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A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data
Abstract 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 inf...
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| Published in: | Bioinformatics 2021-07, Vol.37 (Supplement_1), p.i383-i391 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c400t-a3955728c32ba9991aca3385a09021aa9337da89f518d5ba3772185fe327cbee3 |
| container_end_page | i391 |
| container_issue | Supplement_1 |
| container_start_page | i383 |
| container_title | Bioinformatics |
| container_volume | 37 |
| creator | Trinh, Hung-Cuong Kwon, Yung-Keun |
| description | Abstract
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. |
| doi_str_mv | 10.1093/bioinformatics/btab295 |
| format | article |
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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.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btab295</identifier><identifier>PMID: 34252959</identifier><language>eng</language><publisher>Oxford University Press</publisher><subject>Systems Biology and Networks</subject><ispartof>Bioinformatics, 2021-07, Vol.37 (Supplement_1), p.i383-i391</ispartof><rights>The Author(s) 2021. Published by Oxford University Press. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c400t-a3955728c32ba9991aca3385a09021aa9337da89f518d5ba3772185fe327cbee3</citedby><cites>FETCH-LOGICAL-c400t-a3955728c32ba9991aca3385a09021aa9337da89f518d5ba3772185fe327cbee3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275338/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275338/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,730,783,787,888,1587,27936,27937,53804,53806</link.rule.ids></links><search><creatorcontrib>Trinh, Hung-Cuong</creatorcontrib><creatorcontrib>Kwon, Yung-Keun</creatorcontrib><title>A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data</title><title>Bioinformatics</title><description>Abstract
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.</description><subject>Systems Biology and Networks</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqNkdtOwzAMhiMEYpxeAeUFynJo1uYGaSBOEhI3cF25qbsF2mRKMg5vT2AT0u64smX7_2zrJ-ScswvOtJy21lvX-zBCsiZO2wSt0GqPHPFyxgrBlN7PuZxVRVkzOSHHMb4ypnhZlodkIkuh8rg-InFOnX_HgRrvYgpgHXZ0gQ4zlsKw8MGm5Vi0EHP9yvsBwdHc_fDhjeYLMKAzSEdMS9_RPviRxoTQfRUxQcJfFMXPVcAYrXe0gwSn5KCHIeLZNp6Ql9ub5-v74vHp7uF6_liYkrFUgNRKVaI2UrSgteZgQMpaAdNMcAAtZdVBrXvF6061IKtK8Fr1KEVlWkR5Qi433NW6HbEz6PKDQ7MKdoTw1XiwzW7H2WWz8O9NLSqVN2XAbAMwwccYsP_Tctb82NDs2tBsbchCvhH69eq_mm-zs5W5</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Trinh, Hung-Cuong</creator><creator>Kwon, Yung-Keun</creator><general>Oxford University Press</general><scope>TOX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>5PM</scope></search><sort><creationdate>20210701</creationdate><title>A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data</title><author>Trinh, Hung-Cuong ; Kwon, Yung-Keun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-a3955728c32ba9991aca3385a09021aa9337da89f518d5ba3772185fe327cbee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Systems Biology and Networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Trinh, Hung-Cuong</creatorcontrib><creatorcontrib>Kwon, Yung-Keun</creatorcontrib><collection>Oxford University Press Open Access</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Trinh, Hung-Cuong</au><au>Kwon, Yung-Keun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data</atitle><jtitle>Bioinformatics</jtitle><date>2021-07-01</date><risdate>2021</risdate><volume>37</volume><issue>Supplement_1</issue><spage>i383</spage><epage>i391</epage><pages>i383-i391</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><abstract>Abstract
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.</abstract><pub>Oxford University Press</pub><pmid>34252959</pmid><doi>10.1093/bioinformatics/btab295</doi><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1367-4803 |
| ispartof | Bioinformatics, 2021-07, Vol.37 (Supplement_1), p.i383-i391 |
| issn | 1367-4803 1460-2059 1367-4811 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8275338 |
| source | PubMed Central; Oxford Journals |
| subjects | Systems Biology and Networks |
| title | A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data |
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