A new method for shrinking tumor based on microenvironmental factors: Introducing a stochastic agent-based model of avascular tumor growth

2018 ◽  
Vol 508 ◽  
pp. 771-787 ◽  
Author(s):  
S.H. Sabzpoushan ◽  
Fateme Pourhasanzade
Keyword(s):  
Tumor Growth ◽  
New Method ◽  
Agent Based Model ◽  
Agent Based ◽  
Microenvironmental Factors ◽  
Avascular Tumor

scholarly journals An Agent-Based Model of Avascular Tumor Growth

2015 ◽  
Author(s):  
Ana Victoria Ponce Bobadilla ◽  
◽  
René Doursat ◽  
François Amblard
Keyword(s):  
Tumor Growth ◽  
Agent Based Model ◽  
Agent Based ◽  
Avascular Tumor

scholarly journals An agent-based model of avascular tumor growth: Immune response tendency to prevent cancer development

SIMULATION ◽  
2017 ◽  
Vol 93 (8) ◽  
pp. 641-657 ◽  
Author(s):  
Fateme Pourhasanzade ◽  
S.H Sabzpoushan ◽  
Ali Mohammad Alizadeh ◽  
Ebrahim Esmati
Keyword(s):  
Immune System ◽  
Tumor Growth ◽  
Tumor Cells ◽  
Immune Cells ◽  
Computational Models ◽  
Growth Fraction ◽  
Agent Based Model ◽  
Agent Based ◽  
Clinical Experiments ◽  
Avascular Tumor

Mathematical and computational models are of great help to study and predict phenomena associated with cancer growth and development. These models may lead to introduce new therapies or improve current treatments by discovering facts that may not be easily discovered in clinical experiments. Here, a new two-dimensional (2D) stochastic agent-based model is presented for the spatiotemporal study of avascular tumor growth based on the effect of the immune system. The simple decision-making rules of updating the states of each agent depend not only on its intrinsic properties but also on its environment. Tumor cells can interact with both normal and immune cells in their Moore neighborhood. The effect of hypoxia has been checked off by considering non-mutant proliferative tumor cells beside mutant ones. The recruitment of immune cells after facing a mass of tumor is also considered. Results of the simulations are presented before and after the appearance of immune cells in the studied tissue. The growth fraction and necrotic fraction are used as output parameters along with a 2D graphical growth presentation. Finally, the effect of input parameters on the output parameters generated by the model is discussed. The model is then validated by an in vivo study published in medical articles. The results show a multi-spherical tumor growth before the immune system strongly involved in competition with tumor cells. Besides, considering the immune system in the model shows more compatibility with biological facts. The effect of the microenvironment on the proliferation of cancer and immune cells is also studied.

A multiscale agent-based framework integrated with a constraint-based metabolic network model of cancer for simulating avascular tumor growth

2017 ◽  
Vol 13 (9) ◽  
pp. 1888-1897 ◽  
Author(s):  
Mehrdad Ghadiri ◽  
Mahshid Heidari ◽  
Sayed-Amir Marashi ◽  
Seyed Hasan Mousavi
Keyword(s):  
Tumor Growth ◽  
Metabolic Network ◽  
Network Model ◽  
Agent Based ◽  
Metabolic Network Model ◽  
Avascular Tumor

The integration of an agent-based framework with a constraint-based metabolic network model of cancer for simulating avascular tumor growth.

scholarly journals Simulation of avascular tumor growth by agent-based game model involving phenotype-phenotype interactions

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Yong Chen ◽  
Hengtong Wang ◽  
Jiangang Zhang ◽  
Ke Chen ◽  
Yumin Li
Keyword(s):  
Tumor Growth ◽  
Game Model ◽  
Agent Based ◽  
Avascular Tumor

scholarly journals On Cucker–Smale dynamical systems with degenerate communication

2020 ◽  
pp. 1-23
Author(s):  
Helge Dietert ◽  
Roman Shvydkoy
Keyword(s):  
Dynamical Systems ◽  
Long Range ◽  
Collective Behavior ◽  
Communication Protocol ◽  
New Method ◽  
Energy Estimates ◽  
Agent Based Model ◽  
Agent Based ◽  
Close Range ◽  
Alignment Model

This paper introduces a new method for establishing alignment in systems of collective behavior with degenerate communication protocol. The communication protocol consists of a kernel defining interaction between pairs of agents. Degeneracy presumes that the kernel vanishes in a region, which creates a zone of indifference. A motivating example is the case of a local kernel. Lapses in communication create a lack of coercivity in the energy estimates. Our approach is the construction of a corrector functional that compensates for this lack of coercivity. We obtain a series of new results: unconditional alignment for systems on [Formula: see text] with degeneracy at close range and fat tail in the long range, and for systems on the circle with purely local kernels. The results are proved in the context of both the agent based model and its hydrodynamic counterpart (Euler alignment model). The method covers bounded and singular communication kernels.

scholarly journals Hybrid approach for parameter estimation in agent-based models

2017 ◽  
Author(s):  
Jill A. Gallaher ◽  
Andrea Hawkins-Daarud ◽  
Susan C. Massey ◽  
Kristin R. Swanson ◽  
Alexander R.A. Anderson
Keyword(s):  
Cancer Research ◽  
Parameter Estimation ◽  
Hybrid Approach ◽  
New Method ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Agent Based

AbstractAgent-based models are valuable in cancer research to show how different behaviors emerge from individual interactions between cells and their environment. However, calibrating such models can be difficult, especially if the parameters that govern the underlying interactions are hard to measure experimentally. Herein, we detail a new method to converge on parameter sets that fit an agent-based model to multiscale data using a model of glioblastoma as an example.

scholarly journals Causal Analysis of an Agent-Based Model of Human Behaviour

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Marcel Kvassay ◽  
Peter Krammer ◽  
Ladislav Hluchý ◽  
Bernhard Schneider
Keyword(s):  
Time Evolution ◽  
Predictive Power ◽  
Causal Analysis ◽  
Human Behaviour ◽  
New Method ◽  
Relative Importance ◽  
Agent Based Model ◽  
Contributing Factors ◽  
Agent Based ◽  
Emergent Behaviour

This article investigates causal relationships leading to emergence in an agent-based model of human behaviour. A new method based on nonlinear structural causality is formulated and practically demonstrated. The method is based on the concept of acausal partitionof a model variable which quantifies the contribution of various factors to its numerical value. Causal partitions make it possible to judge the relative importance of contributing factors over crucial early periods in which the emergent behaviour of a system begins to form. They can also serve as the predictors of emergence. The time-evolution of their predictive power and its distribution among their components hint at the deeper causes of emergence and the possibilities to control it.

scholarly journals Vectorization techniques for efficient agent-based model simulations of tumor growth

2015 ◽  
Author(s):  
Jan Poleszczuk ◽  
Heiko Enderling
Keyword(s):  
Tumor Growth ◽  
Growth Dynamics ◽  
Computational Time ◽  
Computational Power ◽  
Agent Based Model ◽  
Agent Based ◽  
Numerical Examples ◽  
Multi Scale ◽  
Implementation Techniques ◽  
Time Required

Multi-scale agent-based models are increasingly used to simulate tumor growth dynamics. Simulating such complex systems is often a great challenge despite large computational power of modern computers and, thus, implementation techniques are becoming as important as the models themselves. Here we show, using a simple agent-based model of tumor growth, how the computational time required for simulation can be decreased by using vectorization techniques. In numerical examples we observed up to 30-fold increases in computation performance when standard approaches were, at least in part, replaced with vectorized routines in MATLAB.

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