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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{Bartasun:2019:10.7717/peerj.7529,
author = {Bartasun, P and Prandi, N and Storch, M and Aknin, Y and Bennett, M and Palma, A and Baldwin, G and Sakuragi, Y and Jones, PR and Rowland, J},
doi = {10.7717/peerj.7529},
journal = {PEERJ},
title = {The effect of modulating the quantity of enzymes in a model ethanol pathway on metabolic flux in Synechocystis sp. PCC 6803},
url = {http://dx.doi.org/10.7717/peerj.7529},
volume = {7},
year = {2019}
}

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RIS format (EndNote, RefMan)

TY - JOUR
AB - Synthetic metabolism allows new metabolic capabilities to be introduced into strains for biotechnology applications. Such engineered metabolic pathways are unlikely to function optimally as initially designed and native metabolism may not efficiently support the introduced pathway without further intervention. To develop our understanding of optimal metabolic engineering strategies, a two-enzyme ethanol pathway consisting of pyruvate decarboxylase and acetaldehyde reductase was introduced into Synechocystis sp. PCC 6803. We characteriseda new set of ribosome binding site sequences in Synechocystis sp. PCC 6803 providing a range of translation strengths for different genes under test. The effect of ribosome-bindingsite sequence, operon design and modifications to native metabolism on pathway flux was analysed by HPLC. The accumulation of all introduced proteins was also quantified using selected reaction monitoring mass spectrometry. Pathway productivity was more strongly dependent on the accumulation of pyruvate decarboxylase than acetaldehyde reductase. In fact, abolishment of reductase over-expression resulted in the greatest ethanol productivity, most likely because strains harbouringsingle-gene constructs accumulated more pyruvate decarboxylase than strains carrying any of the multi-gene constructs. Overall, several lessons were learned. Firstly, the expression level of the first gene in anyoperon influenced the expression level of subsequent genes, demonstrating that translational coupling can also occur in cyanobacteria. Longer operons resulted in lower protein abundance for proximally-encoded cistrons. And, implementation of metabolic engineering strategies that have previously been shown to enhance the growth or yield of pyruvate dependent products, through co-expression with pyruvate kinase and/or fructose-1,6-bisphosphatase/sedoheptulose-1,7-bisphosphatase, indicated that other factors had greater control over growth and metabolic flux under the tested con
AU - Bartasun,P
AU - Prandi,N
AU - Storch,M
AU - Aknin,Y
AU - Bennett,M
AU - Palma,A
AU - Baldwin,G
AU - Sakuragi,Y
AU - Jones,PR
AU - Rowland,J
DO - 10.7717/peerj.7529
PY - 2019///
SN - 2167-8359
TI - The effect of modulating the quantity of enzymes in a model ethanol pathway on metabolic flux in Synechocystis sp. PCC 6803
T2 - PEERJ
UR - http://dx.doi.org/10.7717/peerj.7529
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000482895100005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://peerj.com/articles/7529/#
UR - http://hdl.handle.net/10044/1/76248
VL - 7
ER -