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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).

Speaker bio

Professor Stephen Muggleton holds the a Royal Academy of Engineering Research Chair (2007-) and is Director of the Imperial College Computational Bioinformatics Centre (2001-) (www.doc.ic.ac.uk/bioinformatics) and Director of Modelling for the Imperial College Centre for Integrated Systems Biology. Prof. Muggleton's career has concentrated on the development of theory, implementations and applications of Machine Learning, particularly in the field of Inductive Logic Programming.  Over the last decade he has collaborated increasingly with biological colleagues, in particular Prof Mike Sternberg, on applications of Machine Learning to Biological prediction tasks. These tasks have included the determination of protein structure, the activity of drugs and toxins and the assignment of gene function. Previous posts were as Professor of Machine Learning at the Computer Science Department, University of York (1997-2001); Reader in Machine Learning and Research Fellow at Wolfson College Oxford (1993-1997); EPSRC Advanced Research Fellow (1993-1997); Visiting Associate Professor (Fujitsu Chair) at the University of Tokyo.  EPSRC Post-doctoral Fellow and Turing Institute Fellow (1987-1992); PhD in Artificial Intelligence Edinburgh University (1986); BSc in Computer Science Edinburgh University (1983). Professional positions: Fellow of the American Association for Artificial Intelligence (2002-), Editor-in-Chief of the Machine Intelligence series; panel member for the DTI Functional Genomics inintiative (2002-2005) and the BBSRC EBI Committee (2004-2006). 

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