1. LiSyM- Pillar II Chronic liver disease progression
Introduction
Steven Dooley, Christian Trautwein
Mannheim,

2. AGENDA: Pillar II 1. Introduction of pillar 2 Trautwein/Dooley (10`)
                                                          
2.       Cellular level                  Klingmüller/Timmer/Dooley/Trautwein/Berres  
Cell type specific TGFb signaling and outcome in chronic liver disease progression: Signaling model in hepatic stellate cells and hepatocytes (15`)
Success story – ECM1 restrains liver fibrogenesis as gatekeeper of latent TGFb activation (5`)
3.       Tissue level  Drasdo/Hammad/Hengstler/Dooley/Ghalleb
Modeling of fibrosis pattern formation: From mouse models to human patients of chronic liver disease (15`)
4.       Organ level Saez-Rodriguez/Trautwein/Berres/Dooley/Hammad/Hengstler/Klingmüller/Theis/Müller 
Transfer of regulatory knowledge from human to mouse to identify progression specific biomarkers of liver fibrosis (15`)
 5.       System level Hengstler/Drasdo/Vartak 
Bile flux dynamics and consequences (10`)

3. Complex network in chronic liver disease (CLD): the cellular view
Endothelial cells
Hepatocyte injury
e.g. Drugs, NASH
Quiescent hepatic stellate cells
(HSCs)
Monocytes, T-cells
CCR2
Myofibroblasts
(activated HSCs)
Hepatocytes
Kupffer-cells/
macrophages
Collagen

4. Dynamics of a progressive chronic liver disease: the organ view

5. From experimental data to new targets in personalised medicine
Classical translation bases on extrapolation from mouse to human
Does not take into account physiological/ anatomical inter-species differences
Can by construction not be personalized
Invasive experiments
Validation experiments
Prediction
Prediction
Informs clinician on
Key parameters
Causal relations
Possible therapy options
Non-invasive data collection
Re-parameterize model
Possible personalized
Traditional way: Projection of animal experiments on human often based on invasive animal experiments, which by construction does not take into account the physiological & anatomical differences between the two species and hence can be personalized only later by much complementary information correcting for the inaccuracy of the projection.
Our aim is to add a further column, in which in a first step a virtual computer model is generated for the animal model, which is validated by additional experiments. The computer model is then corrected and reparameterized to account for differences to human, mostly by non-invasive information (biomarkers, non-invasive imaging modalities) to generate a virtual twin of the patient on the computer, that is used to inform the clinician on key disease parameters, causal relations and possible therapy options for the patient.
Establish model
Parameterize model

6. Overall aim: Develop models to improve diagnosis and therapy of liver fibrosis progression
Tissue organisation
Multi-omics data
Whole organ & body
TGF-b signaling
Dooley, Berres,Trautwein
Hengstler, Klingmüller
Hengstler, Dooley, Bode
Berres, Trautwein
Dooley, Klingmüller
Trautwein, Berres
Kiessling, Küpfer, Hengstler, Schenk
Gene/Molecular scale Proteins-Signaling/Cellular scale (Patho)Morphology/Lobular scale Imaging-Pharmacodynamics/Organ-Body scale
The overall goal of Pillar II is to develop models for diagnosis and therapeutic target prediction for liver fibrosis progression.
To fulfill this goal, a systems medicine approach is applied, whereby different scales are examined in iterative cycles between experiments, statistical and image analysis, and mathematical models. One final aim is a multi-level model integrating the key mechanisms of fibrosis progression at different scales.
At the molecular scale, time resolved (in mouse) gene expression data undergo functional analysis to predict the most important contributors to disease progression.
At the cellular scale signaling pathway components are quantitatively delineated and translated into ODE models, whereby a special focus is on the TGFb pathway.
At the lobular/tissue scale, 2D and 3D analysis from tissue sections are integrated in an agent based model at the level of liver micro-architecture with the current aim to understand the mechanism(s) driving fibrosis pattern formation.
At the organ level, tracing of HSC and macrophage phenotypes are developed for improved non invasive diagnosis as in a patient, invasive data collection modalities need to be replaced largely by non-invasive modalities as biomarkers and non-invasive imaging.
At the body scale, blood flow and pharmacodynamics alterations are used to set up compartment models to predict liver disease progression and metabolic dysfunction, respectively.
Finally, the information over all scales needs to be correlated and integrated in one mechanistic picture.
All models are tested in the corresponding scale in clinical cohorts representing different disease stages, and model predictions are validated.
Using this novel –Systems Medicine- approach, we will be able to bridge the gap between experimental, clinical and modelling data in order to develop a liver disease stage diagnostic tool and therapeutic strategies.
Functional analysis
of gene expression
Agent-based model of
fibrotic scarring patterns
ODE TGF-b model in HSCs
Multiscale pharmacokinetics
Saez-Rodriguez, Theis
Timmer, Klingmüller
Drasdo
Hengstler, Küpfer, Preusser

7. Pillar II subprojects and scales

8. LiSyM: Teaming up to better understand liver disease