Predicting growth of the healthy infant using a genome scale metabolic model.
Journal article, 2017

An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.


Avlant Nilsson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Adil Mardinoglu

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

NPJ systems biology and applications

2056-7189 (ISSN)

Vol. 3 3-

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Bioinformatics and Systems Biology





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