Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction.

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urn:nbn:de:bvb:355-epub-305237 Copy

Dümcke, Sebastian ; Bräuer, Johannes ; Anchang, Benedict Nchang ; Spang, Rainer ; Beerenwinkel, Niko ; Tresch, Achim

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Date of publication of this fulltext: 05 Aug 2014 08:14

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Details

Item type:	Article
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
Volume:	30
Number of Issue or Book Chapter:	3
Page Range:	pp. 414-419
Date:	February 2014
Institutions:	Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Identification Number:	Value Type 24292937 PubMed ID 10.1093/bioinformatics/btt696 DOI
Classification:	Notation Type Algorithms MESH Animals MESH Cell Differentiation MESH Embryonic Stem Cells/metabolism MESH Feedback, Physiological MESH Gene Expression MESH Mice MESH Probability MESH RNA Interference MESH Signal Transduction MESH
Dewey Decimal Classification:	500 Science > 500 Natural sciences & mathematics 500 Science > 570 Life sciences 600 Technology > 610 Medical sciences Medicine
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Partially
Item ID:	30523

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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 ...

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. Availability and implementation: The algorithm is implemented in the statistical language R. Code and documentation are available at Bioinformatics online.

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Metadata last modified: 02 Jun 2018 08:02

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