Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction. Issue 138 (24th January 2018)

• Record Type: Journal Article
• Authors: McGrath, Thomas; Murphy, Kevin G.; Jones, Nick S.
• Is Part Of: Journal of the Royal Society interface. Volume 15:Issue 138(2018)
• Publication Date: 2018-01-24
• Subjects: energy homeostasis -- mathematical biology -- machine learning -- glucostasis; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2017.0736
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight from these large, relatively unstructured datasets. We review both physiological models and machine learning results across a diverse range of applications in energy homeostasis, and highlight how modelling and machine learning can work together to improve predictive ability. We collect quantitative details in a comprehensive mathematical supplement. We also discuss the prospects of forecasting homeostatic behaviour and stress the importance of characterizing stochasticity within and between individuals in order to provide practical, tailored forecasts and guidance to combat the spread of obesity.