  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
https://github.com/kerepesi/MiStImm.
