A multiagent system to model an human humoral response

The immune system mechanisms are very complex and the number of parameters is extremely important. Moreover, the interactions between the different cells during an immune response induce chaos and nonlinear phenomena. Our approach consists in using the cooperative models, established by the immunologists, to build a multi-agent model. We avoid the problem of nondeterminism by only encoding the basic behaviors of the agents, and the global chaotic phenomena are induced by the interactions between the agents. The advantages are that agents can be viewed, modified, removed from the model or added to the model very easily. That is not the case in mathematics, where a modification of an assumption generally involves the rewriting of the model. This approach is possible because the behaviors of several immune system cells are known in their principal lines and some qualitative models of immune cell cooperations have been developed by immunologists. These models have already demonstrated that they are valid locally in time and in space, i.e. they do not have a global approach. With the multi-agent system, we can, thanks to the simulation, analyse the global consequences from the local behaviors and observe a qualitative striking resemblance to statistical results coming from a real experimentation. We have chosen to simulate a human secondary humoral response with a multi-agent system to study the kinetic of the antibody proliferation with several type of antigenic substances