Our in silico model was built to investigate the development process of the adaptive immune system. For simplicity, we concentrated on humoral immunity and its major components: T cells, B cells, antibodies, interleukins, non-immune self cells, and foreign antigens. Our model is a microscopic one, similar to the interacting particle models of statistical physics . Events are considered random and modelled by a continuous time, finite state Markov process, that is, they are controlled by  independent exponential clocks. Our main purpose was to compare different theoretical models of the adaptive immune system and self--nonself discrimination: the ones that are described by well-known textbooks, and a novel one developed by our research group. Our theoretical model emphasizes the hypothesis that the immune system of a fetus can primarily learn what self is but unable to prepare itself for the huge, unknown variety of nonself. The simulation begins after conception, by developing the immune system from scratch and learning the set of self antigens. The simulation ends several months after births when a more-or-less stationary state of the immune system has been established. We investigate how the immune system can recognize and fight against a primary infection. We also investigate that under what conditions can an immune memory be created that results in a more effective immune response to a repeated infection. The MiStImm simulation software package and the simulation results are available at the address URL
Our main purpose is to compare classical nonself-centered, two-signal theoretical models of the adaptive immune system with a novel, self-centered, one-signal model developed by our research group. Our model hypothesizes that the immune system of a fetus is capable learning the limited set of self antigens but unable to prepare itself for the unlimited variety of nonself antigens. We have built a computational model that simulates the development of the adaptive immune system. For simplicity, we concentrated on humoral immunity and its major components: T cells, B cells, antibodies, interleukins, non-immune self cells, and foreign antigens. Our model is a microscopic one, similar to the interacting particle models of statistical physics and agent-based models in immunology. Furthermore, our model is stochastic: events are considered random and modeled by a continuous time, finite state Markov process, that is, they are controlled by finitely many independent exponential clocks. We investigate under what conditions can an immune memory be created that results in a more effective immune response to a repeated infection. The simulations show that our self-centered model is realistic. Moreover, in case of a primary adaptive immune reaction, it can destroy infections more efficiently than a classical nonself-centered model. Predictions of our theoretical model were clinically supported by autoimmune-related adverse events in high-dose immune checkpoint inhibitor immunotherapy trials and also by safe and successful low-dose immune checkpoint inhibitor combination treatment of heavily pretreated stage IV cancer patients who had exhausted all conventional treatments. The MiStImm simulation tool and source codes are available at the address URL
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in silico model was built to investigate the development process
main purpose is to compare classical nonself-centered, two-signal theoretical models of the adaptive immune system with a novel, self-centered, one-signal model developed by our research group. Our model hypothesizes that the immune system of a fetus is capable learning the limited set of self antigens but unable to prepare itself for the unlimited variety of nonself antigens. We have built a computational model that simulates the development
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. Events
and agent-based models in immunology. Furthermore, our model is stochastic: events
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modelled
modeled
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finitely many
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Our main purpose was to compare different theoretical models of the adaptive immune system and self--nonself discrimination: the ones that are described by well-known textbooks, and a novel one developed by our research group. Our theoretical model emphasizes the hypothesis that the immune system of a fetus can primarily learn what self is but unable to prepare itself for the huge, unknown variety of nonself. The simulation begins after conception, by developing the immune system from scratch and learning the set of self antigens. The simulation ends several months after births when a more-or-less stationary state of the immune system has been established. We investigate how the immune system can recognize and fight against a primary infection. We also investigate that
We investigate
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MiStImm simulation software package and the simulation results
simulations show that our self-centered model is realistic. Moreover, in case of a primary adaptive immune reaction, it can destroy infections more efficiently than a classical nonself-centered model. Predictions of our theoretical model were clinically supported by autoimmune-related adverse events in high-dose immune checkpoint inhibitor immunotherapy trials and also by safe and successful low-dose immune checkpoint inhibitor combination treatment of heavily pretreated stage IV cancer patients who had exhausted all conventional treatments. The MiStImm simulation tool and source codes