  We consider the mutual interactions, via cytokine exchanges, among helper
lymphocytes, B lymphocytes and killer lymphocytes, and we model them as a
unique system by means of a tripartite network. Each part includes all the
different clones of the same lymphatic subpopulation, whose couplings to the
others are either excitatory or inhibitory (mirroring elicitation and
suppression by cytokine). First of all, we show that this system can be mapped
into an associative neural network, where helper cells directly interact with
each other and are able to secrete cytokines according to "strategies" learnt
by the system and profitable to cope with possible antigenic stimulation; the
ability of such a retrieval corresponds to a healthy reaction of the immune
system. We then investigate the possible conditions for the failure of a
correct retrieval and distinguish between the following outcomes: massive
lymphocyte expansion/suppression (e.g. lymphoproliferative syndromes),
subpopulation unbalance (e.g. HIV, EBV infections) and ageing (thought of as
noise growth); the correlation of such states to auto-immune diseases is also
highlighted. Lastly, we discuss how self-regulatory effects within each
effector branch (i.e. B and killer lymphocytes) can be modeled in terms of a
stochastic process, ultimately providing a consistent bridge between the
tripartite-network approach introduced here and the immune networks developed
in the last decades.
