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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.



BibTex format

author = {Greenig, M and Melville, A and Huntley, D and Isalan, M and Mielcarek, M},
doi = {10.3389/fmolb.2020.565530},
journal = {Frontiers in Molecular Biosciences},
pages = {1--14},
title = {Cross-sectional transcriptional analysis of the ageing murine heart},
url = {},
volume = {7},
year = {2020}

RIS format (EndNote, RefMan)

AB - Cardiovascular disease accounts for millions of deaths each year and is currently the leading cause of mortality worldwide. The ageing process is clearly linked to cardiovascular disease, however, the exact relationship between ageing and heart function is not fully understood. Furthermore, a holistic view of cardiac ageing, linking features of early life development to changes observed in old age, has not been synthesized. Here, we re-purpose RNA-sequencing data previously-collected by our group, investigating gene expression differences between wild-type mice of different age groups that represent key developmental milestones in the murine lifespan. DESeq2’s generalized linear model was applied with two hypothesis6testing approaches to identify differentially-expressed (DE) genes, both between pairs of age groups and across mice of all ages. Pairwise comparisons identified genes associated with specific age transitions, while comparisons across all age groups identified a large set of genes associated with the ageing process more broadly. An unsupervised machine learning approach was then applied to extract common expression patterns from this set of age-associated genes. Sets of genes with both linear and non-linear expression trajectories were identified, suggesting that ageing not only involves the activation of gene expression programs unique to different age groups, but also the re-activation of gene expression programs from earlier ages. Overall, we present a comprehensive transcriptomic analysis of cardiac gene expression patterns across the entirety of the murine lifespan.
AU - Greenig,M
AU - Melville,A
AU - Huntley,D
AU - Isalan,M
AU - Mielcarek,M
DO - 10.3389/fmolb.2020.565530
EP - 14
PY - 2020///
SN - 2296-889X
SP - 1
TI - Cross-sectional transcriptional analysis of the ageing murine heart
T2 - Frontiers in Molecular Biosciences
UR -
UR -
UR -
VL - 7
ER -