scholarly journals Determining the Maximum States of the Ensemble Distribution of Boolean Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaodong Cui ◽  
Binghao Ren ◽  
Zhenghan Li
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Expression Patterns ◽  
Boolean Networks ◽  
Gene Expression Patterns ◽  
Regulation Mechanism ◽  
Microarray Technology ◽  
State Determination

Inference of the gene regulation mechanism from gene expression patterns has become increasingly popular, in recent years, with the advent of microarray technology. Obtaining the states of genes and their regulatory relationships would greatly enable the scientists to investigate and understand the mechanisms of the diseases. However, it is still a big challenge to determine relationships from several thousands of genes. Here, we simplify the above complex gene state determination problem as an inference of the distribution of the ensemble Boolean networks (BNs). In order to investigate and calculate the distribution of the BNs’ states, we first compute the probabilities of the different BNs’ states and obtain the number of states Ω. Then, we find the maximum possible distribution of the number of the BNs’ states and calculate the fluctuation of the distribution. Finally, two representative experiments are conducted, and the efficiency of the obtained results is verified. The proposed algorithm is conceptually concise and easily applicable to many other realistic models; furthermore, it is highly extensible for various situations.

scholarly journals Analysis of gene expression patterns in breast cancer by microarray technology

10.1038/87096 ◽  
2001 ◽  
Vol 27 (S4) ◽  
pp. 56-56
Author(s):  
Nalan Gokgoz ◽  
Xiang Sun ◽  
Shelly Bull ◽  
Jim Woodgett ◽  
Irene Andrulis
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Technology

Use of Microarray Technology to Profile Gene Expression Patterns Important for Reproduction in Cattle

2008 ◽  
Vol 43 ◽  
pp. 359-367 ◽  
Author(s):  
ACO Evans ◽  
N Forde ◽  
GM OGorman ◽  
AE Zielak ◽  
P Lonergan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Technology

scholarly journals Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Mario Flores ◽  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Eric Y. Chuang ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Data Analysis ◽  
Expression Patterns ◽  
Preliminary Finding ◽  
Next Generation Sequencing Data ◽  
Regulation Mechanism ◽  
Sequencing Data ◽  
Rna Regulation ◽  
Network Construction

Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.

scholarly journals Co-Expression Network-based Analysis associated with potato initial resistance

2018 ◽  
Author(s):  
Lang Yan ◽  
Xianjun Lai ◽  
Yan Wu ◽  
Xuemei Tan ◽  
Yizheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Evolutionary Divergence ◽  
Regulation Mechanism ◽  
Rna Seq ◽  
Functional Studies ◽  
Study Gene Expression ◽  
Gene Modules ◽  
Dramatic Shift

RNA sequencing (RNA-seq) providing genome-wide expression datasets has been successfully used to study gene expression patterns and regulation mechanism among multiple samples. Gene co-expression networks (GCNs) studies within or across species showed that coordinated genes in expression patterns are often functionally related. For potatoes, a large amount of publicly available transcriptome datasets have been generated but an optimal GCN detecting expression patterns in different genotypes, tissues and environmental conditions, is lacking. We constructed a potato GCN using 16 published RNA-Seq datasets covering 11 cultivars from native habitat worldwide. The correlations of gene expression were assessed pair-wisely and biologically meaningful gene modules which are highly connected in GCN were identified. One of the primitively native-farmer-selected cultivars in the Andes, ssp.Andigena, had relative far distance in gene expression patterns with other modern varieties. GCN in further enriched 134 highly and specifically co-expressed genes in ssp.Andigena associated with potato disease and stress resistance, which underlying the dramatic shift in evolutionary pressures during potato artificial domestication. In total, the network was consisted of into 14 gene models that involves in a variety of plant processes, which sheds light on how gene modules organized intra- and inter-varieties in the context of evolutionary divergence and provides a basis of information resource for potato gene functional studies.

Gene expression patterns in blood predict future severe endpoints in community acquired pneumonia

Pneumologie ◽  
2018 ◽  
Vol 72 (S 01) ◽  
pp. S8-S9
Author(s):  
M Bauer ◽  
H Kirsten ◽  
E Grunow ◽  
P Ahnert ◽  
M Kiehntopf ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Community Acquired Pneumonia ◽  
Gene Expression Patterns
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