Computational Modeling to Study Disease Development: Applications to Breast Cancer and an in vitro Model of Macular Degeneration (original) (raw)
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Multiscale Agent-based Model of Tumor Angiogenesis
Proceedings of the International Conference on Computational Science, 2013
Computational models of cancer complement the biological study of tumor growth. However, existing modeling approaches can be both inefficient and inaccurate due to the difficulties of representing the complex interactions between cells and tissues. We present a three-dimensional multiscale agent-based model of tumor growth with angiogenesis. The model is designed to easily adapt to various cancer types, although we focus on breast cancer. It includes cellular (genetic control), tissue (cells, blood vessels, angiogenesis), and molecular (VEGF, diffusion) levels of representation. Unlike in most cancer models, both normally functioning tissue cells and tumor cells are included in the model. Tumors grow following the expected spheroid cluster pattern, with growth limited by available oxygen. Angiogenesis, the process by which tumors may encourage new vessel growth for nutrient diffusion, is modeled with a new discrete approach that we propose will decrease computational cost. Our results show that despite proposing these new abstractions, we see similar results to previously accepted angiogenesis models. This may indicate that a more discrete approach should be considered by modelers in the future.
AN AGENT-BASED HYBRID MODEL FOR AVASCULAR TUMOR GROWTH
Tumor development is a complex and multi-faceted process that cannot be captured in a single formula, yet the ability to predict a maturing tumor's magnitude and direction of growth would provide significant clinical benefits. In-vitro trials provide only limited predictive data since it is nearly impossible to chemically reproduce the exact environmental conditions surrounding a tumor. Moreover, each trial is necessarily unique to a specific tumor and cannot be quickly modified to satisfy the requirements of another. Mathematical models provide a virtual solution to this problem by implementing the core processes of tumor development in software. We present a model for tumor development from the single-cell stage to early microinvasion. An overlying nutrient field determines a cell's status as living, quiescent, or nonviable. Interactions between tumor cells are simulated using a competing exponential function and nutrient influx is modeled using the diffusion equation. The model may be applied to a variety of emerging tumors by carefully defining the set of constants that determines the tumor's development pathway.
State of the art in computational modelling of cancer
Mathematical medicine and biology : a journal of the IMA, 2012
Cancer is a complex, multiscale process in which genetic mutations occurring at a subcellular level manifest themselves as functional changes at the cellular and tissue scale. The multiscale nature of cancer requires mathematical modeling approaches that can handle multiple intracellular and extracellular factors acting on different time and space scales. Hybrid models provide a way to integrate both discrete and continuous variables that are used to represent individual cells and concentration or density fields, respectively. Each discrete cell can also be equipped with submodels that drive cell behavior in response to microenvironmental cues. Moreover, the individual cells can interact with one another to form and act as an integrated tissue. Hybrid models form part of a larger class of individualbased models that can naturally connect with tumor cell biology and allow for the integration of multiple interacting variables both intrinsically and extrinsically and are therefore perfectly suited to a systems biology approach to tumor growth.
2017
Background The configuration of necrotic areas within the retinal pigmented epithelium is an important element in the progression of age-related macular degeneration (AMD). In the exudative (wet) and non-exudative (dry) forms of the disease, retinal pigment epithelial (RPE) cells respond to adjacent atrophied regions by secreting vascular endothelial growth factor (VEGF) that in turn recruits new blood vessels which lead to a further reduction in retinal function and vision. In vitro models exist for studying VEGF expression in wet AMD (Vargis et al., Biomaterials 35(13):3999–4004, 2014), but are limited in the patterns of necrotic and intact RPE epithelium they can produce and in their ability to finely resolve VEGF expression dynamics. Results In this work, an in silico hybrid agent-based model was developed and validated using the results of this cell culture model of VEGF expression in AMD. The computational model was used to extend the cell culture investigation to explore the dynamics of VEGF expression in different sized patches of RPE cells and the role of negative feedback in VEGF expression. Results of the simulation and the cell culture studies were in excellent qualitative agreement, and close quantitative agreement. Conclusions The model indicated that the configuration of necrotic and RPE cell-containing regions have a major impact on VEGF expression dynamics and made precise predictions of VEGF expression dynamics by groups of RPE cells of various sizes and configurations. Coupled with biological studies, this model may give insights into key molecular mechanisms of AMD progression and open routes to more effective treatments.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2004
There have been many efforts to explain and simulate tumor growth with mathematical and computational models. However, none have systematically examined the behaviors of tumor spheroids during growth. The interactions among tumor cells during growth are also not well understood. We have implemented an agent-based computational approach to study the macro- and micro- behaviors of avascular tumor spheroids during growth. Our simulations of tumor spheroid growth begin with a single tumor cell in optimal environmental conditions. We observe an initial phase of rapid growth, during which the shape of the collective approximates a spheroid. Subsequently a characteristic layered structure develops, consisting of an outermost proliferating cell layer, an intermediate quiescent cell layer, and a central necrotic core. These behaviors of our in silico spheroids map well to experimental in vitro observations.
Engineering Simulations for Cancer Systems Biology
Current Drug Targets, 2012
Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions.