Statistical mechanics of clonal expansion in lymphocyte networks modelled with slow and fast variables

TY - JOUR

T1 - Statistical mechanics of clonal expansion in lymphocyte networks modelled with slow and fast variables

AU - Mozeika, Alexander

AU - Coolen, Anthony C C

PY - 2016/12/21

Y1 - 2016/12/21

N2 - We use statistical mechanical techniques to model the adaptive immune system, represented by lymphocyte networks in which B cells interact with T cells and antigen. We assume that B- and T-clones evolve in different thermal noise environments and on different timescales, and derive stationary distributions and study expansion of B clones for the case where these timescales are adiabatically separated. We compute characteristics of B-clone sizes, such as average concentrations, in parameter regimes where T-clone sizes are modelled as binary variables. This analysis is independent of the network topology, and its results are qualitatively consistent with experimental observations. To obtain the full distributions of B-clone sizes we assume further that the network topologies are random and locally equivalent to trees. This allows us to compete these distributions via the Bethe-Peierls approach. As an example we calculate B-clone distributions for immune models defined on random regular networks.

AB - We use statistical mechanical techniques to model the adaptive immune system, represented by lymphocyte networks in which B cells interact with T cells and antigen. We assume that B- and T-clones evolve in different thermal noise environments and on different timescales, and derive stationary distributions and study expansion of B clones for the case where these timescales are adiabatically separated. We compute characteristics of B-clone sizes, such as average concentrations, in parameter regimes where T-clone sizes are modelled as binary variables. This analysis is independent of the network topology, and its results are qualitatively consistent with experimental observations. To obtain the full distributions of B-clone sizes we assume further that the network topologies are random and locally equivalent to trees. This allows us to compete these distributions via the Bethe-Peierls approach. As an example we calculate B-clone distributions for immune models defined on random regular networks.

KW - adaptive immune system

KW - lymphocyte network

KW - statistical physics

UR - http://www.scopus.com/inward/record.url?scp=85008222789&partnerID=8YFLogxK

U2 - 10.1088/1751-8121/50/3/035602

DO - 10.1088/1751-8121/50/3/035602

M3 - Article

AN - SCOPUS:85008222789

SN - 1751-8113

VL - 50

JO - Journal Of Physics A-Mathematical And Theoretical

JF - Journal Of Physics A-Mathematical And Theoretical

IS - 3

M1 - 035602

ER -