Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.
Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.
Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Zahra
http://orcid.org/0000-0002-8718-0766
Z
Narimani
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Hamid
H
Beigy
Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany.
Ashar
A
Ahmad
Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Ali
A
Masoudi-Nejad
Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany.|UCB Biosciences GmbH, Monheim, Germany.
Holger
H
Fröhlich
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D015398:Signal Transduction