Mechanistic Insights into Metabolic Disturbance during Type-2 Diabetes and Obesity Using Qualitative Networks

Beyer, Antje; Thomason, Peter; Li, Xinzhong; Scott, James; Fisher, Jasmin

doi:10.1007/978-3-642-11712-1_4

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Glucokinase (hexokinase 4) regulator ( GCKR ) inhibits glucokinase in the liver, and is linked to lipid levels (45, 46), type II diabetes risk (47) and fatty liver in obesity (48). MLX-interacting protein-like ( MLXIPL ) encodes a protein which activates genes involved in triglyceride synthesis, thus is associated with plasma triglycerides (49, 50) and possibly due to the association with carbohydrate in this pathway, is also implicated in type II diabetes risk (51, 52). , and Tribbles Pseudokinase 1 ( TRIB1) is expressed in the liver and associated with lipid levels and cardiovascular diseases in humans (45, 53); in mice it is shown to reduce VLDL particle production, and so reduce plasma triglyceride levels (54).…”

Section: Genetics Of Non-conventional Lipoprotein Fractionsmentioning

confidence: 99%

Genetics of Non-conventional Lipoprotein Fractions

Frazier-Wood

2015

Curr Genet Med Rep

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Lipoprotein subclass measures associate with cardiometabolic disease risk. Currently the information that lipoproteins convey on disease risk over that of traditional demographic and lipid measures is minimal, and so their use is clinics is limited. However, lipoprotein subclass perturbations represent some of the earliest manifestations of metabolic dysfunction, and their etiology is partially distinct from lipids, so information on the genetic etiology of lipoproteins offers promise for improved risk prediction, and unique mechanistic insights into IR and atherosclerosis. Here, I review the genetic variants validated as associating with lipoprotein measures to date, and show that the majority of identified variants have functionality that is best understood as related to lipid measures. Until we focus on the genes as they relate to lipoprotein subclass production, we are limiting our understanding of biological mechanisms underlying cardiometabolic disease.

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Section: Genetics Of Non-conventional Lipoprotein Fractionsmentioning

confidence: 99%

Genetics of Non-conventional Lipoprotein Fractions

Frazier-Wood

2015

Curr Genet Med Rep

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“…Values of variables change gradually according to interactions between the proteins. QNs and similar formalisms (e.g., genetic regulatory networks [17]) are simple enough to be represented graphically and expressive enough to capture interesting biological phenomena (e.g., [2,14,15,17]).…”

Section: Analyzing Biological Networkmentioning

confidence: 99%

Bma: Visual Tool for Modeling and Analyzing Biological Networks

Benque

Bourton

Cockerton

et al. 2012

Computer Aided Verification

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BioModel Analyzer (bma) is a tool for modeling and analyzing biological networks. Designed with a lightweight graphical user interface, the tool facilitates usage for biologists with no previous knowledge in programming or formal methods. The current implementation analyzes systems to establish stabilization. The results of the analysiswhether they be proofs or counterexamples-are represented visually. This paper describes the approach to modeling used in bma and also notes soon-to-be-released extensions to the tool.

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“…The motivation for our research is to provide a similar tool for analyzing evolving graphs, an area of interest in many research communities, Analysis of evolving graphs has been receiving increasing attention, with most progress taking place in the last decade [1,6,10,17,19,21]. Some areas where evolving graphs are being studied are social network analysis [8,14,15,20], biological networks [2,3,22] and the Web [7,18].…”

Section: Introductionmentioning

confidence: 99%

Towards a Distributed Infrastructure for Evolving Graph Analytics

Moffitt

Stoyanovich

2016

Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion

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Graphs are used to represent a plethora of phenomena, from the Web and social networks, to biological pathways, to semantic knowledge bases. Arguably the most interesting and important questions one can ask about graphs have to do with their evolution. Which Web pages are showing an increasing popularity trend? How does influence propagate in social networks? How does knowledge evolve? In this paper we present our ongoing work on the Portal system, an open-source distributed framework for evolving graphs. Portal streamlines exploratory analysis of evolving graphs, making it efficient and usable, and providing critical tools to computational and data scientists. Our system implements a declarative query language by the same name, which we briefly describe in this paper. Our basic abstraction is a TGraph, which logically represents a series of adjacent snapshots. We present different physical representations of TGraphs and show results of a preliminary experimental evaluation of these physical representations for an important class of evolving graph analytics.

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Mechanistic Insights into Metabolic Disturbance during Type-2 Diabetes and Obesity Using Qualitative Networks

Cited by 8 publications

References 14 publications

Genetics of Non-conventional Lipoprotein Fractions

Genetics of Non-conventional Lipoprotein Fractions

Bma: Visual Tool for Modeling and Analyzing Biological Networks

Towards a Distributed Infrastructure for Evolving Graph Analytics

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