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Integrating Cellular Metabolism into a Multiscale Whole-Body Model

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Integrating Cellular Metabolism into a Multiscale Whole-Body Model
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002750
Pubmed ID
Authors

Markus Krauss, Stephan Schaller, Steffen Borchers, Rolf Findeisen, Jörg Lippert, Lars Kuepfer

Abstract

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 264 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
United Kingdom 4 2%
Italy 2 <1%
Netherlands 2 <1%
Japan 2 <1%
Norway 1 <1%
Finland 1 <1%
Switzerland 1 <1%
Germany 1 <1%
Other 7 3%
Unknown 237 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 78 30%
Student > Ph. D. Student 67 25%
Student > Master 22 8%
Professor > Associate Professor 16 6%
Student > Bachelor 14 5%
Other 44 17%
Unknown 23 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 100 38%
Biochemistry, Genetics and Molecular Biology 25 9%
Engineering 21 8%
Computer Science 18 7%
Medicine and Dentistry 17 6%
Other 44 17%
Unknown 39 15%