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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{Hancock:2015:10.1098/rsif.2015.0312,
author = {Hancock, E and Stan, G-B and Arpino, J and Papachristodoulou, A},
doi = {10.1098/rsif.2015.0312},
journal = {Journal of the Royal Society Interface},
title = {Simplified mechanistic models of gene regulation for analysis and design},
url = {http://dx.doi.org/10.1098/rsif.2015.0312},
volume = {12},
year = {2015}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Simplified mechanistic models of gene regulation are fundamental to systems biology and essential for synthetic biology. However, conventional simplified models typically have outputs that are not directly measurable and are based on assumptions that do not often hold under experimental conditions. To resolve these issues, we propose a ‘model reduction’ methodology and simplified kinetic models of total mRNA and total protein concentration, which link measurements, models and biochemical mechanisms. The proposed approach is based on assumptions that hold generally and include typical cases in systems and synthetic biology where conventional models do not hold. We use novel assumptions regarding the ‘speed of reactions’, which are required for the methodology to be consistent with experimental data. We also apply the methodology to propose simplified models of gene regulation in the presence of multiple protein binding sites, providing both biological insights and an illustration of the generality of the methodology. Lastly, we show that modelling total protein concentration allows us to address key questions on gene regulation, such as efficiency, burden, competition and modularity.
AU  - Hancock,E
AU  - Stan,G-B
AU  - Arpino,J
AU  - Papachristodoulou,A
DO  - 10.1098/rsif.2015.0312
PY  - 2015///
SN  - 1742-5689
TI  - Simplified mechanistic models of gene regulation for analysis and design
T2  - Journal of the Royal Society Interface
UR  - http://dx.doi.org/10.1098/rsif.2015.0312
UR  - http://hdl.handle.net/10044/1/27187
VL  - 12
ER  -

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