Mathematical Approaches to Immunology
Elimination of danger (e.g. pathogens) is an important
activity for organisms. From this viewpoint, it is clear that the
optimization analysis is a valid method to study immune system.
There are different patterns of defense observed in immune
system. Higher vertebrates have both innate immunity and acquired
immunity, whilst lower animals don't. Some proteins for defense are
stored beforehand, whilst others are produced after infection. It is
likely that some advantages lay under the realized patterns of
defense. The organisms should prevent pathogen growth, with saving
defense cost (e.g. tissue injury). I present mathematical models to
clarify the optimal defense pattern using the above criterion. There
are 3 parts in my talk. First, I present models focusing on
difference between two defense options, such as delay, cost,
effectiveness, and uncertainty of information available. If defense
proteins are produced via gene expression after infection, there is a
serious risk where pathogen abundance increases quickly until the
level of defense protein becomes enough. In contrast, if defense
proteins are produced and stored before infection, there is no
disadvantage mentioned above. However, storing holds the other
disadvantage where
stored proteins can be wasteful, because the information available
before infection is limited. Taking the above into consideration, I
discuss the optimal defense strategy. Second, I present more
detailed models clarifying dynamic defense strategy. I discuss the
pattern of acquired immune response. Immune system realizes
"bang-bang control", to produce effector and memory cells
economically. Lastly, I present the model, which we have just
started, on JAK-STAT pathway. The last study is based on Yamada et
al (2003).