Network 1Ursula Klingmüller

Regenerating Hepatocytes - a Systems Biology Approach

Coordinator:HD Dr. Jens TimmerCenter for Data Analysis and ModelingCenter for Systems BiologyDepartment for Mathematics and PhysicsUniversity of Freiburg

Deputy-Coordinator:PD Dr. Ursula KlingmüllerTheodor-Boveri-Group Systems Biology of Signal TransductionGerman Cancer Research Center (DKFZ)Heidelberg

BMBF-Funding Initiative: Systems of Life - Systems Biology

Ursula Klingmüller

Platform Modeling

Platform Cell Biology

Network 1Regeneration

Network 2Detoxificationand Dedifferentiation

Networks: regionally Platforms: nationwide

AimUrsula Klingmüller

Systems Properties of Hepatocytes

Determination of Conditions for in vitro Propagation and Differentiation of Hepatocytes

Hepatocyte Cell Line

Network 1Regeneration

Platform Modeling

Platform Cell Biology

Network 2Detoxificationand Dedifferentiation

Systems Biology of Regenerating HepatocytesUrsula Klingmüller

Data-based Mathematical Modeling of Signaling Pathways Involved in Hepatocyte Regeneration

Systems Properties of Regenerating Hepatocytes

Platform Modeling

Platform Cell Biology

Network 2Detoxificationand Dedifferentiation

Network 1Regeneration

Regeneration of HepatocytesUrsula Klingmüller

Highly Regulated Growth Process

Consortium:Freiburg/Heidelberg/Tübingen/Würzburg

Ursula Klingmüller

Hepatocytesvon Weizsäcker

ModelingTimmer

Signaling Pathways1. Klingmüller

2. Walz/Merford/Sparna

3. Mohr

5. Borner

6. Klingmüller

Transcription Factors7. Schütz/Nordheim

Transcription Factors8. Donauer/Walz

8

4. Hecht

Catenin

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

Success Story:Data-based Modeling of the JAK-STAT Pathway

Ursula Klingmüller

collaboration with the group of HD Dr. J. Timmer

Model 1:Feed Forward Cascade

Ursula Klingmüller

Model 2:Cycling

Ursula Klingmüller

Hypothesis Testing:Mathematical Modeling of Quantitative Data

Ursula Klingmüller

Model 1 Model 2

In Silico Prediction:Unobservable Components

Ursula Klingmüller

In Silico Prediction:Targets for Efficient Perturbation

Ursula Klingmüller

Transcriptional Yield

Setting k4 = 0 or =

One cylce yields only 45% efficiency

Most sensitive to nuclear shuttling parameters

Experimental Validation of PredictionUrsula Klingmüller

New Knowledge Generated:JAK-STAT Pathway „Remote Sensor“

Ursula Klingmüller

PNAS 100, 2003, 1028-1033

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

The Challenge:Quantitative Time/Space Resolved Data

Ursula Klingmüller

Purification by Anti-EpoRImmunoprecipitation

ImmunoblotAnti-EpoR

pEpoR

Quantitative Immunoblotting

0 2 4 6 108 121 2014 16 4030 50 6018 26 2822+ Epo

Quantitative Proteinarray

Summary - VisionUrsula 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