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{Yuan:2012,
author = {Yuan, Y and Stan, G-B and Warnick, S and Goncalves, J},
title = {Minimal realization of the dynamical structure function and its  application to network reconstruction},
url = {http://arxiv.org/abs/1209.3808v1},
year = {2012}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Network reconstruction, i.e., obtaining network structure from data, is acentral theme in systems biology, economics and engineering. In some previouswork, we introduced dynamical structure functions as a tool for posing andsolving the problem of network reconstruction between measured states. Whilerecovering the network structure between hidden states is not possible sincethey are not measured, in many situations it is important to estimate theminimal number of hidden states in order to understand the complexity of thenetwork under investigation and help identify potential targets formeasurements. Estimating the minimal number of hidden states is also crucial toobtain the simplest state-space model that captures the network structure andis coherent with the measured data. This paper characterizes minimal orderstate-space realizations that are consistent with a given dynamical structurefunction by exploring properties of dynamical structure functions anddeveloping an algorithm to explicitly obtain such a minimal realization.
AU  - Yuan,Y
AU  - Stan,G-B
AU  - Warnick,S
AU  - Goncalves,J
PY  - 2012///
TI  - Minimal realization of the dynamical structure function and its  application to network reconstruction
UR  - http://arxiv.org/abs/1209.3808v1
ER  -
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