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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 Yuan, Y and Ljung, L and Gonçalves, JM and Stan, G-B},
doi = {10.1109/CDC.2015.7402596},
pages = {2525--2530},
publisher = {IEEE},
title = {Identifying biochemical reaction networks from heterogeneous datasets},
url = {},
year = {2016}

RIS format (EndNote, RefMan)

AB - In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.
AU - Pan,W
AU - Yuan,Y
AU - Ljung,L
AU - Gonçalves,JM
AU - Stan,G-B
DO - 10.1109/CDC.2015.7402596
EP - 2530
PY - 2016///
SP - 2525
TI - Identifying biochemical reaction networks from heterogeneous datasets
UR -
UR -
ER -