A Novel Data-Driven Boolean Model for Genetic Regulatory Networks
A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-09-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fphys.2018.01328/full |
| _version_ | 1819073636440473600 |
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| author | Leshi Chen Don Kulasiri Sandhya Samarasinghe |
| author_facet | Leshi Chen Don Kulasiri Sandhya Samarasinghe |
| author_sort | Leshi Chen |
| collection | DOAJ |
| description | A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug. |
| first_indexed | 2024-12-21T17:56:46Z |
| format | Article |
| id | doaj.art-fd38f16c3a944d578a1961aa7916728d |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-21T17:56:46Z |
| publishDate | 2018-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-fd38f16c3a944d578a1961aa7916728d2022-12-21T18:55:11ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2018-09-01910.3389/fphys.2018.01328326801A Novel Data-Driven Boolean Model for Genetic Regulatory NetworksLeshi Chen0Don Kulasiri1Sandhya Samarasinghe2Computational Systems Biology Laboratory, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New ZealandComputational Systems Biology Laboratory, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New ZealandIntegrated Systems Modelling Group, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New ZealandA Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.https://www.frontiersin.org/article/10.3389/fphys.2018.01328/fullboolean modelingboolean networktime series datanetwork inferencedata-driven boolean modelingfundamental boolean model |
| spellingShingle | Leshi Chen Don Kulasiri Sandhya Samarasinghe A Novel Data-Driven Boolean Model for Genetic Regulatory Networks Frontiers in Physiology boolean modeling boolean network time series data network inference data-driven boolean modeling fundamental boolean model |
| title | A Novel Data-Driven Boolean Model for Genetic Regulatory Networks |
| title_full | A Novel Data-Driven Boolean Model for Genetic Regulatory Networks |
| title_fullStr | A Novel Data-Driven Boolean Model for Genetic Regulatory Networks |
| title_full_unstemmed | A Novel Data-Driven Boolean Model for Genetic Regulatory Networks |
| title_short | A Novel Data-Driven Boolean Model for Genetic Regulatory Networks |
| title_sort | novel data driven boolean model for genetic regulatory networks |
| topic | boolean modeling boolean network time series data network inference data-driven boolean modeling fundamental boolean model |
| url | https://www.frontiersin.org/article/10.3389/fphys.2018.01328/full |
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