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A computational model for genetic and epigenetic signals in colon cancer

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Abstract

Cancer, a class of diseases, characterized by abnormal cell growth, has one of the highest overall death rates world-wide. Its development has been linked to aberrant genetic and epigenetic events, affecting the regulation of key genes that control cellular mechanisms. However, a major issue in cancer research is the lack of precise information on tumour pathways; therefore, the delineation of these and of the processes underlying disease proliferation is an important area of investigation. A computational approach to modelling malignant system events can help to improve understanding likely “triggers”, i.e. initiating abnormal micro-molecular signals that occur during cancer development. Here, we introduce a network-based model for genetic and epigenetic events observed at different stages of colon cancer, with a focus on the gene relationships and tumour pathways. Additionally, we describe a case study on tumour progression recorded for two gene networks on colon cancer, carcinoma in situ. Our results to date showed that tumour progression rate is higher for a small, closely-associated network of genes than for a larger, less-connected set; thus, disease development depends on assessment of network properties. The current work aims to provide improved insight on the way in which aberrant modifications characterize cancer initiation and progression. The framework dynamics are described in terms of interdependencies between three main layers: genetic and epigenetic events, gene relationships and cancer stage levels.

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Authors and Affiliations

  1. Centre for Scientific Computing & Complex Systems Modelling (SCI-SYM), School of Computing, Dublin City University, Dublin 9, Ireland

    Irina Afrodita Roznovăţ & Heather J. Ruskin

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  1. Irina Afrodita Roznovăţ
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  2. Heather J. Ruskin
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Correspondence to Irina Afrodita Roznovăţ.

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Roznovăţ, I.A., Ruskin, H.J. A computational model for genetic and epigenetic signals in colon cancer. Interdiscip Sci Comput Life Sci 5, 175–186 (2013). https://doi.org/10.1007/s12539-013-0172-y

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  • DOI: https://doi.org/10.1007/s12539-013-0172-y

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