Inference of Gene Regulatory Networks with Variable Time Delay from Time-Series Microarray Data

2013 ◽  
Vol 10 (3) ◽  
pp. 671-687 ◽  
Author(s):  
Ola ElBakry ◽  
M. Omair Ahmad ◽  
M. N. S. Swamy
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Gene Regulatory Networks ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Variable Time Delay ◽  
Variable Time ◽  
Gene Regulatory ◽  
Time Series Microarray

scholarly journals MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yasser Abduallah ◽  
Turki Turki ◽  
Kevin Byron ◽  
Zongxuan Du ◽  
Miguel Cervantes-Cervantes ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Networks ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Experimental Results ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Gene Regulatory ◽  
Information Theoretic Approach ◽  
Time Series Microarray

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Global stability analysis of fractional-order gene regulatory networks with time delay

2019 ◽  
Vol 12 (06) ◽  
pp. 1950067 ◽  
Author(s):  
Zhaohua Wu ◽  
Zhiming Wang ◽  
Tiejun Zhou
Keyword(s):  
Time Delay ◽  
Fractional Order ◽  
Gene Regulatory Networks ◽  
Existence And Uniqueness ◽  
Regulatory Networks ◽  
Lyapunov Method ◽  
Regulation Mechanism ◽  
Global Stability Analysis ◽  
Gene Regulatory ◽  
The Stability

Fractional-order gene regulatory networks with time delay (DFGRNs) have proven that they are more suitable to model gene regulation mechanism than integer-order. In this paper, a novel DFGRN is proposed. The existence and uniqueness of the equilibrium point for the DFGRN are proved under certain conditions. On this basis, the conditions on the global asymptotic stability are established by using the Lyapunov method and comparison theorem for the DFGRN, and the stability conditions are dependent on the fractional-order [Formula: see text]. Finally, numerical simulations show that the obtained results are reasonable.

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