Imperial College London
Portrait Picture

Guy-Bart Stan

Faculty of Engineering, Department of Bioengineering

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 7594 6375g.stan Website

 
 
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Location

 

B703Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Pan:2016:10.1109/CDC.2016.7798362,
author = {Pan, W and Menolascina, F and Stan, G},
doi = {10.1109/CDC.2016.7798362},
publisher = {IEEE},
title = {Online Model Selection for Synthetic Gene Networks},
url = {http://dx.doi.org/10.1109/CDC.2016.7798362},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Control algorithms combined with microfluidicdevices and microscopy have enabled in vivo real-time controlof protein expression in synthetic gene networks. Most controlalgorithms rely on the a priori availability of mathematicalmodels of the gene networks to be controlled. These modelsare typically black/grey box models, which can be obtainedthrough the use of data-driven techniques developed in thecontext of systems identification. Data-driven inference of bothmodel structure and parameters is the main focus of thispaper. There are two main challenges associated with theinference of dynamical models for real-time control of generegulatory networks in living cells. Since biological systemsare typically evolving over time, the first challenge stemsfrom the fact that model selection needs to be done online,which prevents the application of computationally expensiveidentification algorithms iterating through large amounts ofstreaming data. The second challenge consists in performingnonlinear model selection, which is typically too burdensomefor Kalman filtering related techniques due the heterogeneityand nonlinearity of the candidate models. In this paper,we combine sparse Bayesian techniques with classic Kalmanfiltering techniques to tackle these challenges
AU  - Pan,W
AU  - Menolascina,F
AU  - Stan,G
DO  - 10.1109/CDC.2016.7798362
PB  - IEEE
PY  - 2016///
TI  - Online Model Selection for Synthetic Gene Networks
UR  - http://dx.doi.org/10.1109/CDC.2016.7798362
UR  - http://hdl.handle.net/10044/1/38382
ER  -
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