Research

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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.

Publications

Citation

BibTex format

@article{Eyerich:2019:10.1016/j.jaci.2018.10.033,
author = {Eyerich, K and Brown, S and Perez, White B and Tanaka, RJ and Bissonette, R and Dhar, S and Bieber, T and Hijnen, DJ and Guttman-Yassky, E and Irvine, A and Thyssen, JP and Vestergaard, C and Werfel, T and Wollenberg, A and Paller, A and Reynolds, NJ},
doi = {10.1016/j.jaci.2018.10.033},
journal = {Journal of Allergy and Clinical Immunology},
pages = {36--45},
title = {Human and computational models of atopic dermatitis: a review and perspectives by an expert panel of the International Eczema Council},
url = {http://dx.doi.org/10.1016/j.jaci.2018.10.033},
volume = {143},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD.
AU  - Eyerich,K
AU  - Brown,S
AU  - Perez,White B
AU  - Tanaka,RJ
AU  - Bissonette,R
AU  - Dhar,S
AU  - Bieber,T
AU  - Hijnen,DJ
AU  - Guttman-Yassky,E
AU  - Irvine,A
AU  - Thyssen,JP
AU  - Vestergaard,C
AU  - Werfel,T
AU  - Wollenberg,A
AU  - Paller,A
AU  - Reynolds,NJ
DO  - 10.1016/j.jaci.2018.10.033
EP  - 45
PY  - 2019///
SN  - 0091-6749
SP  - 36
TI  - Human and computational models of atopic dermatitis: a review and perspectives by an expert panel of the International Eczema Council
T2  - Journal of Allergy and Clinical Immunology
UR  - http://dx.doi.org/10.1016/j.jaci.2018.10.033
UR  - https://www.jacionline.org/article/S0091-6749(18)31573-2/fulltext
UR  - http://hdl.handle.net/10044/1/65968
VL  - 143
ER  -
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