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Nutritional systems biology modeling: from molecular mechanisms to physiology.

Review article
Authors Albert A de Graaf
Andreas P Freidig
Baukje De Roos
Neema Jamshidi
Matthias Heinemann
Johan A C Rullmann
Kevin D Hall
Martin Adiels
Ben van Ommen
Published in PLoS computational biology
Volume 5
Issue 11
Pages e1000554
ISSN 1553-7358
Publication year 2009
Published at Wallenberg Laboratory
Institute of Medicine, Department of Molecular and Clinical Medicine
Pages e1000554
Language en
Links dx.doi.org/10.1371/journal.pcbi.100...
Keywords Systems biology, physiology models, kinetics
Subject categories Applied mathematics, Clinical physiology, Diabetology, Endocrinology, Nutrition and Dietetics

Abstract

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.

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