Reverse-engineering of gene networks for regulating early blood development from single-cell measurements
Abstract Background Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. Ho...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-12-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12920-017-0312-z |
| _version_ | 1818865745540415488 |
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| author | Jiangyong Wei Xiaohua Hu Xiufen Zou Tianhai Tian |
| author_facet | Jiangyong Wei Xiaohua Hu Xiufen Zou Tianhai Tian |
| author_sort | Jiangyong Wei |
| collection | DOAJ |
| description | Abstract Background Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. Methods This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. Results The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks. Conclusion The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations. |
| first_indexed | 2024-12-19T10:52:26Z |
| format | Article |
| id | doaj.art-bf7b5d22dbae462488390ad0d03f2e3a |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-12-19T10:52:26Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-bf7b5d22dbae462488390ad0d03f2e3a2022-12-21T20:24:57ZengBMCBMC Medical Genomics1755-87942017-12-0110S5314310.1186/s12920-017-0312-zReverse-engineering of gene networks for regulating early blood development from single-cell measurementsJiangyong Wei0Xiaohua Hu1Xiufen Zou2Tianhai Tian3School of Statistics and Mathematics, Zhongnan University of Economics and LawSchool of Computer, Central China Normal UniversitySchool of Mathematics and Statistics, Wuhan UniversitySchool of Mathematical Sciences, Monash UniversityAbstract Background Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. Methods This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. Results The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks. Conclusion The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.http://link.springer.com/article/10.1186/s12920-017-0312-zGenetic regulatory networkBlood stem cellSingle-cell experimentGraphic modelDynamic model |
| spellingShingle | Jiangyong Wei Xiaohua Hu Xiufen Zou Tianhai Tian Reverse-engineering of gene networks for regulating early blood development from single-cell measurements BMC Medical Genomics Genetic regulatory network Blood stem cell Single-cell experiment Graphic model Dynamic model |
| title | Reverse-engineering of gene networks for regulating early blood development from single-cell measurements |
| title_full | Reverse-engineering of gene networks for regulating early blood development from single-cell measurements |
| title_fullStr | Reverse-engineering of gene networks for regulating early blood development from single-cell measurements |
| title_full_unstemmed | Reverse-engineering of gene networks for regulating early blood development from single-cell measurements |
| title_short | Reverse-engineering of gene networks for regulating early blood development from single-cell measurements |
| title_sort | reverse engineering of gene networks for regulating early blood development from single cell measurements |
| topic | Genetic regulatory network Blood stem cell Single-cell experiment Graphic model Dynamic model |
| url | http://link.springer.com/article/10.1186/s12920-017-0312-z |
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