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: 2.3). 05/2005; 233(4):469-81. DOI: 10.1016/j.jtbi.2004.10.019
Source: PubMed

ABSTRACT 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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Available from: Chaitanya Athale, Aug 25, 2015
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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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    ABSTRACT: Glioblastomas are highly malignant brain tumours. Mathematical models and their analysis provide a tool to support the understanding of the development of these tumours as well as the design of more effective treatment strategies. We have previously developed a multiscale model of glioblastoma progression that covers processes on the cellular and molecular scale. Here, we present a novel nutrient-dependent multiscale sensitivity analysis of this model that helps to identify those reaction parameters of the molecular interaction network that influence the tumour progression on the cellular scale the most. In particular, those parameters are identified that essentially determine tumour expansion and could be therefore used as potential therapy targets. As indicators for the success of a potential therapy target, a deceleration of the tumour expansion and a reduction of the tumour volume are employed. From the results, it can be concluded that no single parameter variation results in a less aggressive tumour. However, it can be shown that a few combined perturbations of two systematically selected parameters cause a slow-down of the tumour expansion velocity accompanied with a decrease of the tumour volume. Those parameters are primarily linked to the reactions that involve the microRNA-451 and the thereof regulated protein MO25.
    Computational and Mathematical Methods in Medicine 05/2014; 2014:437094. DOI:10.1155/2014/437094 · 1.02 Impact Factor
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    • "The EGFR has been implicated in several cancers including lung cancer, breast cancer, and glioblastoma, yet the EGFR activity itself is not capable of predicting the phenotype of cancer cells. As shown in Fig. 2 (top box), a microscopic, EGFR gene-protein interaction network-based model has been developed [25]. Given initial concentrations of important molecules in tumor microenvironment such as glucose, oxygen, and transforming growth factor α (TGFα), the model predicts whether the cell proceeds to proliferation or migration. "
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    IEEE Journal of Biomedical and Health Informatics 01/2014; PP(99). DOI:10.1109/JBHI.2013.2297167 · 1.98 Impact Factor
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    • "In particular, its natural support of compositionality and its abstraction mechanisms can provide the required flexibility that writing, composing and comparing multi-scale models require. In these paper we show the following advantages of using a process algebra framework in place of traditional modelling approaches such as in [27] [28]: • (writing) different biological scales are represented by the same mathematical objects under a unified framework. A modeller can begin writing a multi-scale model from any scale and continue up-scale or down-scale without changing the mathematical approach; • (composing) composition of models is facilitated by operators for composition within scale (e.g. two tissues next to each other) or between scales (e.g. cells that constitute a tissue); • (comparing) most importantly, the unified framework allows for automated reasoning between entities in a way that is not accessible by traditional modelling approaches. "
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    ABSTRACT: We introduce a novel process algebra for modelling biological systems at multiple scales, called process algebra with hooks (PAHPAH). Processes represent biological entities, such as molecules, cells and tissues, while two algebraic operators, both symmetric, define composition of processes within and between scales. Composed actions allow for biological events to interact within and between scales at the same time. The algebra has a stochastic semantics based on functional rates of reactions. Two bisimulations are defined on PAH processes. The first bisimulation is used to aid model development by checking that two biological scales can interact correctly. The second bisimulation is a congruence that relates models, or part of models, that can perform the same timed events at a specified scale. Finally, we provide a PAH model of pattern formation in a tissue and illustrate reasoning about its behaviour using the PAH framework.
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