DocumentCode
59166
Title
A Glycolysis-Based In Silico Model for the Solid Tumor Growth
Author
Papadogiorgaki, Maria ; Kounelakis, Michalis G. ; Koliou, Panagiotis ; Zervakis, Michalis E.
Author_Institution
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
Volume
19
Issue
3
fYear
2015
fDate
May-15
Firstpage
1106
Lastpage
1117
Abstract
Cancer-tumor growth is a complex process depending on several biological factors, such as the chemical microenvironment of the tumor, the cellular metabolic profile, and its proliferation rate. Several mathematical models have been developed for identifying the interactions between tumor cells and tissue microenvironment, since they play an important role in tumor formation and progression. Toward this direction we propose a new continuum model of avascular glioma-tumor growth, which incorporates a new factor, namely, the glycolytic potential of cancer cells, to express the interactions of three different tumor-cell populations (proliferative, hypoxic, and necrotic) with their tissue microenvironment. The glycolytic potential engages three vital nutrients, i.e., oxygen, glucose, and lactate, which provide cells with the necessary energy for their survival and proliferation. Extensive simulations are performed for different evolution times and various proliferation rates, in order to investigate how the tumor growth is affected. According to medical experts, the experimental observations indicate that the model predicts quite satisfactorily the overall tumor growth as well as the expansion of each region separately. Following extensive evaluation, the proposed model may provide an essential tool for patient-specific tumor simulation and reliable prediction of glioma spatiotemporal expansion.
Keywords
biochemistry; cancer; cellular biophysics; organic compounds; oxygen; tumours; O2; avascular glioma-tumor growth; cancer cells; cancer tumor growth; cell survival; cellular metabolic profile; cellular proliferation rate; chemical microenvironment; continuum model; glioma spatiotemporal expansion; glucose; glycolysis-based in silico model; glycolytic potential; hypoxic cell populations; lactate; necrotic cell populations; oxygen; patient-specific tumor simulation; solid tumor growth; tissue microenvironment; tumor formation; tumor progression; Cancer; Equations; Mathematical model; Sociology; Statistics; Sugar; Tumors; Cancer cells; glioma; glucose; glycolysis; glycolytic potential; hypoxic; lactate; modeling; necrotic; oxygen; proliferative; regions; spatiotemporal evolution; tumor;
fLanguage
English
Journal_Title
Biomedical and Health Informatics, IEEE Journal of
Publisher
ieee
ISSN
2168-2194
Type
jour
DOI
10.1109/JBHI.2014.2356254
Filename
6894123
Link To Document