Functional Data Analysis of the Dynamics of Gene Regulatory Networks

2004 ◽  
pp. 69-83 ◽  
Author(s):  
Tomohiro Ando ◽  
Seiya Imoto ◽  
Satoru Miyano
Keyword(s):  
Data Analysis ◽  
Gene Regulatory Networks ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Regulatory Networks ◽  
Gene Regulatory

scholarly journals Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

2020 ◽  
Vol 41 (6) ◽  
pp. 599-604
Author(s):  
Hao Dai ◽  
◽  
◽  
Qi-Qi Jin ◽  
Lin Li ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Transcriptomic Data ◽  
Gene Regulatory

Microarray Data Analysis: Gene Regulatory Networks

2011 ◽  
pp. 473-488 ◽  
Author(s):  
Riccardo Bellazzi ◽  
Silvio Bicciato ◽  
Claudio Cobelli ◽  
Barbara Di Camillo ◽  
Fulvia Ferrazzi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gene Regulatory Networks ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Microarray Data Analysis ◽  
Gene Regulatory

scholarly journals Modeling the dynamics of Let-7-coupled gene regulatory networks linking cell proliferation to malignant transformation

2019 ◽  
Author(s):  
Claude Gérard ◽  
Frédéric Lemaigre ◽  
Didier Gonze
Keyword(s):  
Cell Cycle ◽  
Data Analysis ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Transcriptomic Data ◽  
Transformed Cell ◽  
Cell State ◽  
Gene Regulatory

AbstractThe microRNA Let-7 controls the expression of proteins that belong to two distinct gene regulatory networks, namely a cyclin-dependent kinases (Cdks) network driving the cell cycle and a cell transformation network which can undergo an epigenetic switch between a non-transformed and a malignant transformed cell state.Using mathematical modeling and transcriptomic data analysis, we here investigate how Let-7 controls the cdk-dependent cell cycle network, and how it couples the latter with the transformation network. We also determine whether the two networks can be combined into a larger entity that impacts on cancer progression.Our analysis shows that the switch from a quiescent to a cycling state depends on the relative levels of Let-7 and several cell cycle activators. Numerical simulations further indicate that the Let-7-coupled cell cycle and transformation networks control each other, and our model identifies key players for this mutual control. Transcriptomic data analysis from the The Cancer Genome Atlas (TCGA) suggest that the two networks are activated in cancer, in particular in gastrointestinal cancers, and that the activation levels vary significantly among patients affected with a same cancer type. Our mathematical model, when applied to a heterogeneous cell population, suggests that heterogeneity among tumors results from stochastic switches between a non-transformed cell state with low proliferative capability and a transformed cell state with high proliferative property. The model further predicts that Let-7 may reduce tumor heterogeneity by decreasing the occurrence of stochastic switches towards a transformed, proliferative cell state.In conclusion, we identified the key components responsible for the qualitative dynamics of two GRNs interconnected by Let-7. The two GRNs are heterogeneously involved in several cancers, thereby stressing the need to consider patient’s specific GRN characteristics to optimize therapeutic strategies.

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