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

5Citations
Citations of this article
35Readers
Mendeley users who have this article in their library.

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. © 2013 The Author 2013.

Cite

CITATION STYLE

APA

Dümcke, S., Bräuer, J., Anchang, B., Spang, R., Beerenwinkel, N., & Tresch, A. (2014). Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction. Bioinformatics, 30(3), 414–419. https://doi.org/10.1093/bioinformatics/btt696

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free