Bcl-xL inhibits tBid and Bax via distinct mechanisms

2020 ◽  
Author(s):  
Ana Garcia ◽  
Fabronia Murad
Keyword(s):  
Interaction Network ◽  
Complex Interaction ◽  
Cellular Membranes ◽  
Mitochondrial Permeabilization

The proteins of the Bcl-2 family are key regulators of apoptosis. They form a complex interaction network in the cytosol and in the cellular membranes, whose outcome determines mitochondrial permeabilization...

scholarly journals Quantitative Cooperation Analysis among Cross-chain Smart Contracts

2021 ◽  
Author(s):  
Hong Su
Keyword(s):  
Interaction Network ◽  
Complex Interaction ◽  
Edge Weight ◽  
Smart Contracts ◽  
Different Types ◽  
The Cross

<div>In cross-chain scenarios, there are different blockchains, which need to cooperate. Cooperation among different blockchains is done by smart contracts that work together to complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interaction network, which makes it difficult to evaluate the cooperation. It needs a common model to quantitatively analyze the cross-chain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the condition-trigger model. Then we propose the method to calculate the cooperation probabilities by the graph weight. As the edge weight lacks the information of interaction probabilities, we introduce the dimension of the edge weight to calculate the probabilities. Finally, we verify the proposed condition-trigger model and its different types. It demonstrates that our proposed methods can effectively analyze the cross-chain cooperation among smart contracts.</div>

scholarly journals Quantitative Cooperation Analysis among Cross-chain Smart Contracts

2021 ◽  
Author(s):  
Hong Su
Keyword(s):  
Interaction Network ◽  
Complex Interaction ◽  
Edge Weight ◽  
Smart Contracts ◽  
Different Types ◽  
The Cross

<div>In cross-chain scenarios, there are different blockchains, which need to cooperate. Cooperation among different blockchains is done by smart contracts that work together to complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interaction network, which makes it difficult to evaluate the cooperation. It needs a common model to quantitatively analyze the cross-chain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the condition-trigger model. Then we propose the method to calculate the cooperation probabilities by the graph weight. As the edge weight lacks the information of interaction probabilities, we introduce the dimension of the edge weight to calculate the probabilities. Finally, we verify the proposed condition-trigger model and its different types. It demonstrates that our proposed methods can effectively analyze the cross-chain cooperation among smart contracts.</div>

The HDL synapse: Decoding a complex interaction network of HDL residing proteins and endothelial cell surface receptors

2020 ◽  
Vol 315 ◽  
pp. e10
Author(s):  
K. Frey ◽  
S. Goetze ◽  
M. Mueller ◽  
L. Rohrer ◽  
A. Von Eckardstein ◽  
...  
Keyword(s):  
Endothelial Cell ◽  
Cell Surface ◽  
Interaction Network ◽  
Complex Interaction ◽  
Cell Surface Receptors ◽  
Surface Receptors ◽  
Endothelial Cell Surface

scholarly journals The complex interaction network among multiple invasive bird species in a cavity-nesting community

2012 ◽  
Vol 15 (2) ◽  
pp. 429-445 ◽  
Author(s):  
Yotam Orchan ◽  
François Chiron ◽  
Assaf Shwartz ◽  
Salit Kark
Keyword(s):  
Bird Species ◽  
Interaction Network ◽  
Complex Interaction ◽  
Cavity Nesting

scholarly journals AML-Related NPM Mutations Drive p53 Delocalization into the Cytoplasm with Possible Impact on p53-Dependent Stress Response

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3266
Author(s):  
Aleš Holoubek ◽  
Dita Strachotová ◽  
Petra Otevřelová ◽  
Pavla Röselová ◽  
Petr Heřman ◽  
...  
Keyword(s):  
Stress Response ◽  
Interaction Network ◽  
Complex Interaction ◽  
Cellular Stress Response ◽  
Tumor Suppressor P53 ◽  
Intact Cells ◽  
Oncogenic Potential ◽  
Return Rates ◽  
The Impact ◽  
Oligomerization Domain

Nucleophosmin (NPM) interaction with tumor suppressor p53 is a part of a complex interaction network and considerably affects cellular stress response. The impact of NPM1 mutations on its interaction with p53 has not been investigated yet, although consequences of NPMmut-induced p53 export to the cytoplasm are important for understanding the oncogenic potential of these mutations. We investigated p53-NPM interaction in live HEK-293T cells by FLIM-FRET and in cell lysates by immunoprecipitation. eGFP lifetime-photoconversion was used to follow redistribution dynamics of NPMmut and p53 in Selinexor-treated cells. We confirmed the p53-NPMwt interaction in intact cells and newly documented that this interaction is not compromised by the NPM mutation causing displacement of p53 to the cytoplasm. Moreover, the interaction was not abolished for non-oligomerizing NPM variants with truncated oligomerization domain, suggesting that oligomerization is not essential for interaction of NPM forms with p53. Inhibition of the nuclear exporter XPO1 by Selinexor caused expected nuclear relocalization of both NPMmut and p53. However, significantly different return rates of these proteins indicate nontrivial mechanism of p53 and NPMmut cellular trafficking. The altered p53 regulation in cells expressing NPMmut offers improved understanding to help investigational strategies targeting these mutations.

Introduction to Cell Membranes at Molecular Resolution

1978 ◽  
Vol 36 (3) ◽  
pp. 497-502
Author(s):  
R.J. Barrnett
Keyword(s):  
Cell Membranes ◽  
International Federation ◽  
Mountain Range ◽  
Cellular Membranes ◽  
Short History ◽  
Biological Organization ◽  
Macromolecular Assemblies ◽  
The Past

This subject, is like observing the panorama of a mountain range, magnificent towering peaks, but it doesn't take much duration of observation to recognize that they are still in the process of formation. The mountains consist of approaches, materials and methods and the rocky substance of information has accumulated to such a degree that I find myself concentrating on the foothills in the foreground in order to keep up with the advance; the edifices behind form a wonderous, substantive background. It's a short history for such an accumulation and much of it has been moved by the members of the societies that make up this International Federation. My panel of speakers are here to provide what we hope is an interesting scientific fare, based on the fact that there is a continuum of biological organization from biochemical molecules through macromolecular assemblies and cellular membranes to the cell itself. Indeed, this fact explains the whole range of towering peaks that have emerged progressively during the past 25 years.

Imaging the “social brain” in schizophrenia: A systematic review of neuroimaging studies of social reward and punishment

2020 ◽  
Author(s):  
Jessica Mow ◽  
Arti Gandhi ◽  
Daniel Fulford
Keyword(s):  
Brain Activity ◽  
Interaction Network ◽  
Social Motivation ◽  
Social Impairment ◽  
Future Research ◽  
Social Reward ◽  
Schizophrenia Spectrum Disorders ◽  
Social Stimuli ◽  
Schizophrenia Spectrum ◽  
Reward And Punishment

Decreased social functioning and high levels of loneliness and social isolation are common in schizophrenia spectrum disorders (SSD), contributing to reduced quality of life. One key contributor to social impairment is low social motivation, which may stem from aberrant neural processing of socially rewarding or punishing stimuli. To summarize research on the neurobiology of social motivation in SSD, we performed a systematic literature review of neuroimaging studies involving the presentation of social stimuli intended to elicit feelings of reward and/or punishment. Across 11 studies meeting criteria, people with SSD demonstrated weaker modulation of brain activity in regions within a proposed social interaction network, including prefrontal, cingulate, and striatal regions, as well as the amygdala and insula. Firm conclusions regarding neural differences in SSD in these regions, as well as connections within networks, are limited due to conceptual and methodological inconsistencies across the available studies. We conclude by making recommendations for the study of social reward and punishment processing in SSD in future research.

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