutionary challenges.
Liquid-handling robots can perform experiments beyond the capabilities of any human... or at least, they could if they were flexibly programmable. We developed PyHamilton, a platform enabling researchers to program their robots in Python, and showed that it lets us trivially maintain and monitor five hundred turbidostats at constant density, simulate migration flows between populations, automate complex operations such as phage plaque assays, and more. We're now collaborating with other groups seeking to adapt PyHamilton to automate stem cell culture.
CRISPR has revolutionized the practice of biotechnology. While the development of new CRISPR-related methods is far from neglected, there are some edge cases that are particularly relevant to evolutionary engineering. Of those, multiplexing is by far the most important. We're currently working to enable massively multiplexed CRISPR targeting to nuclear and cytoplasmic DNA and RNA in microbes, tissue culture, and animal models.