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

Bin Yang1, Wenzheng Bao2, De-Shuang Huang3

  • 1School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China.

Scientific Reports
|December 14, 2018
PubMed
Summary
This summary is machine-generated.

A new parallel algorithm, MPRGEP, accurately infers large-scale gene regulatory networks (GRNs) by optimizing time-delayed S-system models. This method reduces computational load and speeds up the identification of complex regulatory relationships.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Understanding intracellular activity relies on inferring gene regulatory networks (GRNs).
  • Identifying large-scale GRNs is a significant challenge in systems biology due to computational complexity.
  • Existing methods often struggle with the scale and complexity of time-delayed regulatory relationships.

Purpose of the Study:

  • To propose a parallel algorithm, MPRGEP, for efficient and accurate inference of large-scale gene regulatory networks (GRNs).
  • To reduce the computational load associated with identifying instantaneous and time-delayed regulatory relationships.
  • To enhance the understanding of intracellular physiological activity through improved GRN identification.

Main Methods:

  • Developed MPRGEP, a parallel algorithm based on restricted gene expression programming (RGEP).
  • Encoded the structure and parameters of time-delayed S-system (TDSS) models into a single chromosome.
  • Employed a hybrid optimization approach combining genetic algorithm (GA) and gene expression programming (GEP) within a MapReduce framework.

Main Results:

  • MPRGEP demonstrated superior accuracy in inferring gene regulatory networks compared to state-of-the-art methods.
  • The algorithm achieved significant speedup in processing large-scale, time-delayed GRNs (TDGRNs) with hundreds of genes.
  • Successfully inferred both instantaneous and time-delayed regulatory relationships.

Conclusions:

  • MPRGEP offers a computationally efficient and accurate solution for large-scale gene regulatory network inference.
  • The proposed method advances systems biology by enabling more precise understanding of complex gene interactions.
  • This approach is effective for analyzing time-delayed regulatory dynamics in biological systems.