1. Network 1 Ursula Klingmüller Regenerating Hepatocytes - a Systems Biology Approach Coordinator: HD Dr. Jens Timmer Center for Data Analysis and Modeling Center for Systems Biology Department for Mathematics and Physics University of Freiburg Deputy-Coordinator: PD Dr. Ursula Klingmüller Theodor-Boveri-Group Systems Biology of Signal Transduction German Cancer Research Center (DKFZ) Heidelberg

2. BMBF-Funding Initiative: Systems of Life - Systems Biology Ursula Klingmüller Platform Modeling Platform Cell Biology Network 1 Regeneration Network 2 Detoxification and Dedifferentiation Networks: regionally Platforms: nationwide

3. Aim Ursula Klingmüller Systems Properties of Hepatocytes Determination of Conditions for in vitro Propagation and Differentiation of Hepatocytes Hepatocyte Cell Line Network 1 Regeneration Platform Modeling Platform Cell Biology Network 2 Detoxification and Dedifferentiation

4. Systems Biology of Regenerating Hepatocytes Ursula Klingmüller Data-based Mathematical Modeling of Signaling Pathways Involved in Hepatocyte Regeneration Systems Properties of Regenerating Hepatocytes Platform Modeling Platform Cell Biology Network 2 Detoxification and Dedifferentiation Network 1 Regeneration

5. Regeneration of Hepatocytes Ursula Klingmüller Highly Regulated Growth Process

6. Consortium: Freiburg/Heidelberg/Tübingen/Würzburg Ursula Klingmüller Hepatocytes von Weizsäcker Modeling Timmer Signaling Pathways 1.Klingmüller 2.Walz/Merford/Sparna 3.Mohr 5.Borner 6.Klingmüller Transcription Factors 7.Schütz/Nordheim Transcription Factors 8.Donauer/Walz 8 4.Hecht  Catenin

7. Progress: Standard Operating Procedures (SOPs) Ursula Klingmüller Cultivation of Primary Hepatocytes: Defined Medium Starving Procedure Preparation of Primary Hepatocytes by Collagenase Perfusion: C57/BL6 mice 6-8 weeks old male

8. Success Story: Data-based Modeling of the JAK-STAT Pathway Ursula Klingmüller collaboration with the group of HD Dr. J. Timmer

9. Model 1: Feed Forward Cascade Ursula Klingmüller

10. Model 2: Cycling Ursula Klingmüller

11. Hypothesis Testing: Mathematical Modeling of Quantitative Data Ursula Klingmüller Model 1Model 2

12. In Silico Prediction: Unobservable Components Ursula Klingmüller

13. In Silico Prediction: Targets for Efficient Perturbation Ursula Klingmüller Transcriptional Yield Setting k 4 = 0 or  =  One cylce yields only 45% efficiency Most sensitive to nuclear shuttling parameters

14. Experimental Validation of Prediction Ursula Klingmüller

15. New Knowledge Generated: JAK-STAT Pathway „Remote Sensor“ Ursula Klingmüller PNAS 100, 2003, 1028-1033

16. New Knowledge Generated: JAK-STAT Pathway „Remote Sensor“ Ursula Klingmüller  Continuous monitoring of receptor activity  Optimal use of limited STAT5 pool PNAS 100, 2003, 1028-1033

17. The Challenge: Quantitative Time/Space Resolved Data Ursula Klingmüller Purification by Anti-EpoR Immunoprecipitation Immunoblot Anti-EpoR pEpoR Quantitative Immunoblotting 02461081212014164030506018262822 + Epo Quantitative Proteinarray

18. Summary - Vision Ursula Klingmüller  Modular data-based models of signaling pathways involved in hepatocyte regeneration  Incorporation of cross-talk  Generation of interconnected “big“ model Identification of conditions for in vitro expansion and differentiation of hepatocytes