Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series

Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their gene regulatory networks. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based classification approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies.

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Inference of Gene Regulatory Networks Using Time Sliding Comparison and Transcriptional Lagging Time from Time Series Gene Expression Profiles

2007 IEEE 7th International Symposium on BioInformatics and BioEngineering ◽

10.1109/bibe.2007.4375684 ◽

2007 ◽

Author(s):

Sheehyun Kim ◽

Dongsup Kim

Keyword(s):

Gene Expression ◽

Time Series ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Time Series Gene Expression ◽

Gene Regulatory

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Inference of gene regulatory networks based on nonlinear ordinary differential equations

Bioinformatics ◽

10.1093/bioinformatics/btaa032 ◽

2020 ◽

Vol 36 (19) ◽

pp. 4885-4893 ◽

Cited By ~ 2

Author(s):

Baoshan Ma ◽

Mingkun Fang ◽

Xiangtian Jiao

Keyword(s):

Gene Expression ◽

Time Series ◽

Steady State ◽

Differential Equations ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Series Data ◽

Series Data ◽

State Data ◽

Gene Regulatory

Abstract Motivation Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks. Results In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity. Availability and implementation The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs Supplementary information Supplementary data are available at Bioinformatics online.

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