In this section

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{Haines:2019:synbio/ysz019,
author = {Haines, M and Storch, M and Oyarzun, D and Stan, G and Baldwin, G},
doi = {synbio/ysz019},
journal = {Synthetic Biology},
pages = {1--10},
title = {Riboswitch identification using Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR)},
url = {http://dx.doi.org/10.1093/synbio/ysz019},
volume = {4},
year = {2019}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In vitro selection of ligand-responsive ribozymes can identify rare, functional sequences from large libraries. While powerful, key caveats of this approach include lengthy and demanding experimental workflows; unpredictable experimental outcomes and unknown functionality of enriched sequences in vivo. To address the first of these limitations we developed Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR). LigASERR is scalable, amenable to automation and requires less time to implement compared to alternative methods. To improve the predictability of experiments, we modelled the underlying selection process, predicting experimental outcomes based on sequence and population parameters. We applied this new methodology and model to the enrichment of a known, in vitro-selected sequence from a bespoke library. Prior to implementing selection, conditions were optimised and target sequence dynamics accurately predicted for the majority of the experiment. In addition to enriching the target sequence, we identified two new, theophylline-activated ribozymes. Notably, all three sequences yielded riboswitches functional in Escherichia coli, suggesting LigASERR and similar in vitro selection methods can be utilised for generating functional riboswitches in this organism.
AU  - Haines,M
AU  - Storch,M
AU  - Oyarzun,D
AU  - Stan,G
AU  - Baldwin,G
DO  - synbio/ysz019
EP  - 10
PY  - 2019///
SN  - 2397-7000
SP  - 1
TI  - Riboswitch identification using Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR)
T2  - Synthetic Biology
UR  - http://dx.doi.org/10.1093/synbio/ysz019
UR  - https://academic.oup.com/synbio/advance-article/doi/10.1093/synbio/ysz019/5529681
UR  - http://hdl.handle.net/10044/1/71866
VL  - 4
ER  -