Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis.
Gene regulatory networks (GRNs) coherently coordinate the expressions of genes and control the behaviors of cellular systems. The complexity in modeling a quantitative GRN usually results from inaccurate parameter estimation, which is mostly due to small sample sizes. For better modeling of GRNs, we...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4204895?pdf=render |
| _version_ | 1818578601914662912 |
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| author | Yin Wang Rudong Li Chunguang Ji Shuliang Shi Yufan Cheng Hong Sun Yixue Li |
| author_facet | Yin Wang Rudong Li Chunguang Ji Shuliang Shi Yufan Cheng Hong Sun Yixue Li |
| author_sort | Yin Wang |
| collection | DOAJ |
| description | Gene regulatory networks (GRNs) coherently coordinate the expressions of genes and control the behaviors of cellular systems. The complexity in modeling a quantitative GRN usually results from inaccurate parameter estimation, which is mostly due to small sample sizes. For better modeling of GRNs, we have designed a small-sample iterative optimization algorithm (SSIO) to quantitatively model GRNs with nonlinear regulatory relationships. The algorithm utilizes gene expression data as the primary input and it can be applied in case of small-sized samples. Using SSIO, we have quantitatively constructed the dynamic models for the GRNs controlling human and mouse adipogenesis. Compared with two other commonly-used methods, SSIO shows better performance with relatively lower residual errors, and it generates rational predictions on the adipocyte responses to external signals and steady-states. Sensitivity analysis further indicates the validity of our method. Several differences are observed between the GRNs of human and mouse adipocyte differentiations, suggesting the differences in regulatory efficiencies of the transcription factors between the two species. In addition, we use SSIO to quantitatively determine the strengths of the regulatory interactions as well as to optimize regulatory models. The results indicate that SSIO facilitates better investigation and understanding of gene regulatory processes. |
| first_indexed | 2024-12-16T06:48:24Z |
| format | Article |
| id | doaj.art-4410b9635ca545bb9d9acf0c53943950 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-16T06:48:24Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4410b9635ca545bb9d9acf0c539439502022-12-21T22:40:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01910e11056310.1371/journal.pone.0110563Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis.Yin WangRudong LiChunguang JiShuliang ShiYufan ChengHong SunYixue LiGene regulatory networks (GRNs) coherently coordinate the expressions of genes and control the behaviors of cellular systems. The complexity in modeling a quantitative GRN usually results from inaccurate parameter estimation, which is mostly due to small sample sizes. For better modeling of GRNs, we have designed a small-sample iterative optimization algorithm (SSIO) to quantitatively model GRNs with nonlinear regulatory relationships. The algorithm utilizes gene expression data as the primary input and it can be applied in case of small-sized samples. Using SSIO, we have quantitatively constructed the dynamic models for the GRNs controlling human and mouse adipogenesis. Compared with two other commonly-used methods, SSIO shows better performance with relatively lower residual errors, and it generates rational predictions on the adipocyte responses to external signals and steady-states. Sensitivity analysis further indicates the validity of our method. Several differences are observed between the GRNs of human and mouse adipocyte differentiations, suggesting the differences in regulatory efficiencies of the transcription factors between the two species. In addition, we use SSIO to quantitatively determine the strengths of the regulatory interactions as well as to optimize regulatory models. The results indicate that SSIO facilitates better investigation and understanding of gene regulatory processes.http://europepmc.org/articles/PMC4204895?pdf=render |
| spellingShingle | Yin Wang Rudong Li Chunguang Ji Shuliang Shi Yufan Cheng Hong Sun Yixue Li Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis. PLoS ONE |
| title | Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis. |
| title_full | Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis. |
| title_fullStr | Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis. |
| title_full_unstemmed | Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis. |
| title_short | Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis. |
| title_sort | quantitative dynamic modelling of the gene regulatory network controlling adipogenesis |
| url | http://europepmc.org/articles/PMC4204895?pdf=render |
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