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Synthetic Biology underpins advances in the bioeconomy

Biological systems - including the simplest cells - exhibit a broad range of functions to thrive in their environment. Research in the Imperial College Centre for Synthetic Biology is focused on the possibility of engineering the underlying biochemical processes to solve many of the challenges facing society, from healthcare to sustainable energy. In particular, we model, analyse, design and build biological and biochemical systems in living cells and/or in cell extracts, both exploring and enhancing the engineering potential of biology. 

As part of our research we develop novel methods to accelerate the celebrated Design-Build-Test-Learn synthetic biology cycle. As such research in the Centre for Synthetic Biology highly multi- and interdisciplinary covering computational modelling and machine learning approaches; automated platform development and genetic circuit engineering ; multi-cellular and multi-organismal interactions, including gene drive and genome engineering; metabolic engineering; in vitro/cell-free synthetic biology; engineered phages and directed evolution; and biomimetics, biomaterials and biological engineering.



BibTex format

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 = {},
year = {2016}

RIS format (EndNote, RefMan)

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
PY - 2016///
TI - Online Model Selection for Synthetic Gene Networks
UR -
UR -
ER -