A model for cancer tissue heterogeneity. Academic Article

abstract

• An important problem in the study of cancer is the understanding of the heterogeneous nature of the cell population. The clonal evolution of the tumor cells results in the tumors being composed of multiple subpopulations. Each subpopulation reacts differently to any given therapy. This calls for the development of novel (regulatory network) models, which can accommodate heterogeneity in cancerous tissues. In this paper, we present a new approach to model heterogeneity in cancer. We model heterogeneity as an ensemble of deterministic Boolean networks based on prior pathway knowledge. We develop the model considering the use of qPCR data. By observing gene expressions when the tissue is subjected to various stimuli, the compositional breakup of the tissue under study can be determined. We demonstrate the viability of this approach by using our model on synthetic data, and real-world data collected from fibroblasts.

authors

• Datta, Aniruddha

published proceedings

• IEEE Trans Biomed Eng

author list (cited authors)

• Mohanty, A. K., Datta, A., & Venkatraj, V.

citation count

• 13

complete list of authors

• Mohanty, Anwoy Kumar||Datta, Aniruddha||Venkatraj, Vijayanagaram

publication date

• March 2014

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Biomedical Engineering  Journal

keywords

• Algorithms
• Cell Line, Tumor
• Cells, Cultured
• Computational Biology
• Computer Simulation
• Fibroblasts
• Gene Expression Profiling
• Gene Regulatory Networks
• Humans
• MAP Kinase Signaling System
• Models, Biological
• Models, Statistical
• Neoplasms

Digital Object Identifier (DOI)

• 10.1109/TBME.2013.2294469

start page

• 966

end page

• 974

volume

• 61

issue

• 3

URL

• http://dx.doi.org/10.1109/tbme.2013.2294469