Davidsen, PK, Turan, N, Egginton, S et al. (1 more author) (2016) Multi-level functional genomics data integration as a tool for understanding physiology: A network biology perspective. Journal of Applied Physiology, 120 (3). pp. 297-309. ISSN 8750-7587
Abstract
The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of “omics” technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to “learn” biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 the American Physiological Society. This is an author produced version of a paper published in Journal of Applied Physiology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | systems biology; data integration; genomics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Nov 2015 11:20 |
Last Modified: | 16 Nov 2016 11:00 |
Published Version: | http://dx.doi.org/10.1152/japplphysiol.01110.2014 |
Status: | Published |
Publisher: | American Physiological Society |
Identification Number: | 10.1152/japplphysiol.01110.2014 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:91929 |