Literature DB >> 24085748

Network analysis: a new approach to study endocrine disorders.

A Stevens1, C De Leonibus, D Hanson, A W Dowsey, A Whatmore, S Meyer, R P Donn, P Chatelain, I Banerjee, K E Cosgrove, P E Clayton, M J Dunne.   

Abstract

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

Entities:  

Keywords:  endocrine disruptors; microarray; modelling; network biology; secretion

Mesh:

Year:  2013        PMID: 24085748     DOI: 10.1530/JME-13-0112

Source DB:  PubMed          Journal:  J Mol Endocrinol        ISSN: 0952-5041            Impact factor:   5.098


  12 in total

1.  Left-sided congenital heart lesions in mosaic Turner syndrome.

Authors:  Nouha Bouayed Abdelmoula; Balkiss Abdelmoula; Walid Smaoui; Imen Trabelsi; Rim Louati; Samir Aloulou; Wafa Aloulou; Fatma Abid; Senda Kammoun; Khaled Trigui; Olfa Bedoui; Hichem Denguir; Souad Mallek; Mustapha Ben Aziza; Jamila Dammak; Oldez Kaabi; Nawel Abdellaoui; Fatma Turki; Asma Kaabi; Wafa Kamoun; Jihen Jabeur; Wided Ltaif; Kays Chaker; Haytham Fourati; Samir M'rabet; Hedi Ben Ameur; Naourez Gouia; Mohamed Nabil Mhiri; Tarek Rebai
Journal:  Mol Genet Genomics       Date:  2017-12-01       Impact factor: 3.291

Review 2.  A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction.

Authors:  Jordi Merino; Miriam S Udler; Aaron Leong; James B Meigs
Journal:  Curr Diab Rep       Date:  2017-11-04       Impact factor: 4.810

Review 3.  Systems Medicine: The Application of Systems Biology Approaches for Modern Medical Research and Drug Development.

Authors:  Duncan Ayers; Philip J Day
Journal:  Mol Biol Int       Date:  2015-08-18

4.  Atypical Forms of Congenital Hyperinsulinism in Infancy Are Associated With Mosaic Patterns of Immature Islet Cells.

Authors:  Bing Han; Zainab Mohamed; Maria Salomon Estebanez; Ross J Craigie; Melanie Newbould; Edmund Cheesman; Raja Padidela; Mars Skae; Matthew Johnson; Sarah Flanagan; Sian Ellard; Karen E Cosgrove; Indraneel Banerjee; Mark J Dunne
Journal:  J Clin Endocrinol Metab       Date:  2017-09-01       Impact factor: 5.958

5.  Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease.

Authors:  Xiao Miao; Xinlin Chen; Zhijun Xie; Honghuang Lin
Journal:  Sci Rep       Date:  2018-07-31       Impact factor: 4.379

6.  Prediction of hub genes associated with intramuscular fat content in Nelore cattle.

Authors:  Danielly Beraldo Dos Santos Silva; Larissa Fernanda Simielli Fonseca; Daniel Guariz Pinheiro; Maria Malane Magalhães Muniz; Ana Fabrícia Braga Magalhães; Fernando Baldi; Jesus Aparecido Ferro; Luis Artur Loyola Chardulo; Lucia Galvão de Albuquerque
Journal:  BMC Genomics       Date:  2019-06-25       Impact factor: 3.969

7.  Network analysis identifies protein clusters of functional importance in juvenile idiopathic arthritis.

Authors:  Adam Stevens; Stefan Meyer; Daniel Hanson; Peter Clayton; Rachelle P Donn
Journal:  Arthritis Res Ther       Date:  2014-05-08       Impact factor: 5.156

8.  Effect of summer daylight exposure and genetic background on growth in growth hormone-deficient children.

Authors:  C De Leonibus; P Chatelain; C Knight; P Clayton; A Stevens
Journal:  Pharmacogenomics J       Date:  2015-10-27       Impact factor: 3.550

Review 9.  Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets.

Authors:  Pablo Ivan Pereira Ramos; Luis Willian Pacheco Arge; Nicholas Costa Barroso Lima; Kiyoshi F Fukutani; Artur Trancoso L de Queiroz
Journal:  Front Genet       Date:  2019-11-13       Impact factor: 4.599

10.  Spliced genes in muscle from Nelore Cattle and their association with carcass and meat quality.

Authors:  Danielly B S Silva; Larissa F S Fonseca; Daniel G Pinheiro; Ana F B Magalhães; Maria M M Muniz; Jesus A Ferro; Fernando Baldi; Luis A L Chardulo; Robert D Schnabel; Jeremy F Taylor; Lucia G Albuquerque
Journal:  Sci Rep       Date:  2020-09-07       Impact factor: 4.379

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