Biological Computer Modeling / Biological Computer Simulation
Sajad Shafiekhani; Amin Mashayekhi Shams; Seyed Yashar Banihashem; Nematollah Gheibi; Amir Homayoun Jafari
Volume 14, Issue 1 , May 2020, , Pages 55-67
Abstract
According to cancer’s global statistics, there will be 27.5 million new cases of cancer each year by 2040, therefore, it is crucial to achieve a deeper understanding of the cancer progression mechanisems and immune system functions in response to it. Nowadays, computational models are widely used ...
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According to cancer’s global statistics, there will be 27.5 million new cases of cancer each year by 2040, therefore, it is crucial to achieve a deeper understanding of the cancer progression mechanisems and immune system functions in response to it. Nowadays, computational models are widely used to capture dynamics of the tumor- immune system (TIS). The proposed model on this manuscript is on the basis of the ordinary differential equations which mechanistically models the interactions of tumor cells, CTLs, NKs and MDSCs. CTLs and NK cells are the most important cells of adaptive and innate immune system, respectively that encounter with tumor cells, while MDSCs as immature immune cells suppress the immune responses in the inflammatory environments. Due to the error of the in-vivo/in-vitro experiments, vagueness, imprecise information, incomplete data and natural variability of the tumor-immune system emerges between different individuals, the kinetic parameters of computational models are uncertain that this uncertainty can be captured by fuzzy sets. Hence, we assign fuzzy numbers with triangular membership functions instead of crisp numbers to some kinetic parameters of the tumor–immune system model. In fact, the uncertainty in the kinetic parameters of the ordinary differential equations affects the dynamic of the system species. In this essay, for the first time, a fuzzy number has been used to model the uncertainty of the parameters of the ODE model. Our data reveals that increasing/decreasing the uncertainty region of the model's fuzzy parameters increases/decreases the uncertainty region of dynamics of species. Furtheremore, the simulations of the model in the crisp setting of parameters show that the repition of 5-FU treatment for inhibition of MDSCs dramatically inhibits tumor cells and eradicate tumor.
Biological Computer Modeling / Biological Computer Simulation
Reza Vosoughi; Armin Allahverdy; Sajjad Shafiekhani; Amir Homayoun Jafari
Volume 11, Issue 4 , February 2018, , Pages 291-301
Abstract
In recent decades, due to the increased prevalence of diabetes and its chronic complications, glucose measurement, modeling of glucose-insulin system and glucose control have been especially important. Since the type I diabetes does not secrete insulin, cells do not absorb glucose, and thus the blood ...
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In recent decades, due to the increased prevalence of diabetes and its chronic complications, glucose measurement, modeling of glucose-insulin system and glucose control have been especially important. Since the type I diabetes does not secrete insulin, cells do not absorb glucose, and thus the blood glucose level increase. In order to control your blood sugar, insulin should besubcutaneously injected into the body under complex, controlled conditions. If the level of insulin increases beyond the natural physiological range, there is a risk of death. There are various treatments for diabetes, the main treatment of which is insulin therapy. Monitoring the patient's blood sugar level continuously during the day and night is a very good treatment strategy, since it controls the patient's blood sugar level in a safe area with the lowest amount of insulin injected at the required times. This mechanism avoid the hyperglycemia (blood glucose levels greater than 120 mg/dl) and hypoglycemia (blood sugar less than 65 mg / dl). To achieve this goal, a two delay model has been developed to model blood glucose levels continuously during time. Some of the parameters of this model are estimated using the genetic algorithm to achieve the best fitness between the dynamics of the model with the experimental data obtained in this study. As a result, the developed model of this study can dynamically obtain blood glucose continuously during time, consequently it can predicts the insulin dynamics required to be injected into the patient to control the amount of blood glucose in the normal range. Therefore this controlling system is capable of preventing hypoglycemia and hyperglycemia.
