Duemcke, Sebastian and Braeuer, Johannes and Anchang, Benedict and Spang, Rainer and Beerenwinkel, Niko and Tresch, Achim (2014) Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction. BIOINFORMATICS, 30 (3). pp. 414-419. ISSN 1367-4803, 1460-2059
Full text not available from this repository. (Request a copy)Abstract
Motivation: For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network. Results: In this work, we present a novel model-based approach involving Boolean networks to reconstruct small to medium-sized regulatory networks. In particular, we solve the problem of exact likelihood computation in Boolean networks with probabilistic exponential time delays. Simulations demonstrate the high accuracy of our approach. We apply our method to data of Ivanova et al. (2006), where RNA interference knockdown experiments were used to build a network of the key regulatory genes governing mouse stem cell maintenance and differentiation. In contrast to previous analyses of that data set, our method can identify feedback loops and provides new insights into the interplay of some master regulators in embryonic stem cell development.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | NESTED EFFECTS MODELS; BAYESIAN NETWORKS; REGULATORY NETWORKS; STEM-CELLS; EXPRESSION; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 28 Nov 2019 08:59 |
| Last Modified: | 28 Nov 2019 08:59 |
| URI: | https://pred.uni-regensburg.de/id/eprint/10738 |
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