A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data
| Autor: | Yung-Keun Kwon, Hung-Cuong Trinh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Theoretical computer science Source code AcademicSubjects/SCI01060 Computer science Heuristic (computer science) media_common.quotation_subject Inference Gene Expression Biochemistry Reduction (complexity) 03 medical and health sciences 0302 clinical medicine Convergence (routing) Genetic algorithm Gene Regulatory Networks Molecular Biology 030304 developmental biology media_common 0303 health sciences Models Genetic Systems Biology Quantitative Biology::Genomics Expression (mathematics) Computer Science Applications Computational Mathematics Boolean network Computational Theory and Mathematics Systems Biology and Networks 030217 neurology & neurosurgery Algorithms Software |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | Motivation It is a challenging problem in systems biology to infer both the network structure and dynamics of a gene regulatory network from steady-state gene expression data. Some methods based on Boolean or differential equation models have been proposed but they were not efficient in inference of large-scale networks. Therefore, it is necessary to develop a method to infer the network structure and dynamics accurately on large-scale networks using steady-state expression. Results In this study, we propose a novel constrained genetic algorithm-based Boolean network inference (CGA-BNI) method where a Boolean canalyzing update rule scheme was employed to capture coarse-grained dynamics. Given steady-state gene expression data as an input, CGA-BNI identifies a set of path consistency-based constraints by comparing the gene expression level between the wild-type and the mutant experiments. It then searches Boolean networks which satisfy the constraints and induce attractors most similar to steady-state expressions. We devised a heuristic mutation operation for faster convergence and implemented a parallel evaluation routine for execution time reduction. Through extensive simulations on the artificial and the real gene expression datasets, CGA-BNI showed better performance than four other existing methods in terms of both structural and dynamics prediction accuracies. Taken together, CGA-BNI is a promising tool to predict both the structure and the dynamics of a gene regulatory network when a highest accuracy is needed at the cost of sacrificing the execution time. Availability and implementation Source code and data are freely available at https://github.com/csclab/CGA-BNI. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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