From Gene to Cell State: A Multi-Level Computational Methodology for Predicting Cancer Behaviors

Date

2017

Authors

Trieu Do, Cyanea Van

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Abstract

Complexity of cancer pose many challenges for diagnosis, prognosis, and more importantly, treatment planning. One of the fundamental issues is how cancer cells behave and their responses to various therapies. To investigate these issues, it involves different disciplines such as biology, biophysics, biochemistry and biomechanics. In this study, we focus on developing methods to predict cell states underlining characteristics that affects cell behaviors. The goal is to leverage big data generated in last few decades from DNA and RNA sequencing that includes information of gene mutations, copy numbers and indices of recurrence. The basis of this study follows the Central dogma of Biology: DNA to RNA to Protein. Using methods of fuzzy logic to determine the protein loss and gain functions, it provides the necessary information to construct protein function specific signaling pathways using ordinary differential equation systems to predict the rate of change of components in the signaling pathway. The cell states can be determined by the protein concentrations and their rate of change. The real patient data are used to compare with the computational results, which show the proposed method is applicable to predict cell stats base on genetic information and ultimately determine the cell states.

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Keywords

CANCER BEHAVIORS, COMPUTATIONAL METHODOLY, FUZZY LOGIC, MIGRATION, MULTI-LEVEL, PROLIFERATION

Citation

Department

Mechanical Engineering