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. 2018 Dec 12;8(1):17787.
doi: 10.1038/s41598-018-36180-y.

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Bin Yang  1 , Wenzheng Bao  2 , De-Shuang Huang  3 , Yuehui Chen  4
Affiliations

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Bin Yang et al. Sci Rep. .

Abstract

Inference of gene regulatory network (GRN) is crucial to understand intracellular physiological activity and function of biology. The identification of large-scale GRN has been a difficult and hot topic of system biology in recent years. In order to reduce the computation load for large-scale GRN identification, a parallel algorithm based on restricted gene expression programming (RGEP), namely MPRGEP, is proposed to infer instantaneous and time-delayed regulatory relationships between transcription factors and target genes. In MPRGEP, the structure and parameters of time-delayed S-system (TDSS) model are encoded into one chromosome. An original hybrid optimization approach based on genetic algorithm (GA) and gene expression programming (GEP) is proposed to optimize TDSS model with MapReduce framework. Time-delayed GRNs (TDGRN) with hundreds of genes are utilized to test the performance of MPRGEP. The experiment results reveal that MPRGEP could infer more accurately gene regulatory network than other state-of-art methods, and obtain the convincing speedup.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flowchart of MapReduce framework.
Figure 2
Figure 2
An example of RGEP chromosome with parameters. I1 is given as {2⁎,3⁎}∪{x1,x2,…,x5}.
Figure 3
Figure 3
The expression tree of a RGEP chromosome with the parameters.
Figure 4
Figure 4
Encoding form of chromosome i in the hybrid evolutionary method.
Figure 5
Figure 5
The main flowchart of TDGRN inference.
Figure 6
Figure 6
The proposed hybrid evolutionary framework with the Hadoop MapReduce model.
Figure 7
Figure 7
Description of TP, FN, FP and TN.
Figure 8
Figure 8
The reconstructed GRN with 30 genes. Solid lines denote the instantaneous regulatory relationships, while dashed lines denote the time-delayed regulatory relationships.
Figure 9
Figure 9
Runtime performance of MPRGEP for three TDGRNs inference.
Figure 10
Figure 10
Speedup performance of MPRGEP for three TDGRNs inference.
Figure 11
Figure 11
Performance of MPRGEP with different partition numbers.
Figure 12
Figure 12
Speedup performance of MPRGEP for GRN inference with 500 genes.
See this image and copyright information in PMC

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