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Multiobjective optimization to reconstruct biological networks.

Ahmed Naef1, Rosni Abdullah2, Nur'Aini Abdul Rashid3

  • 1School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.

Bio Systems
|September 22, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a multiobjective genetic algorithm for reconstructing biological networks, improving computational systems biology. Parallelizing this method on GPUs significantly speeds up network reconstruction, achieving a 492-fold speedup.

Keywords:
Biological network reconstructionMetaheuristicOptimizationTopological properties

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Area of Science:

  • Computational Systems Biology
  • Bioinformatics
  • Network Reconstruction

Background:

  • Automated biological network reconstruction is crucial for systems biology.
  • Public databases facilitate the inference of biological networks across organisms.
  • Understanding metabolic and protein interaction networks is key to biological insights.

Purpose of the Study:

  • To propose a multiobjective genetic algorithm for reconstructing metabolic and protein interaction networks.
  • To leverage scale-free network properties and biological information for network inference.
  • To address the computational time limitations of network reconstruction methods.

Main Methods:

  • Utilized a multiobjective genetic algorithm incorporating scale-free network properties.
  • Integrated omics data and gene/protein information for network reconstruction.
  • Applied the method to yeast metabolic (KEGG, BioCyc) and protein interaction (Krogan, BioGrid) networks.
  • Parallelized the algorithm on Graphics Processing Units (GPUs) to enhance computational efficiency.

Main Results:

  • The proposed method successfully reconstructs biological networks by integrating diverse omics data and network characteristics.
  • Experimental validation on yeast networks demonstrated the method's capability.
  • Parallelization on GPUs resulted in a significant reduction in execution time.
  • Achieved a 492-fold speedup using the parallelized GPU approach.

Conclusions:

  • The multiobjective genetic algorithm effectively reconstructs biological networks.
  • GPU parallelization dramatically improves the efficiency of network reconstruction.
  • This approach offers a powerful tool for advancing computational systems biology research.