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.
Cell Biomechanics / Cell Mechanics / Mechanobiology
Siavash Mazdeyasna; Amir Homayoun Jafari
Volume 5, Issue 3 , June 2011, , Pages 181-192
Abstract
In this paper, two models are introduced based on cellular automata and the game theory to study behavior, growth, development and morphology of cancerous cells by assuming nutrition supplies, extracellular matrix, and immune cells. A two-dimensional cellular automaton combine with game theory is considered ...
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In this paper, two models are introduced based on cellular automata and the game theory to study behavior, growth, development and morphology of cancerous cells by assuming nutrition supplies, extracellular matrix, and immune cells. A two-dimensional cellular automaton combine with game theory is considered as the structure of model. The cellular automata modeling framework can be an efficient approach to a number of biological problems; and game theory aims to help us to understand situations in which decision-makers interact such as competitive activity. In the first model, we consider different oxygen supplies to study the growth and invasion of cancerous cell. The results of our simulation are validated by the results of other articles. The results show that the number of cancerous cells is easily changed by changing amount of oxygen supplies, but invasive distance of tumor cells is not easily affected by this factor. Furthermore the results of this model are not linear, that could show the improvement of the model. In addition, this model has the ability of producing metastasis, as it is shown. In the second model, the interaction between immune cells and cancerous cells are considered. Two-dimensional cellular automata and game theory are used for this purpose. In this model the behavior of cellular automata is determined by the game theory. The rules of cellular automata are determined by game theory table, so each element of the system could make a decision separately.
Neuro-Muscular Engineering
Amir Homayoun Jafari; Seyed Mohammad Reza Hashemi Golpayegani; Farzad Towhidkhah; Ali Fallah
Volume -2, Issue 1 , July 2005, , Pages 57-70
Abstract
A hierarchical structure model with three levels is presented for modeling motor control in skill movements. At each level, based on accuracy and quality of control, a specific controller is activated. At first level, control concepts are qualitative. The duty of the first level is to provide stability ...
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A hierarchical structure model with three levels is presented for modeling motor control in skill movements. At each level, based on accuracy and quality of control, a specific controller is activated. At first level, control concepts are qualitative. The duty of the first level is to provide stability of system, based on the received qualitative information from second level such as the decrement or increment of error. A self-organized controller at first level is used to generate qualitative control commands, and it plays an encouragement-punishment role to keep the stability of system by sending discrete commands to the second level. This controller only contributes at control action when the controller of second level can not preserve stability individually. At second level, control concepts are quantitative. The duty of the second level is adaptation and control of system accurately. The received information at this level generally comes from sensory and visual feedbacks, and it includes more accurate concepts of control action - like the amount of movement error. A model based on the predictive controller at second level generates quantitative control commands and indeed, determines trajectory of movement accurately. A fuzzy switch combines the control commands of first and second levels, based on the sliding mode strategy, to provide a robust control. At third level, this command is interpreted and then is applied to the involved muscles in movement. The received information at this level is generally the contribution of muscles in performing movement and the effects of environment on the movement, which comes from sensory feedbacks. The presented model with this hierarchical structure has a proper ability to control and keep the stability of system. The simulation results confirm this subject.
