dorsal/arxiv
View SchemaSimulating Brain Tumor Heterogeneity with a Multiscale Agent-Based Model: Linking Molecular Signatures, Phenotypes and Expansion Rate
| Authors | Le Zhang, Costas G. Strouthos, Zhihui Wang, Thomas S. Deisboeck |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0612037 |
| URL | https://arxiv.org/abs/q-bio/0612037 |
| DOI | 10.1016/j.mcm.2008.05.011 |
| Journal | Mathematical and Computer Modelling 49 (1-2), pp. 307-319, 2009 |
Abstract
We have extended our previously developed 3D multi-scale agent-based brain tumor model to simulate cancer heterogeneity and to analyze its impact across the scales of interest. While our algorithm continues to employ an epidermal growth factor receptor (EGFR) gene-protein interaction network to determine the cells' phenotype, it now adds an explicit treatment of tumor cell adhesion related to the model's biochemical microenvironment. We simulate a simplified tumor progression pathway that leads to the emergence of five distinct glioma cell clones with different EGFR density and cell 'search precisions'. The in silico results show that microscopic tumor heterogeneity can impact the tumor system's multicellular growth patterns. Our findings further confirm that EGFR density results in the more aggressive clonal populations switching earlier from proliferation-dominated to a more migratory phenotype. Moreover, analyzing the dynamic molecular profile that triggers the phenotypic switch between proliferation and migration, our in silico oncogenomics data display spatial and temporal diversity in documenting the regional impact of tumorigenesis, and thus support the added value of multi-site and repeated assessments in vitro and in vivo. Potential implications from this in silico work for experimental and computational studies are discussed.
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"abstract": "We have extended our previously developed 3D multi-scale agent-based brain\ntumor model to simulate cancer heterogeneity and to analyze its impact across\nthe scales of interest. While our algorithm continues to employ an epidermal\ngrowth factor receptor (EGFR) gene-protein interaction network to determine the\ncells\u0027 phenotype, it now adds an explicit treatment of tumor cell adhesion\nrelated to the model\u0027s biochemical microenvironment. We simulate a simplified\ntumor progression pathway that leads to the emergence of five distinct glioma\ncell clones with different EGFR density and cell \u0027search precisions\u0027. The in\nsilico results show that microscopic tumor heterogeneity can impact the tumor\nsystem\u0027s multicellular growth patterns. Our findings further confirm that EGFR\ndensity results in the more aggressive clonal populations switching earlier\nfrom proliferation-dominated to a more migratory phenotype. Moreover, analyzing\nthe dynamic molecular profile that triggers the phenotypic switch between\nproliferation and migration, our in silico oncogenomics data display spatial\nand temporal diversity in documenting the regional impact of tumorigenesis, and\nthus support the added value of multi-site and repeated assessments in vitro\nand in vivo. Potential implications from this in silico work for experimental\nand computational studies are discussed.",
"arxiv_id": "q-bio/0612037",
"authors": [
"Le Zhang",
"Costas G. Strouthos",
"Zhihui Wang",
"Thomas S. Deisboeck"
],
"categories": [
"q-bio.TO",
"q-bio.MN"
],
"doi": "10.1016/j.mcm.2008.05.011",
"journal_ref": "Mathematical and Computer Modelling 49 (1-2), pp. 307-319, 2009",
"title": "Simulating Brain Tumor Heterogeneity with a Multiscale Agent-Based Model: Linking Molecular Signatures, Phenotypes and Expansion Rate",
"url": "https://arxiv.org/abs/q-bio/0612037"
},
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