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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{Pan:2015:10.1109/TAC.2015.2426291,
author = {Pan, W and Yuan, Y and Goncalves, J and Stan, G-B},
doi = {10.1109/TAC.2015.2426291},
journal = {IEEE Transactions on Automatic Control},
pages = {182--187},
title = {A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems},
url = {http://dx.doi.org/10.1109/TAC.2015.2426291},
volume = {61},
year = {2015}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This technical note considers the identification ofnonlinear discrete-time systems with additive process noise butwithout measurement noise. In particular, we propose a methodand its associated algorithm to identify the system nonlinear functionalforms and their associated parameters from a limited numberof time-series data points. For this, we cast this identificationproblem as a sparse linear regression problem and take a Bayesianviewpoint to solve it. As such, this approach typically leads tononconvex optimizations. We propose a convexification procedurerelying on an efficient iterative re-weighted 1-minimization algorithmthat uses general sparsity inducing priors on the parametersof the system and marginal likelihood maximisation. Using thisapproach, we also show how convex constraints on the parameterscan be easily added to the proposed iterative re-weighted1-minimization algorithm. In the supplementary material availableonline (arXiv:1408.3549), we illustrate the effectiveness of theproposed identification method on two classical systems in biologyand physics, namely, a genetic repressilator network and a largescale network of interconnected Kuramoto oscillators.
AU  - Pan,W
AU  - Yuan,Y
AU  - Goncalves,J
AU  - Stan,G-B
DO  - 10.1109/TAC.2015.2426291
EP  - 187
PY  - 2015///
SN  - 1558-2523
SP  - 182
TI  - A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
T2  - IEEE Transactions on Automatic Control
UR  - http://dx.doi.org/10.1109/TAC.2015.2426291
UR  - http://hdl.handle.net/10044/1/32462
VL  - 61
ER  -