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2007 CompTox Forum
Abstract - A Quantitative Understanding of Dynamic Cellular Processes During Detoxification in Human Hepatocytes (Research Network Within Hepatosys)
Matthias Reuss
University Professor for Biochemical Engineering
Universität Stuttgart, Institut fϋr Bioverfahrenstechnik
Allmandring 31, D-70569 Stuttgart, Germany
Phone: 49-711-685-64573
E-mail: reuss@ibvt.uni-stuttgart.de
The contribution aims at introducing the German government funding initiative on Systems Biology of Hepatocytes (HepatoSys), thereby focusing on the more detailed description of one of the networks within the initiative coordinated by the University Stuttgart. The 12 research groups within this network are addressing the issue of detoxification processes in human hepatocytes. The contribution of the individual partners are outlined in the introduction of the lecture.
The second part of the contribution focuses on more specific results on dynamic modeling of the detoxification system based on quantitative measurements of various metabolites of Phase I and Phase II reactions, Micro Array Data, and flux analysis of the central metabolism. The detoxification metabolism shows a high inter-individual variability in the enzyme expression level, especially in the phase I catalyzing cytochrome P450 monooxygenases (CYP). This is caused by individual food and drug treatment, sex, age, diseases, or due to the polymorphism resulting in phenotype plasticity. However, the detoxification functionality has to be maintained against these external and internal perturbations, characterizing its robustness. Based on different mathematical models for structure and dynamics of the detoxification system, the important issues of structural and dynamic robustness are discussed.
Superimposed to the metabolism responsible for the detoxification we also tackle the complex phenomena related to the genetic regulation of the expression of the various enzymes. The complexities arise due to the communication between different signaling networks (Crosstalk), ligand competition, and drug-drug interactions, to name but a few. In addition, the array of transporters involved in coordinating the entry and exit of the compounds into and out of the system adds another dimension to this complexity. Based upon time series of transcript data a Boolean/Probabilistic Boolean framework is presented to reconstruct the regulatory networks governing the activity of a specific CYP in response to a specific drug. In comparison with the common methods in transcriptome analysis, like clustering and co-expression analysis, it is shown that there are some interactions that are highly critical for the drug metabolism to work properly. New hitherto unknown interactions were identified and new hypotheses can be developed, and concurrently experiments can be conducted to confirm the validity of the model findings.
The final part of the lecture introduces and discuss a new approach of instationary 13C flux analysis for quantitatively describing the metabolic traffic within the central metabolism under different physiological conditions. This flux analysis provides a convenient basis for quantifying cell physiology in terms of engagement of metabolic pathways in overall cellular processes and also in context with detoxification.