Guy-Bart Stan

View all speakers
Imperial College London
Stan, Guy-Bart

Dr Stan received his PhD degree in Applied Sciences (Analysis and Control of Nonlinear Dynamical Systems) in March 2005, from the University of Liège, Belgium. In 2005, he worked as Senior Digital Signal Processing Engineer and R&D coordinator at Philips Applied Technologies, Leuven, Belgium. In 2006, he joined the Control Group of the Department of Engineering at the University of Cambridge, UK, being supported by a EU-FP6 IEF Marie-Curie Fellowship and the UK EPSRC. During summer 2008, he was an invited Visiting Scientist at the Laboratory for Information and Decision Systems, MIT, USA.

Since 2009 he is the head of the “Control Engineering Synthetic Biology” group at the Department of Bioengineering and the EPSRC-funded Centre for Synthetic Biology and Innovation. His research interests include the mathematical modelling, analysis and control of biological or technological systems using methods inspired from systems and control theory.

Wed July 10 | 2:00 - 4:00 | Parallel Session
ABSTRACT: Control Engineering Synthetic Biology

In this talk I will give a brief overview of some of the research activities in my group, the “Control Engineering Synthetic Biology” group, where we focus our efforts on developing foundational forward-engineering tools to mathematically model, and rigorously analyse, design and control synthetic gene circuits and cellular metabolism so as to endow engineered cells with novel functionalities. The tools and approaches that we take rely on principles drawn from Robust Optimal Control and Dynamical Systems theory, applied to Synthetic Biology problems.      

Some of the topics covered will include (if time allows): (a) Design of in vivo genetic feedback controllers for automatic robust regulation of branched and unbranched metabolic pathways, and (b) Exogenous data-based optimal feedback control of gene regulatory networks.      

(a) Among Synthetic Biology’s most prominent applications is the manipulation of bacterial metabolism for producing high-value chemicals in diverse sectors such as energy, biomedicine and food technology. In this regard, we are developing foundational tools for the analysis and design of feedback control synthetic biodevices that automatically regulate bacterial metabolism according to pre-defined objectives. Because these feedback controllers are intracellular, they have a great potential for applications where cellular behaviour needs to be controlled without real-time human intervention.       

(b) In the second part of the talk, I will present research results pertaining to the inference of (close-to) optimal feedback control strategies for exogenous control of biological systems (natural or synthetic) directly from input-output measurements, i.e., without the need for identifying a mathematical model of the system’s dynamics a priori. The examples discussed include data-based inference of optimal control strategies for regulation and reference trajectory tracking in synthetic gene regulatory networks such as the toggle-switch or the generalised repressilator.