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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{Tay:2015:10.1016/j.jbi.2014.12.014,
author = {Tay, D and Poh, CL and Kitney, RI},
doi = {10.1016/j.jbi.2014.12.014},
journal = {JOURNAL OF BIOMEDICAL INFORMATICS},
pages = {305--314},
title = {A novel neural-inspired learning algorithm with application to clinical risk prediction},
url = {http://dx.doi.org/10.1016/j.jbi.2014.12.014},
volume = {54},
year = {2015}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AU  - Tay,D
AU  - Poh,CL
AU  - Kitney,RI
DO  - 10.1016/j.jbi.2014.12.014
EP  - 314
PY  - 2015///
SN  - 1532-0464
SP  - 305
TI  - A novel neural-inspired learning algorithm with application to clinical risk prediction
T2  - JOURNAL OF BIOMEDICAL INFORMATICS
UR  - http://dx.doi.org/10.1016/j.jbi.2014.12.014
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000353932500029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
VL  - 54
ER  -

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