Multiobjective optimization to reconstruct biological networks
| Autor: | Ahmed Naef, Rosni Abdullah, Nur'Aini Abdul Rashid |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Statistics and Probability Speedup Databases Factual Computer science 0206 medical engineering Inference 02 engineering and technology computer.software_genre Multi-objective optimization General Biochemistry Genetics and Molecular Biology 03 medical and health sciences Humans Gene Regulatory Networks Protein Interaction Maps KEGG Metaheuristic Models Statistical Applied Mathematics Modelling biological systems Systems Biology General Medicine ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Modeling and Simulation Graph (abstract data type) Data mining computer 020602 bioinformatics Biological network Algorithms Metabolic Networks and Pathways |
| Zdroj: | Bio Systems. 174 |
| ISSN: | 1872-8324 |
| Popis: | Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup. |
| Databáze: | OpenAIRE |
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