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Integrating Cellular Metabolism into a Multiscale Whole-Body Model
Author(s) -
Markus Krauß,
Stephan Schaller,
Steffen Borchers,
Rolf Findeisen,
Jörg Lippert,
Lars Kuepfer
Publication year - 2012
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1002750
Subject(s) - metabolic network , metabolomics , computational biology , organism , cellular metabolism , systems biology , flux balance analysis , detoxification (alternative medicine) , drug discovery , computer science , flux (metallurgy) , biology , bioinformatics , biochemical engineering , metabolism , chemistry , medicine , pathology , biochemistry , paleontology , alternative medicine , organic chemistry , engineering
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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