An Energy Model for Detecting Community in PPI Networks

2015 ◽  
pp. 119-127 ◽  
Author(s):  
Yin Pang ◽  
Lin Bai ◽  
Kaili Bu
Keyword(s):  
Energy Model ◽  
Ppi Networks

Hydrogen Atom Transfer Reaction Free Energy as a Predictor of Abiotic Nitroaromatic Reduction Rate Constants: A Comprehensive Analysis

2019 ◽  
Author(s):  
Dominic Di Toro ◽  
Kevin P. Hickey ◽  
Herbert E. Allen ◽  
Richard F. Carbonaro ◽  
Pei C. Chiu
Keyword(s):  
Free Energy ◽  
Hydrogen Atom ◽  
Hydrogen Atom Transfer ◽  
Energy Model ◽  
Second Order ◽  
Transfer Model ◽  
Atom Transfer ◽  
Order Rate ◽  
Linear Free Energy ◽  
Free Energy Model

<div>A linear free energy model is presented that predicts the second order rate constant for the abiotic reduction of nitroaromatic compounds (NACs). For this situation previously presented models use the one electron reduction potential of the NAC reaction. If such value is not available, it has been has been proposed that it could be computed directly or estimated from the electron affinity (EA). The model proposed herein uses the Gibbs free energy of the hydrogen atom transfer (HAT) as the parameter in the linear free energy model. Both models employ quantum chemical computations for the required thermodynamic parameters. The available and proposed models are compared using second order rate constants obtained from five investigations reported in the literature in which a variety of NACs were exposed to a variety of reductants. A comprehensive analysis utilizing all the NACs and reductants demonstrate that the computed hydrogen atom transfer model and the experimental one electron reduction potential model have similar root mean square errors and residual error probability distributions. In contrast, the model using the computed electron affinity has a more variable residual error distribution with a significant number of outliers. The results suggest that a linear free energy model utilizing computed hydrogen transfer reaction free energy produces a more reliable prediction of the NAC abiotic reduction second order rate constant than previously available methods. The advantages of the proposed hydrogen atom transfer model and its mechanistic implications are discussed as well.</div>

Explore the mechanism of Swertia mussotii Franch. for hepatoprotective effects with iTRAQ-LC-MS/MS

2020 ◽  
Vol 23 ◽  
Author(s):  
Haixia Yun ◽  
Xinyu Wu ◽  
Yiwei Ding ◽  
Wendou Xiong ◽  
Xianglan Duan ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Carbon Tetrachloride ◽  
Liver Injury ◽  
Aspartate Aminotransferase ◽  
Alanine Aminotransferase ◽  
Total Bilirubin ◽  
Acute Liver Injury ◽  
Proteome Analysis ◽  
Ppi Networks ◽  
Hepatoprotective Effects

Background and Objective : A Tibetan traditional herb named Swertia mussotii Franch., also called “Zangyinchen” by the local people of Qinghai-Tibet area, has been used to protect the liver from injury for many years. However, the curative effect and molecular mechanism of the herb have not been demonstrated clearly. Materials and Methods: In our study, serum alanine aminotransferase, aspartate aminotransferase, total bilirubin levels were examined after S. mussotii Franch. treatment in the acute liver injury of the carbon tetrachloride-induced rat model. Then, Proteome Analysis was applied to explore the potential mechanism of SMT for hepatoprotective effects after iTRAQLC-MS/MS analysis (isobaric tag for relative and absolute quantification-liquid chromatograph-mass spectrometer with tandem mass spectrometry). Results: Serum results showed, alanine aminotransferase, aspartate aminotransferase, total bilirubin levels of rats with acute liver injury were all improved with SMT treatment. Moreover, Proteome Analysis suggested that, with S. Mussotii Franch. treatment, the levels of lipid catabolic process and lipid homeostasis were all enhanced. And the results of protein-protein interaction (PPI) analysis illustrated that these proteins assembled in PPI networks were found almost significantly enriched in response to lipid, negative regulation of lipase activity, response to lipopolysaccharide etc. Furthermore, the downregulated MRP14 and MRP8 proteins were found involved in the lipid metabolism, which may indicate the mechanism of SMT protection liver from ALI induced by carbon tetrachloride. Conclusion: SMT herb could play a role in hepatoprotection and alleviate the effect of acute liver injury by impacting the lipid metabolism associated biological process.

Axin PPI Networks: New Interacting Proteins and New Targets?

2016 ◽  
Vol 16 (30) ◽  
pp. 3678-3690
Author(s):  
Xiaomin Song ◽  
Wenwen Cai ◽  
Lin Li
Keyword(s):  
Interacting Proteins ◽  
New Targets ◽  
Ppi Networks

Identification of key mRNAs and lncRNAs involved in the effects of anti-TWEAK on Osteosarcoma

2020 ◽  
Vol 15 ◽  
Author(s):  
Mingxuan Yang ◽  
Liangtao Zhao ◽  
Xuchang Hu ◽  
Haijun Feng ◽  
Xuewen Kang
Keyword(s):  
Dna Replication ◽  
Signaling Pathway ◽  
Bioinformatics Analysis ◽  
Mapk Signaling ◽  
Migration And Invasion ◽  
Type I ◽  
Interferon Signaling ◽  
Nf Kappa B ◽  
Ppi Networks ◽  
Coexpression Networks

Background: Osteosarcoma (OS) is one of the most common primary malignant bone tumors in teenagers. Emerging studies demonstrated TWEAK and Fn14 were involved in regulating cancer cell differentiation, proliferation, apoptosis, migration and invasion. Objective: The present study identified differently expressed mRNAs and lncRNAs after anti-TWEAK treatment in OS cells using GSE41828. Methods: We identified 922 up-regulated mRNAs, 863 downregulated mRNAs, 29 up-regulated lncRNAs, and 58 down-regulated lncRNAs after anti-TWEAK treatment in OS cells. By constructing PPI networks, we identified several key proteins involved in anti-TWEAK treatment in OS cells, including MYC, IL6, CD44, ITGAM, STAT1, CCL5, FN1, PTEN, SPP1, TOP2A, and NCAM1. By constructing lncRNAs coexpression networks, we identified several key lncRNAs, including LINC00623, LINC00944, PSMB8-AS1, LOC101929787. Result: Bioinformatics analysis revealed DEGs after anti-TWEAK treatment in OS were involved in regulating type I interferon signaling pathway, immune response related pathways, telomere organization, chromatin silencing at rDNA, and DNA replication. Bioinformatics analysis revealed differently expressed lncRNAs after antiTWEAK treatment in OS were related to telomere organization, protein heterotetramerization, DNA replication, response to hypoxia, TNF signaling pathway, PI3K-Akt signaling pathway, Focal adhesion, Apoptosis, NF-kappa B signaling pathway, MAPK signaling pathway, FoxO signaling pathway. Conclusion: : This study provided useful information for understanding the mechanisms of TWEAK underlying OS progression and identifying novel therapeutic markers for OS.

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

On the limits of active module identification

2021 ◽  
Author(s):  
Olga Lazareva ◽  
Jan Baumbach ◽  
Markus List ◽  
David B Blumenthal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Diameter ◽  
Extensive Study ◽  
Biological Knowledge ◽  
Expression Data ◽  
Module Identification ◽  
Ppi Networks ◽  
Novel Algorithms ◽  
Context Specific

Abstract In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

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