Network analysis: A new approach to study endocrine disorders

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

    Research output: Contribution to journalArticlepeer-review


    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. © 2014 Society for Endocrinology.
    Original languageEnglish
    Pages (from-to)R79-R93
    JournalJournal of molecular endocrinology
    Issue number1
    Publication statusPublished - 1 Oct 2013


    • Endocrine disruptors
    • Microarray
    • Modelling
    • Network biology
    • Secretion


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