Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

Yi Kan Wang,Santiago Schnell,Edmund J Crampin,Cristin G Print,Daniel G Hurley,Shyamal D Peddada

doi:10.1371/journal.pone.0072103

Abstract

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

Highlights

Determining gene regulatory network structure from gene expression data is one of the most challenging problems in molecular systems biology
In this study we compare the performance of three different versions of MIKANA, a regression-based ordinary differential equations (ODEs) model for gene regulatory network inference
Simulation of Microarray gene expression data To determine whether combining steady-state and time-series data can provide better prediction of gene regulatory interactions, we assessed the performance of network inference with steadystate, time-series and combined datasets by comparing candidate networks inferred from 100-gene simulated datasets against the synthetic networks used to simulate the data

Summary

Introduction

Determining gene regulatory network structure from gene expression data is one of the most challenging problems in molecular systems biology. Microarray technologies, as well as other newer approaches such as RNA-seq, have been widely used to generate quantitative gene expression data. Measurements of gene expression are typically conducted at a single time-point, or during successive time-points, after some perturbation. These data are termed steady-state data, and timeseries data, respectively. Both types have been used for network inference. Steady-state and time-series data can both provide valuable information about the topology, or ‘wiring diagram’, and dynamics of the gene regulatory network. Time-series data are thought to be more useful for revealing directional interactions to indicate the cause-and-effect relationships among genes [1]

Methods

Results

Conclusion