The Biodesign Laboratory pushes the limits of biotechnology for use on Earth and in space. We aim to make bioengineering as robust, quantitative, and predictable as physical engineering. By using modern techniques in automation, machine learning, and synthetic biology, we can surface and interpret large datasets about biological systems to improve engineering outcomes.
Today, computational protein engineering is largely limited to designing rigid proteins, preventing us from understanding and engineering the third of the proteome that is disordered. We will use modern structure design and prediction methods [1] and robotics-accelerated evolution [2] to design and evolve proteins that are structurally dynamic, such as allosteric proteins that can alter their function in response to the environment, or disordered proteins that confer extremophile properties such as tolerance to freeze thaw cycles.
Previously, we have shown that directed evolution occurs more robustly and more rapidly when conducted using robotic feedback-control [2], and that this can be applied to engineer biocontained organisms using genetic code expansion [3,4]. Building on these methods, this PhD project will apply Robotics-Accelerated Evolution to engineer proteins and chassis organisms designed for use in non-Earth environments such as Mars [5].
The candidate will join a recently established lab that focuses on the intersection of synthetic biology, automation, and machine learning. There will be ample opportunities for hands-on-supervision, as required. The exact project will be co-developed with the PhD student during recruitment, and may place a greater emphasis on protein engineering, organism engineering, automation, or machine learning, depending on the student’s experience and interests.
Candidate background
The ideal candidate will have:
- Enthusiasm for scientific questions relevant to protein and organism engineering
- Dedication to a fostering a positive lab atmosphere
- Interest in synthetic biology, automation, or synthetic biology
- Skill and experience with either experimental biology or computational science
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