LiSyM- Pillar II Chronic liver disease progression Introduction Steven Dooley, Christian Trautwein Mannheim, 4-5.04.2018

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`)

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

Dynamics of a progressive chronic liver disease: the organ view

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

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

Pillar II subprojects and scales

LiSyM: Teaming up to better understand liver disease