Abstract
One methodology that has met success to infer gene networks from gene expression data is based upon ordinary differential equations (ODE). However new types of data continue to be produced, so it is worthwhile to investigate how to integrate these new data types into the inference procedure. One such data is physical interactions between transcription factors and the genes they regulate as measured by ChIP-chip or ChIP-seq experiments. These interactions can be incorporated into the gene network inference procedure as a priori network information. In this article, we extend the ODE methodology into a general optimization framework that incorporates existing network information in combination with regularization parameters that encourage network sparsity. We provide theoretical results proving convergence of the estimator for our method and show the corresponding probabilistic interpretation also converges. We demonstrate our method on simulated network data and show that existing network information improves performance, overcomes the lack of observations, and performs well even when some of the existing network information is incorrect. We further apply our method to the core regulatory network of embryonic stem cells utilizing predicted interactions from two studies as existing network information. We show that including the prior network information constructs a more closely representative regulatory network versus when no information is provided.
Highlights
Considerable progress has been obtained in the ability to infer gene regulatory networks from gene expression data
Gene network inference based on ordinary differential equations (ODEs) describes gene regulation as a function of other genes: dxi ðtÞ dt where xiðtÞ is the concentration of mRNA for gene i measured at time t, dxiðtÞ=dt is the rate of change for the mRNA concentration of gene i, and p is the number of genes
We have taken the ODE methodology for inferring gene networks from gene expression data and extended it to incorporate a priori network information, such that might be obtained from additional biological data like ChIP-chip or ChIP-seq experiments
Summary
Considerable progress has been obtained in the ability to infer gene regulatory networks from gene expression data. Recent techniques attempt to integrate additional data sources or introduce constraints to help guide the inference procedure Such techniques consider including modeling of environmental and transcription factor interactions [10,11], incorporating DNA motif sequence in gene promoter regions [12,13,14], combining multiple microarray datasets from the same organism across multiple experiments [15,16] or from completely different organisms [2], and integrating proteomics and metabolomics [17]. Gene network inference remains an extremely difficult problem and new integrative techniques still need to be explored
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