Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge

 Background: Cellular processes are controlled by gene-regulatory networks. Several
computational methods are currently used to learn the structure of gene-regulatory networks
from data. This study focusses on time series gene expression and gene knock-out data in order to
identify the underlying network structure. We compare the performance of different network
reconstruction methods using synthetic data generated from an ensemble of reference networks.
Data requirements as well as optimal experiments for the reconstruction of gene-regulatory
networks are investigated. Additionally, the impact of prior knowledge on network reconstruction
as well as the effect of unobserved cellular processes is studied.
Results: We identify linear Gaussian dynamic Bayesian networks and variable selection based on
F-statistics as suitable methods for the reconstruction of gene-regulatory networks from time
series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result
can be attributed to the inevitable information loss by discretization of expression data. It is shown
that short time series generated under transcription factor knock-out are optimal experiments in
order to reveal the structure of gene regulatory networks. Relative to the level of observational
noise, we give estimates for the required amount of gene expression data in order to accurately
reconstruct gene-regulatory networks. The benefit of using of prior knowledge within a Bayesian
learning framework is found to be limited to conditions of small gene expression data size.
Unobserved processes, like protein-protein interactions, induce dependencies between gene
expression levels similar to direct transcriptional regulation. We show that these dependencies
cannot be distinguished from transcription factor mediated gene regulation on the basis of gene
expression data alone.
Conclusion: Currently available data size and data quality make the reconstruction of gene
networks from gene expression data a challenge. In this study, we identify an optimal type of
experiment, requirements on the gene expression data quality and size as well as appropriate
reconstruction methods in order to reverse engineer gene regulatory networks from time series


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