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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

@inproceedings{Hurault:2018:10.1111/bjd.16718,
author = {Hurault, G and Roekevisch, E and Szegedi, K and Kezic, S and Spuls, PI and Middelkamp-Hup, MA and Tanaka, RJ},
doi = {10.1111/bjd.16718},
pages = {E17--E18},
publisher = {Wiley},
title = {Predicting short- and long-term outcomes of a systemic therapy for atopic dermatitis using machine learning methods},
url = {http://dx.doi.org/10.1111/bjd.16718},
year = {2018}
}

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RIS format (EndNote, RefMan)

TY  - CPAPER
AU  - Hurault,G
AU  - Roekevisch,E
AU  - Szegedi,K
AU  - Kezic,S
AU  - Spuls,PI
AU  - Middelkamp-Hup,MA
AU  - Tanaka,RJ
DO  - 10.1111/bjd.16718
EP  - 18
PB  - Wiley
PY  - 2018///
SN  - 1365-2133
SP  - 17
TI  - Predicting short- and long-term outcomes of a systemic therapy for atopic dermatitis using machine learning methods
UR  - http://dx.doi.org/10.1111/bjd.16718
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000439461600032&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/61881
ER  -