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- PublicationA dynamic model of the MYCN regulated DNA damage response in NeuroblastomaNeuroblastoma is the most common the most common cancer in infancy with an extremely heterogeneous phenotype that is mainly driven by the MYCN oncogene. The MYCN transcription factor and its amplification is commonly associated with poor prognosis in patients, although it has also been shown that elevated MYCN levels correlates with apoptosis sensitization in cells. HMGA1 is one of MYCN target genes and is involved in triggering apoptosis through a DNA Damage Response (DDR) by inducing ataxia-telangiectasia-mutated (ATM) gene expression. But HMGA1 is also involved in preventing apoptosis by directly binding HIPK2 and decreasing its presence in the nucleus, therefore decreasing phosphorylation of p53 at serine 46 which is required for the activation of p53 apoptotic targets. In this article, we propose a model in which MYCN protein regulates the HMGA1-ATM-p53 and HMGA1-HIPK2-p53 subsystems. Because the molecular details concerning the HMGA1-HMGA1 interaction are uncertain several possibilities were explored in simulations. Our model points towards an important role of MYCN-dependent regulation of HMGA1 expression levels and the subsequent HIPK2 nuclear/cytoplasmic re-localization and led to experimentally testable predictions that can discern between alternative model structures.
- PublicationOn the personalised modelling of cancer signallingDynamic modelling has long been used to understand fundamental principles of cell signalling and its dysregulation in cancer. More recently these models have also been used to understand the individual risks of cancer patients, and predict their survival probabilities. However, the current methodologies for integrating tumour data and generating patient-specific simulations suffer from the lack of general applicability; they only work for cell signalling models in which only posttranslational protein modifications are considered, so that the total protein concentrations are conserved. Here, we present novel, generally applicable method. The method is based on a simple theoretical framework for modelling gene-regulation, and the indirect estimation of patient-specific parameters from tumour data. Because our method does not require time-invariance of the total-protein concentrations, it can be applied to models of any nature, including the many cancer signalling models involving gene-regulation.
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