Tuesday 17 March 2015

BioMaths Colloquia - 20/03/2015

BioMaths Colloquium Series - 2014/15

20 March 2015 - 3pm Maths Seminar Room 

(room 224 Talbot Building 2nd floor)

Estimation and control of cell populations

Prof John Lygeros

from Ruess et al (2013) DOI: 10.1098/rsif.2013.0588

Our BioMaths Colloquium series continues this week with a talk by Prof John Lygeros, head of the Automatic Control Laboratory at ETH Zurich. John has broad interests in modelling, analysis, and control of hierarchical, hybrid, and stochastic systems, with applications to biochemical networks, automated highway systems, air traffic management, power grids and camera networks. John started his education with a B.Eng. degree in electrical engineering and an M.Sc. degree in Systems Control, both at Imperial College of Science Technology and Medicine, London, U.K., followed by a Ph.D. degree from the Electrical Engineering and Computer Sciences Department, University of California, Berkeley in 1996. 

After a series of research appointments in the US, incl. the National Automated Highway Systems Consortium, Berkeley, the Laboratory for Computer Science, M.I.T., and the Electrical Engineering and Computer Sciences Department at U.C. Berkeley, John moved back to the UK in 2000 as  University Lecturer at the Department of Engineering, University of Cambridge, and a Fellow of Churchill College. In 2003 he moved to Greece as an Assistant Professor at the Department of Electrical and Computer Engineering, University of Patras, and in 2006 he joined the Automatic Control Laboratory at ETH Zurich,as an Associate Professor, Full Professor since 2010.


Feedback mechanisms are at the heart of many cell functions, including genetic regulation. Engineering such mechanisms in living cells synthetically has proved to be a challenging task, however. This is partly due to the difficulties associated with making the process robust against cell-to-cell variability. 

We discuss how stochastic analysis and control methods can be used to shed light into the uncertainty affecting these systems. We first investigate how modelling and analysis methods can be extended to account for stochasticity not only at the level of individual cells, but also at the level of cell to cell variability. We then use the resulting models for system identification, optimal experiment design for strain characterization, and ultimately regulation of gene expression. 

We demonstrate the methodological contributions by experimental results on a yeast strain, using a light-sensitive transcription factor as the input and flow cytometry measurements of the resulting fluorescent protein as the output.

The discussions will continue over biscuits and tea/coffee after the seminar. 
Hope to see many of you!

from: http://control.ee.ethz.ch/~atc/research.php#model

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