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{Misirli:2020:10.1021/acssynbio.0c00046,
author = {Misirli, G and Beal, J and Gorochowski, TE and Stan, G-B and Wipat, A and Myers, CJ},
doi = {10.1021/acssynbio.0c00046},
journal = {ACS Synthetic Biology},
pages = {972--977},
title = {SBOL visual 2 ontology},
url = {http://dx.doi.org/10.1021/acssynbio.0c00046},
volume = {9},
year = {2020}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Standardizing the visual representation of genetic parts and circuits is essential for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has up to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology.
AU  - Misirli,G
AU  - Beal,J
AU  - Gorochowski,TE
AU  - Stan,G-B
AU  - Wipat,A
AU  - Myers,CJ
DO  - 10.1021/acssynbio.0c00046
EP  - 977
PY  - 2020///
SN  - 2161-5063
SP  - 972
TI  - SBOL visual 2 ontology
T2  - ACS Synthetic Biology
UR  - http://dx.doi.org/10.1021/acssynbio.0c00046
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000526886400026&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://pubs.acs.org/doi/10.1021/acssynbio.0c00046
UR  - http://hdl.handle.net/10044/1/84191
VL  - 9
ER  -