Developing Robust Synthetic Biology Designs using a Microfluidic Robot Scientist
Professor Stephen Muggleton ( Imperial College )
Synthetic Biology is an emerging discipline that is providing a conceptual framework for biological engineering based on principles of standardisation, modularity and abstraction. For this approach to achieve the ends of becoming a widely applicable engineering discipline it is critical that the resulting devices are capable of functioning according to a given specification in a robust fashion. This proposal aims to use experimental validation and revision based on the development of a microfluidic robot scientist to support the empirical testing and automatic revision of robust component and device-level designs. The approach will be based on probabilistic and logical hypotheses generated by active machine learning. A previous paper based on the author's design of a Robot Scientist appeared in Nature and was widely reported in the press. The proposed techniques will extend those in the PI's previous publications in which it was demonstrated that the scientific cycle of hypothesis formation, choice of low-expected cost experiments and the conducting of biological experiments could be implemented in a fully automated closed-loop. In the present proposal we plan to develop the use of Chemical Turing machines based on micro-fluidic technology, to allow high-speed (sub-second) turnaround in the cycle of hypothesis formation and testing. If successful such an approach would allow a speed-up of several orders of magnitude compared to the previous technique (previously 24 hour experimental cycle).