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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.
@article{Pan:2018:10.1109/TCNS.2017.2758966,
author = {Pan, W and Yuan, Y and Ljung, L and Goncalves, J and Stan, G},
doi = {10.1109/TCNS.2017.2758966},
journal = {IEEE Transactions on Control of Network Systems},
pages = {737--747},
title = {Identification of nonlinear state-space systems from heterogeneous datasets},
url = {http://dx.doi.org/10.1109/TCNS.2017.2758966},
volume = {5},
year = {2018}
}
TY - JOUR
AB - This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through 1) replicate measurements from the same biological system or 2) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed heterogeneous data. To enable scale up to large number of features, the resulting nonconvex optimization problem is relaxed to a reweighted Group Lasso problem using a convex–concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalized eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise.
AU - Pan,W
AU - Yuan,Y
AU - Ljung,L
AU - Goncalves,J
AU - Stan,G
DO - 10.1109/TCNS.2017.2758966
EP - 747
PY - 2018///
SN - 2325-5870
SP - 737
TI - Identification of nonlinear state-space systems from heterogeneous datasets
T2 - IEEE Transactions on Control of Network Systems
UR - http://dx.doi.org/10.1109/TCNS.2017.2758966
UR - https://ieeexplore.ieee.org/document/8055630
UR - http://hdl.handle.net/10044/1/50887
VL - 5
ER -