834 research outputs found
Dynamic Load Balancing Strategy for Parallel Tumor Growth Simulations
In this paper, we propose a parallel cellular automaton tumor growth model that includes load balancing of
cells distribution among computational threads with the introduction of adjusting parameters. The obtained
results show a fair reduction in execution time and improved speedup compared with the sequential tumor
growth simulation program currently referenced in tumoral biology. The dynamic data structures of the model
can be extended to address additional tumor growth characteristics such as angiogenesis and nutrient intake
dependencies
Modeling Three-dimensional Invasive Solid Tumor Growth in Heterogeneous Microenvironment under Chemotherapy
A systematic understanding of the evolution and growth dynamics of invasive
solid tumors in response to different chemotherapy strategies is crucial for
the development of individually optimized oncotherapy. Here, we develop a
hybrid three-dimensional (3D) computational model that integrates
pharmacokinetic model, continuum diffusion-reaction model and discrete cell
automaton model to investigate 3D invasive solid tumor growth in heterogeneous
microenvironment under chemotherapy. Specifically, we consider the effects of
heterogeneous environment on drug diffusion, tumor growth, invasion and the
drug-tumor interaction on individual cell level. We employ the hybrid model to
investigate the evolution and growth dynamics of avascular invasive solid
tumors under different chemotherapy strategies. Our simulations reproduce the
well-established observation that constant dosing is generally more effective
in suppressing primary tumor growth than periodic dosing, due to the resulting
continuous high drug concentration. In highly heterogeneous microenvironment,
the malignancy of the tumor is significantly enhanced, leading to inefficiency
of chemotherapies. The effects of geometrically-confined microenvironment and
non-uniform drug dosing are also investigated. Our computational model, when
supplemented with sufficient clinical data, could eventually lead to the
development of efficient in silico tools for prognosis and treatment strategy
optimization.Comment: 41 pages, 8 figure
Modeling tumor cell migration: from microscopic to macroscopic
It has been shown experimentally that contact interactions may influence the
migration of cancer cells. Previous works have modelized this thanks to
stochastic, discrete models (cellular automata) at the cell level. However, for
the study of the growth of real-size tumors with several millions of cells, it
is best to use a macroscopic model having the form of a partial differential
equation (PDE) for the density of cells. The difficulty is to predict the
effect, at the macroscopic scale, of contact interactions that take place at
the microscopic scale. To address this we use a multiscale approach: starting
from a very simple, yet experimentally validated, microscopic model of
migration with contact interactions, we derive a macroscopic model. We show
that a diffusion equation arises, as is often postulated in the field of glioma
modeling, but it is nonlinear because of the interactions. We give the explicit
dependence of diffusivity on the cell density and on a parameter governing
cell-cell interactions. We discuss in details the conditions of validity of the
approximations used in the derivation and we compare analytic results from our
PDE to numerical simulations and to some in vitro experiments. We notice that
the family of microscopic models we started from includes as special cases some
kinetically constrained models that were introduced for the study of the
physics of glasses, supercooled liquids and jamming systems.Comment: Final published version; 14 pages, 7 figure
A multiple scale model for tumor growth
We present a physiologically structured lattice model for vascular tumor growth which accounts for blood flow and structural adaptation of the vasculature, transport of oxygen, interaction between cancerous and normal tissue, cell division, apoptosis, vascular endothelial growth factor release, and the coupling between these processes. Simulations of the model are used to investigate the effects of nutrient heterogeneity, growth and invasion of cancerous tissue, and emergent growth laws
Programmable models of growth and mutation of cancer-cell populations
In this paper we propose a systematic approach to construct mathematical
models describing populations of cancer-cells at different stages of disease
development. The methodology we propose is based on stochastic Concurrent
Constraint Programming, a flexible stochastic modelling language. The
methodology is tested on (and partially motivated by) the study of prostate
cancer. In particular, we prove how our method is suitable to systematically
reconstruct different mathematical models of prostate cancer growth - together
with interactions with different kinds of hormone therapy - at different levels
of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104
Tracking uncertainty in a spatially explicit susceptible-infected epidemic model
In this paper we conceive an interval-valued continuous cellular automaton for describing the spatio-temporal dynamics of an epidemic, in which the magnitude of the initial outbreak and/or the epidemic properties are only imprecisely known. In contrast to well-established approaches that rely on probability distributions for keeping track of the uncertainty in spatio-temporal models, we resort to an interval representation of uncertainty. Such an approach lowers the amount of computing power that is needed to run model simulations, and reduces the need for data that are indispensable for constructing the probability distributions upon which other paradigms are based
Towards whole-organ modelling of tumour growth
Multiscale approaches to modelling biological phenomena are growing rapidly. We present here some recent results on the formulation of a theoretical framework which can be developed into a fully integrative model for cancer growth. The model takes account of vascular adaptation and cell-cycle dynamics. We explore the effects of spatial inhomogeneity induced by the blood flow through the vascular network and of the possible effects of p27 on the cell cycle. We show how the model may be used to investigate the efficiency of drug-delivery protocols
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