Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Abstract

BackgroundDynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models’ expressiveness.ResultsContinuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights.ConclusionsContinuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.

Computational dynamic approaches for temporal omics data with applications to systems medicine

“…They compared two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis [123]. Results showed that continuous time Bayesian networks were effective on networks of both small and large size, and were particularly feasible when the measurements were not evenly distributed over time.…”

Semi-supervised prediction of gene regulatory networks using machine learning algorithms

“…Various computational methods for performing unsupervised, supervised and semisupervised prediction of GRNs have been proposed. These methods employ a variety of techniques ranging from Boolean networks (Lähdesmäki et al, 2003) and Bayesian networks (Acerbi et al, 2014;Vignes et al, 2011) to compressive sensing (Chang et al, 2014). Integrated toolkits combining different network inference methods are also available (Hurley et al, 2015).…”

CIDER: a pipeline for detecting waves of coordinated transcriptional regulation in gene expression time-course data

“…microarray or RNA-Seq) are a longitudinal representation of gene expression useful for characterizing the dynamics of regulatory ac-tivity within cells. Transcriptomics time-courses describe the evolution of gene expression within the profiled cells across time-points, thus extending the static description provided by steady-state experiments [3], [4]. Hence, analyzing time-course data can help characterizing the mechanisms of transcriptional regulation triggered by external stimuli.…”