Simulating the impact of a molecular 'decision-process' on cellular phenotype and multicellular patterns in brain tumors

Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.
Journal of Theoretical Biology (Impact Factor: 2.12). 05/2005; 233(4):469-81. DOI: 10.1016/j.jtbi.2004.10.019
Source: PubMed


Experimental evidence indicates that human brain cancer cells proliferate or migrate, yet do not display both phenotypes at the same time. Here, we present a novel computational model simulating this cellular decision-process leading up to either phenotype based on a molecular interaction network of genes and proteins. The model's regulatory network consists of the epidermal growth factor receptor (EGFR), its ligand transforming growth factor-alpha (TGF alpha), the downstream enzyme phospholipaseC-gamma (PLC gamma) and a mitosis-associated response pathway. This network is activated by autocrine TGF alpha secretion, and the EGFR-dependent downstream signaling this step triggers, as well as modulated by an extrinsic nutritive glucose gradient. Employing a framework of mass action kinetics within a multiscale agent-based environment, we analyse both the emergent multicellular behavior of tumor growth and the single-cell molecular profiles that change over time and space. Our results show that one can indeed simulate the dichotomy between cell migration and proliferation based solely on an EGFR decision network. It turns out that these behavioral decisions on the single cell level impact the spatial dynamics of the entire cancerous system. Furthermore, the simulation results yield intriguing experimentally testable hypotheses also on the sub-cellular level such as spatial cytosolic polarization of PLC gamma towards an extrinsic chemotactic gradient. Implications of these results for future works, both on the modeling and experimental side are discussed.

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    • "A variety of modeling strategies are available to investigate one or more aspects of cancer. Discrete cell-based models (e.g., cellular automata [2] [9] [30] [44] [46] [62] and agent-based models [7] [43] [50]), where individual cells are tracked and updated according to a specific set of biophysical rules, are particularly useful for studying carcinogenesis, natural selection, genetic instability, and interactions of individual cells with each other and the microenvironment . In larger-scale systems, continuum methods provide a good modeling alternative [14] [16] [17] [18] [19] [27] [37] [39] [48]. "
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    • "In the case of cancer modelling, a few multiscale approaches exist [31]. In [32] [33], a model for glioblastoma growth was introduced that combines an agent based model with an EGFR (epidermal growth factor receptor) signalling network and focuses on the determination of the cell phenotypes " migrating " and " proliferating. " Later, also a model for the progression of lung cancer was developed [34] in that essentially only the molecular interaction network was interchanged. "
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