Research Aims

Introduction

In healthy organisms cellular decisions are tightly controlled. Perturbation of the extracellular communication and intracellular information processing caused by mutations and amplifications promote the onset of diseases such as cancer. To understand key dynamic properties and predict strategies for intervention, data-based mathematical models are essential. An important prerequisite for these models are high-quality quantitative data. To ensure reproducibility of results, standard operating procedures (SOP) have been developed for the preparation and cultivation of primary murine hepatocytes and erythroid progenitors as well as for the handling of cancer cell lines. We study signaling networks by employing quantitative immunoblotting, reverse phase protein arrays, multiplex bead assays and mass spectrometry. The quantitative analysis of cell-cell communication is brought forward by multiplex bead assays. The induction of target genes and microRNAs is quantified using qRT-PCR and Next Generation Sequencing.
Under development is the quantitative analysis of metabolic alterations. At the single cell level multi-color live cell imaging and flow cytometry is currently in use. Key for these fluorescence-based experiments is the authentic expression of fluorescently labeled proteins enabled by the bacterial artificial chromosome (BAC) technology and the generation of knock-in mice. Physiological responses examined comprise cell cycle progression, survival decisions and cellular migration. These data feed into multiple modeling approaches that are selected based on the question addressed and the type of data that can be generated. To explore large signaling networks logical models are employed, whereas for detailed insights into regulatory mechanisms ordinary differential equation (ODE) models are most informative. A major challenge is the integration of different types of mathematical models and models across the scales. This integration is required to finally quantitatively link decisions at the cellular level with their impact at the tissue or organ level.

Unraveling principal mechanisms of erythropoietin (Epo) mediated cellular decisions in the hematopoietic system

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The cells of the hematopoietic system are continuously renewed in a tightly controlled growth and differentiation process. Deregulation of hematopoiesis results in leukemias or anemia. Key regulator of the erythroid lineage is the hormone erythropoietin (Epo) that binds to the erythropoietin receptor (EpoR), a hematopoietic cytokine receptor specifically present on erythroid progenitor cells. Tight coordination of signal strength and duration in response to receptor activation controls the regeneration of erythrocytes from erythroid progenitor cells. By data-based mathematical modeling we demonstrated that signal integration at the receptor level is linear over a broad range of ligand concentrations due to extremely rapid receptor turnover at the cell surface.
Furthermore, dual feedback loops control the JAK2-STAT5 signaling cascade that is crucial in regulating survival signaling, whereas MAP kinase and PI3 kinase activation contribute to cell proliferation.
Taking advantage of the system in which Epo as a single factor coordinates proliferation, survival and differentiation of erythroid progenitor cells, our aim is to develop strategies for the establishment of an integrative mathematical model comprising signal transduction, gene regulation and physiological responses and to unravel general design principles controlling cellular decisions. This knowledge will enable the design of strategies for the targeted expansion of erythroid progenitor cells to counteract anemia.

Bridging from the cellular to the whole organ level during liver regeneration

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A unique feature of the liver is its nearly unlimited potential for regeneration. In a tightly regulated process non-parenchymal cells such as Kupffer cells and hepatic sinusoidal endothelial cells are activated to secrete interleukin (IL)-6 and tumor necrosis factor (TNF)-alpha that prime the quiescent and differentiated hepatocytes in the liver for the proliferative response. Subsequently, hepatic stellate cells and hepatic sinusoidal endothelial cells secrete hepatocyte growth factor (HGF), the major mitogen for hepatocytes. A key factor involved in termination of liver regeneration and ensuring liver mass conservation is transforming growth factor (TGF)-beta, which suppresses cell cycle progression.
In the frame of the BMBF-funded network HepatoSys we have established dynamic pathway models for IL-6, HGF and TGF-beta signaling in primary mouse hepatocytes. Within the successor network Virtual Liver we are currently establishing integrative models addressing pathway crosstalk, regulation through microRNAs and cell-cell communication. A main goal of the Virtual Liver is to link data-based mathematical models across scales to establish a reliable framework to quantitatively predict physiological responses of the liver.

Insights into altered regulation in cancer and prediction of strategies for efficient intervention in diseases

The identification of general control mechanisms in the context of healthy cells enables us to pinpoint critical alterations in the diseased state. Major topics in the division are:

Lung Cancer and Hepatocellular Carcinoma

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Mutations and gene copy alterations cause changes in the function and the amount of signaling components, respectively, and thereby perturb information processing in cancer cells. Reappearance of the erythropoietin receptor on lung cancer cells has been observed and is discussed as a potential explanation for tumor promoting effects by Epo treatment of cancer related anemia. In the BMBF-funded MedSys network LungSys we are employing our knowledge regarding dynamic properties of EpoR signaling in the hematpoietic system to understand mechanisms that pose a risk in treating lung cancer related anemia with Epo.
Lung cancer is one of the deadliest cancers worldwide due to early metastatic spread independent of tumor size. In the CancerSys network LungSysII and the DZL we will address the dynamics of signaling pathways at the cell population and single cell level in close collaboration with clinicians and partners from companies. This will be performed in lung cancer cell lines representative for key alterations observed in lung cancer, which are cultivated either in monolayer, as spheroid or xenograft. By integrating dynamic pathway models, spatio-temporal models and multi-scale models, our aim is to link the extent of pathway activation to migratory and proliferative decisions and thereby unravel mechanisms promoting early spread in lung cancer.
Furthermore, in the context of the EU-funded network CancerSys we are employing our knowledge of key mechanisms controlling liver regeneration to address properties that promote the onset of hepatocellular carcinoma formation. The aim is to use our data-based integrative mathematical models to predict strategies for more effective intervention.

Drug Induced Liver Injury

One of the most serious complications in the development of therapeutic compounds is drug induced liver injury that is uncovered late in the drug development process. In particular, inflammatory responses could amplify the toxicity of drugs and may contribute to an even greater variability from patient to patient. In the frame of the EU-funded network MIP-DILI within the IMI initiative we are collaborating with multiple major pharmaceutical companies to make use of our dynamic pathway models and to examine the impact of inflammatory cytokines on hepatotoxicity of test compounds. The aim is to establish mathematical models that will guide the development of compounds and facilitate early decisions.

Viral infection

The Hepatitis C Virus is the major cause of chronic liver disease and hepatocellular carcinoma worldwide. By modulation of cellular signaling pathways the virus evades the cellular antiviral response, leading to a persistent infection in more than 80% of cases. In the frame of the BMBF-funded FORSYS network ViroQuant we could show by dynamic pathway modeling that interferon (IFN)-alpha signaling is controlled by positive and negative feedback loops and the selective targeting of these loops could provide means to foster anti-viral responses. The aim is to gain deeper insights how the virus interferes with the cellular defense mechanisms and how anti-viral therapies could be rendered more effective.

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