  The immune response is a dynamic process by which the body determines whether
an antigen is self or nonself. The state of this dynamic process is defined by
the relative balance and population of inflammatory and regulatory actors which
comprise this decision making process. The goal of immunotherapy as applied to,
e.g. Rheumatoid Arthritis (RA), then, is to bias the immune state in favor of
the regulatory actors - thereby shutting down autoimmune pathways in the
response. While there are several known approaches to immunotherapy, the
effectiveness of the therapy will depend on how this intervention alters the
evolution of this state. Unfortunately, this process is determined not only by
the dynamics of the process, but the state of the system at the time of
intervention - a state which is difficult if not impossible to determine prior
to application of the therapy. To identify such states we consider a mouse
model of RA (Collagen-Induced Arthritis (CIA)) immunotherapy; collect high
dimensional data on T cell markers and populations of mice after treatment with
a recently developed immunotherapy for CIA; and use feature selection
algorithms in order to select a lower dimensional subset of this data which can
be used to predict both the full set of T cell markers and populations, along
with the efficacy of immunotherapy treatment.
