Fateme Pourhasanzade; Seyed Hojat Sabzpoushan; Danial Makvandi
Volume 13, Issue 2 , August 2019, , Pages 159-175
Abstract
Cancer is a leading cause of death in the world. Mathematical and computer models may help scientists to better understand it, and improve current treatments. They may also introduce new aspects of therapy. In this paper, a Cellular Automata model of tumor by emphasizing on immune system is presented. ...
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Cancer is a leading cause of death in the world. Mathematical and computer models may help scientists to better understand it, and improve current treatments. They may also introduce new aspects of therapy. In this paper, a Cellular Automata model of tumor by emphasizing on immune system is presented. Considering the spetio-temporal heterogeneity that is not considered in most mathematical models, is one of the novelity of this work. In presented model each tumor cell in a square lattice can interact with both immune and normal cells in its Moore neighborhood. The rules for updating the states of the model are stochastic. Modeling tumor cells scaping from immune system and their survivance and considering immune system recurrement into the studied tissue is another innovation of this model. The results of our simulations are presented with/without considering immune system. The growth fraction and necrotic fraction are considered as output parameters of model as well as a 2-D graphical growth presentation. Results show that considering the heterogeneity will improve the compatibility of the model with biological reality and experimental studies. It can be seen that the number of immune cells increases during the tumor growth and follows the same dynamics as tumor cells. In this paper, we have innovatively focused on the effect of model parameters on different steps of tumor growth from the cancer therapy viewpoint.
Biological Computer Modeling / Biological Computer Simulation
Seyed Hojat Sabzpoushan; Fateme Pourhasanzade
Volume 11, Issue 1 , May 2017, , Pages 1-18
Abstract
In this paper, a new method is proposed for slowing down avascular tumor growth. Our method is established on an agent based avascular tumor growth model (ABM). The model is based on biological assumptions with regard to the immune system interactions. The model parameters are fitted in compatability ...
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In this paper, a new method is proposed for slowing down avascular tumor growth. Our method is established on an agent based avascular tumor growth model (ABM). The model is based on biological assumptions with regard to the immune system interactions. The model parameters are fitted in compatability with cancer biology using in vivo expremental data. The immune cells recruitment, which usually occur after that tumor cells are identified, are also considered in ABM model. The results show that the proposed model not only is able to simulate the tumor growth graphically, but also the in vivo tumor growth quantitatively and qualitatively. Besides, the model proposes a new idea for slowing down the tumor growth considering two types of prolaiferative tumor cells, i.e. the tumor will grow slowly if the division probability of the proliferative tumor cells depends on the microenvironmental conditions. The proposed idea has been validated using an in silico simulation.
Bioelectrics
Seyed Hojat Sabzpoushan; Tina Ghodsi Asnaashari; Fateme Pourhasanzade
Volume 11, Issue 1 , May 2017, , Pages 41-49
Abstract
Cancer is one of the most important causes of mortality in human society; therefore, scientists are always looking for new ways to cope with the disease. Understanding more about the dynamics of cancerous tumors in body can help researches. Therefore, making simple models for tumor growth is important. ...
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Cancer is one of the most important causes of mortality in human society; therefore, scientists are always looking for new ways to cope with the disease. Understanding more about the dynamics of cancerous tumors in body can help researches. Therefore, making simple models for tumor growth is important. Various models have been proposed for the dynamics of cancer cell growth in the body. In some models, the interaction of different types of cells in the cancerous system is mentioned. The cells in the cancerous system include tumor, healthy, and the immune system cells. Generally, the previous models based on these three cell populations couldn’t simulate chaotic behaviors, while the biology of cancer has confirmed chaos in the system. In this paper, a model of three variables is presented and it’s shown that for some values of parameters the system can simulate chaotic behaviors. Model parameters are defined based on biological relationships, each of which plays a particular role in the dynamics of the system. To analyze the role of the parameters, a specific interval is assigned to each parameter, and by plotting the bifurcation diagram, behavioral changes of the system is observed. The results show that some of the parameters have less role in the system's behavior, and by adjusting some of them, free tumor system can be provided. Also, by setting other parameters, the system can lead to a malignant tumor. The parameters of the immune system equation have the least effect on the system’s dynamics. Regarding this finding, it can be said that applying a therapeutic approach that changes the parameters of the immune system will play a minor role in treatment. While applying therapies that change the parameters of healthy cells has the greatest effect on treatment.
Biological Computer Modeling / Biological Computer Simulation
Fateme Pourhasan Zade; Seyed Hojat Sabzpoushan; Ali Mohammad Alizade; Ebrahim Esmati
Volume 10, Issue 2 , August 2016, , Pages 99-112
Abstract
Cancer is the third leading cause of death in Iran after cardiac diseases and car accidents. Mathematical and computational models are great help to better understand cancer related phenomena. It may even improve common therapies or introduce new therapies. In this paper, a new multiscale cellular automata ...
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Cancer is the third leading cause of death in Iran after cardiac diseases and car accidents. Mathematical and computational models are great help to better understand cancer related phenomena. It may even improve common therapies or introduce new therapies. In this paper, a new multiscale cellular automata model of tumor growth based on the tumor micro-environment is introduced. Two separate square lattices are presumed for metabolic and cellular spaces. One of the following four states can be devoted to each cell in the cellular lattice: proliferating cancer, non- proliferating cancer, necrotic, and normal cells. Changing the cell's state and tumor growth is discussed in this lattice. However, production/consumption, and the diffusion of nutrients (oxygen and glucose) and also waste products including lactic acid are studied in the metabolic lattice. In this study, we determined the stochastic rules of altering the states of each cell based on the concentration rates of nutrients and lactic acid. The growth fraction and necrotic fraction were used as output parameters beside a 2-D graphical display of growth. The changes in the level of nutrients in the metabolic lattice and the effect of acidity on the growth of tumor have been reported in this paper. Our simulations faithfully reproduce the in vivo experimental observations reported for cholangiocarcinoma.
Biological Computer Modeling / Biological Computer Simulation
Seyed Hojat Sabzpoushan; Fateme Pourhasan Zadeh; Zohre Agin
Volume 7, Issue 1 , June 2013, , Pages 65-73
Abstract
A great number of people are diagnosed with a brain tumor, annually. Glioblastoma multiform (GBM) is the most common and deadliest malignant primary brain tumor. Therefore, the study of the growth of GBM is one of the issues considered by researchers. Many mathematical models to simulate the growth of ...
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A great number of people are diagnosed with a brain tumor, annually. Glioblastoma multiform (GBM) is the most common and deadliest malignant primary brain tumor. Therefore, the study of the growth of GBM is one of the issues considered by researchers. Many mathematical models to simulate the growth of GBM brain tumor have been proposed. These models help scientists to understand the process of tumor growth in order to achieve effective treatment. To simulate the tumor growth, a four dimensional (4D) model using cellular automata (CA) method is presented in this paper. A three dimensional (3D) lattice constituted by Voronoi tessellation is used. Spatial distribution of grid points in 3D has been generated by using Random Sequential Addition (RSA). In the utilized lattice, each cell is a polyhedron with various number of edges and neighboring. Delaunay triangulation is applied to find neighboring cells. Each cell in this lattice can be necrotic, non-proliferative, proliferative, non-tumorous or normal. The simulation is capable to exhibit a tumor growth of 0.1 mm to 25 mm in radius. The proposed model has been compared with experimental data in four temporal stages: spheroid, detectable lesion, diagnosis and death. Studies show that the accuracy of the presented model is generally about 85%.
Cell Biomechanics / Cell Mechanics / Mechanobiology
Seyed Hojat Sabzpoushan; Fateme Pourhasan Zadeh; Azar Badangiz
Volume 4, Issue 1 , June 2010, , Pages 45-52
Abstract
The heart tissue is an excitable media. Cellular Automata is an approach describing cardiac action potential propagation. One of the advantages of Cellular Automata approach over the differential equations based models is its high speed in large scale simulations. Prior Cellular Automata models are not ...
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The heart tissue is an excitable media. Cellular Automata is an approach describing cardiac action potential propagation. One of the advantages of Cellular Automata approach over the differential equations based models is its high speed in large scale simulations. Prior Cellular Automata models are not able to eliminate flat edges in the simulated patterns or have large neighborhoods. Moreover, they are not able to match the shape of ventricular action potential to the real ones. In this paper, we present a new model which prevents flat edges creation by using minimum number of neighbors. we also rather preserve the real shape of action potential by using linear curve fitting of a well known electrophysiological model.
