Effect of Asthma on Erectile Dysfunction in Rats as Determined by Biological Network Analysis

Abstract Motivation The assessment of graphs through crisp numerical metrics has long been a hallmark of biological network analysis. However, typical graph metrics ignore regulatory signals that are crucially important for optimal pathway operation, for instance, in biochemical or metabolic studies. Here we introduce adjusted metrics that are applicable to both static networks and dynamic systems. Results The metrics permit quantitative characterizations of the importance of regulation in biochemical pathway systems, including systems designed for applications in synthetic biology or metabolic engineering. They may also become criteria for effective model reduction. Availability and implementation The source code is available at https://gitlab.com/tienbien44/metrics-bsa

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Statistical Null Models for Biological Network Analysis

Statistical and Evolutionary Analysis of Biological Networks ◽

10.1142/9781848164345_0008 ◽

2009 ◽

pp. 145-165

Author(s):

WILLIAM P. KELLY ◽

THOMAS THORNE ◽

MICHAEL P. H. STUMPF

Keyword(s):

Network Analysis ◽

Biological Network ◽

Null Models ◽

Biological Network Analysis

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Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis

Journal of Personalized Medicine ◽

10.3390/jpm10040162 ◽

2020 ◽

Vol 10 (4) ◽

pp. 162

Author(s):

Siyuan Huang ◽

Yong-Kai Wei ◽

Satyavani Kaliamurthi ◽

Yanghui Cao ◽

Asma Sindhoo Nangraj ◽

...

Keyword(s):

Network Analysis ◽

Lung Adenocarcinoma ◽

Signaling Pathway ◽

Target Genes ◽

Biological Network ◽

Mapk Signaling ◽

Surgical Operation ◽

Biological Network Analysis ◽

Potential Biomarker ◽

Key Pathways

Analysis of circulating miRNAs (cmiRNAs) before surgical operation (BSO) and after the surgical operation (ASO) has been informative for lung adenocarcinoma (LUAD) diagnosis, progression, and outcomes of treatment. Thus, we performed a biological network analysis to identify the potential target genes (PTGs) of the overexpressed cmiRNA signatures from LUAD samples that had undergone surgical therapy. Differential expression (DE) analysis of microarray datasets, including cmiRNAs (GSE137140) and cmRNAs (GSE69732), was conducted using the Limma package. cmiR-1246 was predicted as a significantly upregulated cmiRNA of LUAD samples BSO and ASO. Then, 9802 miR-1246 target genes (TGs) were predicted using 12 TG prediction platforms (MiRWalk, miRDB, and TargetScan). Briefly, 425 highly expressed overlapping miRNA-1246 TGs were observed between the prediction platform and the cmiRNA dataset. ClueGO predicted cell projection morphogenesis, chemosensory behavior, and glycosaminoglycan binding, and the PI3K–Akt signaling pathways were enriched metabolic interactions regulating miRNA-1245 overlapping TGs in LUAD. Using 425 overlapping miR-1246 TGs, a protein–protein interaction network was constructed. Then, 12 PTGs of three different Walktrap modules were identified; among them, ubiquitin-conjugating enzyme E2C (UBE2C), troponin T1(TNNT1), T-cell receptor alpha locus interacting protein (TRAIP), and ubiquitin c-terminal hydrolase L1(UCHL1) were positively correlated with miR-1246, and the high expression of these genes was associated with better overall survival of LUAD. We conclude that PTGs of cmiRNA-1246 and key pathways, namely, ubiquitin-mediated proteolysis, glycosaminoglycan binding, the DNA metabolic process, and the PI3K–Akt–mTOR signaling pathway, the neurotrophin and cardiomyopathy signaling pathway, and the MAPK signaling pathway provide new insights on a noninvasive prognostic biomarker for LUAD.

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