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Identifying important measurements to describe human energy metabolism
Author(s) -
Johnson Heidi A,
Keim Nancy L
Publication year - 2007
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.21.5.a692
Subject(s) - respiratory quotient , triglyceride , metabolite , fatty acid , metabolism , biochemistry , chemistry , protein turnover , lipid metabolism , energy metabolism , amino acid , beta oxidation , carbohydrate metabolism , fatty acid synthesis , energy balance , biology , endocrinology , protein biosynthesis , cholesterol , ecology
Experimental measurements at the cellular and tissue level that are critical to understanding energy metabolism can be difficult to identify. To that end, a computer model of human energy metabolism has been created to predict which parameters are most sensitive to measured inputs to aid in designing an experiment. The model is composed of 6 state variables representing amino acids, body and visceral protein, glucose, triglyceride and fatty acids. Exchanges of metabolites between pools follow mass action kinetics. Energy is created and used depending on metabolite flow. To identify which input parameters are most important to measure in an experiment, a sensitivity analysis was conducted by the method of Liang et al. (2002). Sensitivity values are defined as the change in output relative to a change in input which is then normalized by the output response. A large sensitivity value indicates a critical experimental measure. According to the model, glucose and fatty acid oxidation were most affected by glucose intake (130 and 130) and least sensitive to protein turnover (1.0). Energy balance and respiratory quotient were most sensitive to triglyceride turnover and de novo fatty acid synthesis (87 and 33) and least affected by protein synthesis (3.1). Therefore glucose intake, de novo fatty acid synthesis and triglyceride turnover experimental estimates are critical to understanding differences in energy metabolism.