Publications

@inproceedings{Hsu2018ADCHEM,

title = {Modelling of the GAL1 Genetic Circuit in Yeast Using Three Equations},

author = {Chi-Ching Hsu and Wu Yu-Heng and Filippo Menolascina and Torbjörn E. M. Nordling},

editor = {Sizhao Joe Qin and B. Wayne Bequette and Lorenz T. Biegler and Martin Guay and Rolf Findeisen and Jin Wang and Victor Zavala},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2405896318319785},

doi = {10.1016/j.ifacol.2018.09.297},

issn = {24058963},

year  = {2018},

date = {2018-07-01},

booktitle = {IFAC-PapersOnLine, 10th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2018: Shenyang, China, 25–27 July 2018},

volume = {51},

number = {18},

pages = {185–190},

publisher = {International Federation of Automatic Control (IFAC)},

address = {Shenyang, China},

abstract = {Synthetic gene circuits can be used to modify and control existing biological processes and thus e.g. increase drug yields. Currently their use is hampered by the, largely, trial and error approach used to design them. Lack of reliable quantitative dynamical models of genetic circuits e.g. prevents the use of well established control design methods. We aim toward creation of a pipeline for automated closed-loop identification of dynamic models of synthetically engineered genetic circuits in microorganisms. As a step towards this aim, we here study modelling of the input-output behaviour of the yGIL337 strain of S. cerevisiae. In this strain expression of the fluorescent reporter can be turned on by growing the yeast in galactose and off by glucose. We perform parameter estimation on a system of three ordinary differential equations of Michaelis-Menten type based on in vivo data from a microfluidic experiment by Fiore et al. (2013) after redoing the data preprocessing. The parameter estimation is done using AMIGO2–a state of the art Matlab toolbox for iterative identification of dynamical models. We show that the goodness-of-fit of our model is comparable to the five models proposed by Fiore et al. and we hypothesise that the system is an adaptive feedback system.},

keywords = {genetic circuit, parameter estimation, synthetic biology, system identification, Systems Biology},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jacobsen2016DYCOPS,

title = {Robust Target Identification for Drug Discovery},

author = {Elling W. Jacobsen and Torbjörn E. M. Nordling},

doi = {10.1016/j.ifacol.2016.07.290},

year  = {2016},

date = {2016-06-01},

booktitle = {IFAC-PapersOnLine, 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS-CAB 2016)},

volume = {49},

number = {7},

pages = {815–820},

publisher = {The International Federation of Automatic Control},

address = {Trondheim, Norway},

abstract = {A key step in the development of new pharmaceutical drugs is that of identifying direct targets of the bioactive compounds, and distinguishing these from all other gene products that respond indirectly to the drug targets. Currently dominating approaches to this problem are based on often time consuming and costly experimental methods aimed at locating physical bindings of the corresponding small molecule to proteins or DNA sequences. In this paper we consider target identification based on time-series expression data of the corresponding gene regulatory network, using perturbation with the active compound only. As we show, the problem of identifying the direct targets can then be cast as a linear regression problem and, in principle, be accomplished with a number of samples equal to the number of involved genes and bioactive compounds. However, the regression matrix will typically be highly ill-conditioned and the target identification therefore prone even to small measurement uncertainties. In order to provide a label of confidence for the target identification, we consider conditions that can be used to quantify the robustness of the identification of individual drug targets with respect to uncertainty in the expression data. For this purpose, we cast the uncertain regression problem as a robust rank problem and employ SVD or the structured singular value to compute the robust rank. The proposed method is illustrated by application to a small scale gene regulatory network synthesised in yeast to serve as a benchmark problem in network inference.},

keywords = {drug discovery, gene regulatory networks, Network Inference, regression, robust, Robust network inference, Systems Biology, systems medicine, target identification},

pubstate = {published},

tppubtype = {inproceedings}

}