Research

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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.

Publications

Citation

BibTex format

@article{Reynolds:2017:10.1049/enb.2017.0008,
author = {Reynolds, CR and Exley, K and Bultelle, MA and Sainz, de Murieta I and Kitney, RI},
doi = {10.1049/enb.2017.0008},
journal = {Engineering Biology},
pages = {51--54},
title = {Debugging experiment machinery through time-course event sequence analysis},
url = {http://dx.doi.org/10.1049/enb.2017.0008},
volume = {1},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This application note describes an open-source web application software package for viewing and analysing time-course event sequences in the form of log files containing timestamps. Web pages allow the visualisation of time-course event sequences as time curves and the comparison of sequences against each other to visualise deviations between the timings of the sequences. A feature allows the analysis of the sequences by parsing selected sections with a support vector machine model that heuristically calculates a value for the likelihood of an error occurring based on the textual output in the log files. This allows quick analysis for errors in files with large numbers of log events. The software is written in ASP.NET with Visual Basic code-behind to allow it to be hosted on servers and integrated into web application frameworks.
AU  - Reynolds,CR
AU  - Exley,K
AU  - Bultelle,MA
AU  - Sainz,de Murieta I
AU  - Kitney,RI
DO  - 10.1049/enb.2017.0008
EP  - 54
PY  - 2017///
SN  - 2398-6182
SP  - 51
TI  - Debugging experiment machinery through time-course event sequence analysis
T2  - Engineering Biology
UR  - http://dx.doi.org/10.1049/enb.2017.0008
UR  - http://hdl.handle.net/10044/1/59080
VL  - 1
ER  -
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