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Robust Target Identification in Drug Discovery
KTH, School of Electrical Engineering (EES), Automatic Control.
National Cheng Kung University, Taiwan.
2016 (English)In: 11th IFAC DYCOPS-CAB, Elsevier, 2016Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier, 2016.
Keyword [en]
Systems biology, systems medicine, target identification, network inference, robust regression, gene regulatory networks, drug discovery
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-182278ISI: 000381504800137OAI: oai:DiVA.org:kth-182278DiVA: diva2:904186
Conference
11th IFAC DYCOPS-CAB, Trondheim, Norway, 2016
Note

QC 20161011

Available from: 2016-02-18 Created: 2016-02-18 Last updated: 2016-10-11Bibliographically approved

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http://dycops2016.org/

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CiteExportLink to record
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Citation style
  • apa
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  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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