2016 Conference on Computational Modelling with COPASI
Manchester Institute of Biotechnology, 12th – 13th May, 2016
1 - University of Costa Rica, Costa Rica
Keywords: cancer, miRNA, Transcription factor, COPASI, Systems Biology, Gene dosage compensation, Aneuploidy
Cancer robustness is enabled at the tumor cell population level by heterogeneity in therapy responses, which is driven by genomic instability, specially by aneuploidy: gains and losses of whole or partial chromosomes. It is unknown how cancer cells deal with so much aneuploidy whereas normal cells are very sensitive. A possible explanation is given by the hypothesis of gene dosage compensation (genetic balance), a mechanism that has been described for other organisms to compensate the negative effects of aneuploidy. It has been shown for aneuploid cancers that messenger RNA (mRNA) levels generally correlate well with an increased DNA copy number (gene dosage) but these changes are not reflected at the protein levels for several genes. Several lines of evidence suggest the existence of a gene dosage compensation mechanism that provides stability to cancer despite its genomic instability. However, this mechanism must be able to regulate the expression of a handful of critical genes simultaneously. We hypothesize the existence of a complex regulatory network mediated by microRNAs (miRNAs) to compensate for gene dosage changes in aneuploid cancer cells. miRNAs are small endogenous RNA molecules that bind mRNAs and repress gene expression. Currently, 1500 miRNAs have been described within the human genome regulating the expression of nearly 30% of all genes. Additionally, miRNAs can regulate hundreds of genes and their target genes can be regulated by several miRNAs. Furthermore, they form regulatory interactions with transcription factors including feedback and feedforward loops leading to non-linear, systems-level properties such as bistability, ultrasensitivity and oscillations. We suggest that the manipulation of specific nodes of this miRNA-based regulatory network could block gene dosage compensation, representing a specific target against cancer. Due to the complexity of this network, identification of optimal targets requires an advanced computational platform to: i) integrate multiple cancer data, ii) develop algorithms oriented to the identification of factors mediating genetic balance, and iii) identify optimal targets against cancer through the use of the mathematical modeling and simulation of miRNA-transcription factor networks. In order to identify possible genes under gene dosage compensation, we compared copy number, gene expression and proteomic data of the NCI60 panel. Our results suggest the existence of 19 candidate genes under dosage compensation across the NCI60 panel, partially related by common chromosomal locations or other functional interactions. To evaluate whether there are any reported or predicted connections between these candidate genes and miRNAs/TF, we automatically constructed a network of putative regulatory interactions based on the information available at the databases Mirtarbase, MiRBase, Pazar, TRED. Our results indicate that several putative regulatory loops link together all the candidate target genes and these regulatory motifs with potential systems-level properties are widely present within our network. Next, we translated this network into a large scale mathematical model of miRNA-transcription factor regulations of gene dosage compensation using COPASI. After model fitting and refinement we obtained a mathematical able to fit the data and ready to perform sensitivity/stability analysis to identify possible targets against gene dosage compensation in aneuploid cancer (unpublished ongoing project).