Dynamic Identification and Visualization of Gene Regulatory Networks from Time-Series Gene Expression Profiles

2009 ◽  
pp. 65-74 ◽  
Author(s):  
Yu Chen ◽  
Kyungsook Han
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Dynamic Identification ◽  
Time Series Gene Expression ◽  
Gene Regulatory

MODELING NONLINEAR GENE REGULATORY NETWORKS FROM TIME SERIES GENE EXPRESSION DATA

2008 ◽  
Vol 06 (05) ◽  
pp. 961-979 ◽  
Author(s):  
ANDRÉ FUJITA ◽  
JOÃO RICARDO SATO ◽  
HUMBERTO MIGUEL GARAY-MALPARTIDA ◽  
MARI CLEIDE SOGAYAR ◽  
CARLOS EDUARDO FERREIRA ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
A Priori ◽  
Molecular Networks ◽  
Biological Knowledge ◽  
Vector Autoregressive ◽  
Gene Regulatory

In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein–protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregressive models and Granger causality to estimate nonlinear gene regulatory networks from time series microarray data. Most of the models available in the literature assume linearity in the inference of gene connections; moreover, these models do not infer directionality in these connections. Thus, a priori biological knowledge is required. However, in pathological cases, no a priori biological information is available. To overcome these problems, we present the nonlinear vector autoregressive (NVAR) model. We have applied the NVAR model to estimate nonlinear gene regulatory networks based entirely on gene expression profiles obtained from DNA microarray experiments. We show the results obtained by NVAR through several simulations and by the construction of three actual gene regulatory networks (p53, NF-κB, and c-Myc) for HeLa cells.

scholarly journals An Evaluation of Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Component

2018 ◽  
Author(s):  
Sergii Babichev
Keyword(s):  
Gene Expression ◽  
Information Technology ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Noise Component ◽  
Gene Regulatory ◽  
Processing Stability ◽  
Different Levels

The paper presents the results of the research concerning an evaluation of information 1 technology of gene expression profiles processing stability with the use of gene expression profiles 2 with different levels of noise component. The information technology is presented as a structural 3 block-chart, which contains all stages of the studied data processing. The hybrid model of objective 4 clustering based on SOTA algorithm and the technology of gene regulatory networks reconstruction 5 have been studied to evaluate the stability to the level of the noise component. The results of the 6 simulation have shown that the hybrid model of objective clustering has high level of stability 7 to noise component and vice versa, the technology of gene regulatory networks reconstruction is 8 very sensitivity to level of noise component. The obtained results indicate the importance of gene 9 expression profiles preprocessing at early stage of gene regulatory network reconstruction in order to 10 remove background noise and non-informative genes in terms of used criteria

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