- Meeting abstract
- Open Access
Combining systems biology models of apoptosis provides superior predictions of the responsiveness of melanoma cells to cell death inducing drugs
© Curtayne et al. 2015
Published: 27 October 2015
Key to the clinical management of melanoma is the development of new diagnostic tools that predict individual patient prognosis and select from potential treatments those which may be effective. Identifying individual biomarkers in tumour cells to predict susceptibility to apoptotic cell death has thus far been largely unsuccessful, as apoptosis pathways show a high degree of signalling redundancy.
DR_MOMP  and ApoptoCell  are mathematical systems biology models of the mitochondrial outer membrane permeabilisation and execution stages of the apoptosis pathway, respectively, that take into account the complex nature of apoptosis regulation. Both models use a network of ordinary differential equations representing measured protein concentrations and reaction kinetics. In this study we combine these models and compare model predictions to experimental measurements of cell death in a range of melanoma cell-lines that were treated with different cytotoxic agents.
The combined approach is found to outperform either individual model in predicting strong and weak responses to treatment with cell death inducing drugs.
This work may provide a basis for the development of improved prediction tools for clinical treatment outcomes and treatment selection in melanoma.
- Lindner AU, Concannon CG, Boukes GJ, Cannon MD, Llambi F, Ryan D, et al: Systems Analysis of BCL2 Protein Family Interactions Establishes a Model to Predict Responses to Chemotherapy. Cancer Research. 2013, 73 (2): 519-28. 10.1158/0008-5472.CAN-12-2269.PubMedView ArticleGoogle Scholar
- Rehm M, Huber HJ, Dussmann H, Prehn JHM: Systems analysis of effector caspase activation and its control by X-linked inhibitor of apoptosis protein. EMBO J. 252006, 4338-49.Google Scholar
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