Publications
Our published Work!
2016
Jacobsen, Elling W.; Nordling, Torbjörn E. M.
Robust Target Identification for Drug Discovery Proceedings Article
In: IFAC-PapersOnLine, 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS-CAB 2016), pp. 815–820, The International Federation of Automatic Control, Trondheim, Norway, 2016.
Abstract | Links | BibTeX | Tags: drug discovery, gene regulatory networks, Network Inference, regression, robust, Robust network inference, Systems Biology, systems medicine, target identification
@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}
}
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.
Nordling, Torbjörn E M
Robust Network Inference–Testing of all possible influence hypotheses during model selection Miscellaneous
The 6th East Asia Mechanical and Aerospace Engineering Workshop, 2016.
Abstract | Links | BibTeX | Tags: gene regulatory networks, Hypothesis testing, Nordling's confidence score, Reverse engineering, Robust network inference
@misc{Nordling20166thEast,
title = {Robust Network Inference–Testing of all possible influence hypotheses during model selection},
author = {Torbjörn E M Nordling},
url = {http://news-en.secr.ncku.edu.tw/files/14-1083-155190,r614-1.php?Lang=en},
year = {2016},
date = {2016-06-01},
booktitle = {The 6th East Asia Mechanical and Aerospace Engineering Workshop},
publisher = {The 6th East Asia Mechanical and Aerospace Engineering Workshop in Tainan, Taiwan},
address = {National Cheng Kung University, Tainan, Taiwan (R.O.C.)},
abstract = {The main objective of network inference is to identify the structure, i.e. topology, of a network based on observed changes in state variables representing the nodes. For example, to infer genetic influences among genes based on measured expression changes in so called gene regulatory networks one need to test all possible influence hypotheses. Each influence hypothesis corresponds to a possible link in the network, which typically is represented by a model parameter. Network inference is thus model selection. In Robust Network Inference (RNI) the hypothesis—the link/parameter is not needed to explain the data—is tested for every possible link/parameter, based on the assumed uncertainty in the observed inputs and outputs of the system. RNI reveals the links that exist, i.e. must be present in order to explain the data within the chosen class of models. These links exist in reality, i.e. are true positives, assuming that the real system can be approximated using the chosen class of models and the real measurement error is smaller than the assumed uncertainty. Contrary to methods that infer the most likely model, RNI accounts for all possible models. For each possible link/parameter, Nordling's confidence score is calculated in RNI. Contrary to the marginal tests used so far, this score is a reliable significance measure for existence of the link, as well as a relative measure of the distance to the closest model lacking the link. By rejecting the hypothesis, e.g., the existence of a genetic influence or the importance of a parameter is proven using RNI.},
howpublished = {The 6th East Asia Mechanical and Aerospace Engineering Workshop},
keywords = {gene regulatory networks, Hypothesis testing, Nordling's confidence score, Reverse engineering, Robust network inference},
pubstate = {published},
tppubtype = {misc}
}
The main objective of network inference is to identify the structure, i.e. topology, of a network based on observed changes in state variables representing the nodes. For example, to infer genetic influences among genes based on measured expression changes in so called gene regulatory networks one need to test all possible influence hypotheses. Each influence hypothesis corresponds to a possible link in the network, which typically is represented by a model parameter. Network inference is thus model selection. In Robust Network Inference (RNI) the hypothesis—the link/parameter is not needed to explain the data—is tested for every possible link/parameter, based on the assumed uncertainty in the observed inputs and outputs of the system. RNI reveals the links that exist, i.e. must be present in order to explain the data within the chosen class of models. These links exist in reality, i.e. are true positives, assuming that the real system can be approximated using the chosen class of models and the real measurement error is smaller than the assumed uncertainty. Contrary to methods that infer the most likely model, RNI accounts for all possible models. For each possible link/parameter, Nordling's confidence score is calculated in RNI. Contrary to the marginal tests used so far, this score is a reliable significance measure for existence of the link, as well as a relative measure of the distance to the closest model lacking the link. By rejecting the hypothesis, e.g., the existence of a genetic influence or the importance of a parameter is proven using RNI.
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