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To Supplement or Not to Supplement: A Metabolic Network Framework for Human Nutritional Supplements

Overview of attention for article published in PLOS ONE, August 2013
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Title
To Supplement or Not to Supplement: A Metabolic Network Framework for Human Nutritional Supplements
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0068751
Pubmed ID
Authors

Christopher D. Nogiec, Simon Kasif

Abstract

Flux balance analysis and constraint based modeling have been successfully used in the past to elucidate the metabolism of single cellular organisms. However, limited work has been done with multicellular organisms and even less with humans. The focus of this paper is to present a novel use of this technique by investigating human nutrition, a challenging field of study. Specifically, we present a steady state constraint based model of skeletal muscle tissue to investigate amino acid supplementation's effect on protein synthesis. We implement several in silico supplementation strategies to study whether amino acid supplementation might be beneficial for increasing muscle contractile protein synthesis. Concurrent with published data on amino acid supplementation's effect on protein synthesis in a post resistance exercise state, our results suggest that increasing bioavailability of methionine, arginine, and the branched-chain amino acids can increase the flux of contractile protein synthesis. The study also suggests that a common commercial supplement, glutamine, is not an effective supplement in the context of increasing protein synthesis and thus, muscle mass. Similar to any study in a model organism, the computational modeling of this research has some limitations. Thus, this paper introduces the prospect of using systems biology as a framework to formally investigate how supplementation and nutrition can affect human metabolism and physiology.

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Geographical breakdown

Country Count As %
Greece 1 2%
Peru 1 2%
Singapore 1 2%
Unknown 56 95%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 13 22%
Student > Ph. D. Student 10 17%
Student > Master 7 12%
Researcher 6 10%
Lecturer 4 7%
Other 13 22%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 31%
Sports and Recreations 10 17%
Biochemistry, Genetics and Molecular Biology 8 14%
Medicine and Dentistry 8 14%
Engineering 2 3%
Other 4 7%
Unknown 9 15%