SP 2

Extending the SYSACT model by Boolean model based network analysis incorporating TRAIL and MEK signaling networks

Despite remarkable scientific and clinical efforts in melanoma research, the incidence of malignant melanoma-related skin cancer strongly increased over the last few decades.  While metastatic melanoma is often characterized by mutations of the kinase BRAF, overall the large number of different mutations represent the biggest challenge in our understanding of the disease. These mutations lead to a very aggressive form of cancer with a usually poor prognosis. The response to chemotherapies only last for a few months until usually drug resistance occurs leading to a relapse of the cancer.
For an in-depth analysis of melanoma resistance to chemotherapies, we apply a systems biology approach which integrates experimental data into a mathematical model. The previously published SYSACT network model of apoptotic signaling (Passante et al., 2013, Cell Death Differ), accurately predicted and experimentally validated the cellular responsiveness to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and dacarbazine (DTIC). This model will be used as a reference and will be extended based on newly acquired experimental data. In order to be capable to describe large-scale signal transduction networks, we employ a probabilistic Boolean network modeling approach (Trairatphisan et al., 2014, PLoS One). This method allows the quantitative large scale modeling of molecular interactions and regulations among signaling molecules. The probability of active signaling molecules can be correlated to normalized experimental data. This approach allows to identify the influence of targeted molecules via different signaling pathways onto the treatments success/apoptosis rate. Furthermore, to identify critical network points for each cell line, sensitivity analysis methods will be performed. These sensitive network points of the MEK-pathway as well as from the SYSACT model will be used to extend the mathematical model.
The goal of this study is to identify, with the mathematical model, the critical mechanisms leading to drug resistance in the different melanoma cell lines. The most sensitive points in the network will be identified, allowing formulating new hypotheses which can then be tested experimentally. In close collaboration with experimental research groups (Experimental Dermatology, Dresden and Division of Dermatologic Oncology, Tübingen), newly identified drug targets will be tested in vitro and may lead to new clinical treatments.

Systems Biology approach from bench to bedside. A. In vitro experiments are performed by the experimenter in different cell lines and under different treatment conditions. B. The quantified experimental results produced by the experimenter will be used by the systems biologist for computational analysis. Here, with the help of Probabilistic Boolean Networks, the mathematical model will be calibrated and predictions on drug sensitive cellular processes will be performed. These model predictions will be validated by the experimentalists. C. The newly identified biomarkers and developed drug targets will be explored for improved treatment of melanoma patients in the clinics.

Melanoma, SYSACT, Systems Biology, Probabilistic Boolean Network

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