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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{Selles:2021:10.1371/journal.pone.0245280,
author = {Selles, Vidal L and Ayala, R and Stan, G-B and Ledesma-Amaro, R},
doi = {10.1371/journal.pone.0245280},
journal = {PLoS One},
title = {rfaRm: An R client-side interface to facilitate the analysis of the Rfam database of RNA families},
url = {http://dx.doi.org/10.1371/journal.pone.0245280},
volume = {16},
year = {2021}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - rfaRm is an R package providing a client-side interface for the Rfam database of non-coding RNA and other structured RNA elements. The package facilitates the search of the Rfam database by keywords or sequences, as well as the retrieval of all available information about specific Rfam families, such as member sequences, multiple sequence alignments, secondary structures and covariance models. By providing such programmatic access to the Rfam database, rfaRm enables genomic workflows to incorporate information about non-coding RNA, whose potential cannot be fully exploited just through interactive access to the database. The features of rfaRm are demonstrated by using it to analyze the SARS-CoV-2 genome as an example case.
AU  - Selles,Vidal L
AU  - Ayala,R
AU  - Stan,G-B
AU  - Ledesma-Amaro,R
DO  - 10.1371/journal.pone.0245280
PY  - 2021///
SN  - 1932-6203
TI  - rfaRm: An R client-side interface to facilitate the analysis of the Rfam database of RNA families
T2  - PLoS One
UR  - http://dx.doi.org/10.1371/journal.pone.0245280
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000609991600009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/86424
VL  - 16
ER  -