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Research

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.

Publications

Citation

BibTex format

@article{Ceroni:2015:10.1038/NMETH.3339,
author = {Ceroni, F and Algar, R and Stan, G-B and Ellis, T},
doi = {10.1038/NMETH.3339},
journal = {Nature Methods},
pages = {415--418},
title = {Quantifying cellular capacity identifies gene expression designs with reduced burden},
url = {http://dx.doi.org/10.1038/NMETH.3339},
volume = {12},
year = {2015}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Heterologous gene expression can be a significant burden forcells. Here we describe an in vivo monitor that tracks changesin the capacity of Escherichia coli in real time and can be usedto assay the burden imposed by synthetic constructs and theirparts. We identify construct designs with reduced burden thatpredictably outperformed less efficient designs, despite havingequivalent output.
AU  - Ceroni,F
AU  - Algar,R
AU  - Stan,G-B
AU  - Ellis,T
DO  - 10.1038/NMETH.3339
EP  - 418
PY  - 2015///
SN  - 1548-7105
SP  - 415
TI  - Quantifying cellular capacity identifies gene expression designs with reduced burden
T2  - Nature Methods
UR  - http://dx.doi.org/10.1038/NMETH.3339
UR  - http://hdl.handle.net/10044/1/31367
VL  - 12
ER  -