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Progress on development of a microfluidic robot scientist

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If you have a question about this talk, please contact Dr Fabien Petitcolas.

Abstract: Synthetic Biology is an emerging discipline that is providing a conceptual framework for engineering biological devices based on the principles of standardisation, modularity and abstraction. To become a widely applied engineering discipline it will be critical for the newly designed biological devices to function in a robust and predictable fashion. This project is involved in the development of a microfluidic robot scientist that will address these limitations. We areintegrating computational hypotheses with automatic generation of experimental trials within a microfluidic device. The use of droplet microfluidics reduces reagent volumes and increases throughput. The strategy maximises the efficiency of convergence to optimal values as applied to the engineering of proteins with new ligand binding specificities. This approach will enable the design and empirical testing of robust BioPart and device-level Synthetic Biology designs, which will have general applicability. Specifically we will design novel ligand-binding for the LuxR family of bacterial quorum sensing proteins to enable the rational engineering of orthogonal bacterial signalling pathways. The project is being conducted as an interdisciplinary collaboration between computing, bioscience, chemical and bio-engineering at Imperial College London as part of the work of the Institute of Systems and Synthetic Biology (IoSSB).

Biography: Professor Stephen Muggleton holds the a Royal Academy of Engineering and Microsoft Research Chair (2007-) and is Director of the Imperial College Computational Bioinformatics Centre (2001-) ( and Acting Director of 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).

Note: This talk, which is part of a series of 4 presentations that afternoon, will be preceded at 2pm by a brief introduction by Rick Rashid, Senior Vice President, Microsoft Research.

This talk is part of the Microsoft Research Symposium series.

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