Jun 17, 2016 - Alexander Mozeika and Anthony CC Coolen. Institute for ..... Biology: Deborah Dunn-Walters, Victoria Martin, Joselli Silva. O'Hare. Comp.
Statistical mechanics of lymphocyte networks modelled with slow and fast variables Alexander Mozeika and Anthony CC Coolen Institute for Mathematical and Molecular Biomedicine, King’s College London.
June 17, 2016
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Outline
Adaptive Immune System Motivation Dynamics of clonal expansion Statistical mechanics of clonal expansion Results Summary
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Adaptive Immune System
Immune system (IS) defends organism from invading pathogens such as viruses, bacterium, parasites, etc. In complicated organisms usually divided into two parts: innate and adaptive IS. Innate IS is a first line of defence but nonspecific. Adaptive IS is more specific and offers a more long-term immunity by learning and memorising a wide range of pathogens.
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Immune Response: Interaction of B cells and T cells
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Immune Response: B cells, T helper cells and Ag
Results of flow cytometry on day 7 after immunization (Baumjohann et. al. 2013 Immunity 38 596).
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Immune Response: Helper and Regulator T cells
Regulatory and helper T cells in the germinal centre response (Vanderleyden et. al. 2014 Arthritis Res. Ther. 16 471).
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Motivation One one hand, distributions of B cell and T cell clone-sizes can be obtained by modern experimental techniques such as High-throughput (Yu-Chang Wu et. al. 2010 Blood 116 1070) and Single-cell RNA sequencing (Stubbington et. al. 2016 Nature Methods 13 329). On the other hand, mathematical models of clone-size distributions mainly use stochastic processes (Desponds et. al. 2016 PNAS 113 274) and ordinary differential equations (De Boer et. al. 2001 J. Theor. Biol. 212 333), but usually do not consider interactions between B cells and T cells. In this work we first define model of interacting B clones and T clones then we use statistical mechanics to obtain distributions B-clone sizes.
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Dynamics of B-clones Dynamics: B-clones, specified by the log-concentration b = (b1 , . . . , bM ), are governed by the Langevin equation τb
dbµ dt
where hχµ (t)χν (t 0 )i =
= Fµ (σ) − ρbµ + χµ (t) 2τb δµν δ(t−t 0 ) . β˜
P µ The signal Fµ (σ) = Jµ ξ σ + θ is a function of µ i i∈∂µ i T-clones specified by the concentrations σ = (σ1 , . . . , σN ). P The interaction Jµ = M ν=1 Sµν aµ , where a = (a1 , . . . , aM ) are “epitope” concentrations of Ag/Ags. The i-th T-clone is helper (regulator) if ξiµ > 0 (ξiµ < 0). T-independent activation of B-clones is facilitated by θµ . Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
(1)
Lymphocyte Network
Interactions of helper and regulator T-clones with B-clones. Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Dynamics of T-clones The energy function H(b, σ) = − allows us to write dbµ τb dt
M X
µ=1
= −
M
1 X 2 bµ Fµ (σ) + ρ bµ 2
(2)
µ=1
∂ H(b, σ) + χµ (t). ∂bµ
(3)
We assume that the same energy function governs T-clones X ∂ dσi = Jµ ξiµ bµ − V (σ) + ηi (t), (4) τσ dt ∂σi µ∈∂i
where hηi (t)ηj (t 0 )i =
2τσ δij δ(t−t 0 ) . β
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Fast equilibration of B-clones Assume that B-clones are fast variables (τb → 0) and 1 −βH(b, ˜ σ). P(b|σ) = e Z (σ)
Furthermore,
dσi dt
=−
D
E
∂ ∂σi H(b, σ)
= −
(5)
+ ηi (t)
∂ F(σ) + ηi (t), ∂σi
(6)
where F(σ) = −β˜−1 log Z (σ), from which follows P(σ) = Alexander Mozeika and Anthony CC Coolen
1 −βF (σ ) e . Z Statistical mechanics of lymphocyte networks
(7)
Fast equilibration of B-clones Joint distribution Pβ,β˜(b, σ) F (σ ) 2 β PM − 12 ρβ˜ bµ − µ ρ M F 2 (σ ) Y e e 2ρ µ=1 µ q , (8) = β PM R ˜) Fµ2 (σ µ=1 2ρ µ=1 ˜ Dσ ˜e 2π/ρβ where Dσ = e−βV (σ ) dσ. Distribution of B clone concentrations P(c) =
Z
2 − 12 ρβ˜ log(c)− Fρ
e
Alexander Mozeika and Anthony CC Coolen
c
q 2π/ρβ˜
P(F ) dF .
Statistical mechanics of lymphocyte networks
(9)
Fast equilibration of B-clones
B clones create interactions, with strength Jijµ = helper and regulator T clones. Alexander Mozeika and Anthony CC Coolen
Jµ2 µ µ ρ ξi ξj ,
between
Statistical mechanics of lymphocyte networks
Analysis of equilibrium: Fast B-clone equilibration regime Assume that T clone: σi ∈ {−1, 1} (active regulator or helper), σi ∈ {0, 1} (active or inactive helper), etc. Then T clones are governed by the distribution PM
P(σ1 , . . . , σN ) =
e
µ=1
2 βJµ ρ
(
2 P i∈∂µ ξi σi 2
2 ( βJµ P PM µ=1 ρ e ˜ σ
)
2 P ˜i i∈∂µ ξi σ 2
)
.
(10)
Above is equivalent to ferromagnetic Ising model when σi ∈ {−1, 1} or σi ∈ {0, 1} with ξi = 1. P Average T-clone “activity”: m = N1 N i=1 hσi i gives us the fraction of helper, m+ = 1+m , and regulator, m− = 1 − m+ , 2 T clones. There are many T clone networks with finite βc (N → ∞) such that: m = 0 (m+ = 21 ) when β < βc and m 6= 0 (either m+ > 21 or m+ < 12 ) when β > βc . Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Analysis of equilibrium
Examples of lymphocyte (B-clone and T-clone ◦) network topologies leading to T clone networks (right) with finite βc .
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Analysis of equilibrium
1−m 2 , and βJ 2 of ρµ .
Fraction of regulator T-clones, m− = T-clones , m+ =
1+m 2 ,
as a function
Alexander Mozeika and Anthony CC Coolen
fraction of helper
Statistical mechanics of lymphocyte networks
Analysis of equilibrium
˜
Average B clone size, hci = e1/2ρβ heF /ρ iβ , as a function of Alexander Mozeika and Anthony CC Coolen
βJµ2 ρ .
Statistical mechanics of lymphocyte networks
Analysis of equilibrium
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
Systems on random regular graphs Assume: Each T-clone is “connected” to L B-clones and each L B clone is connected to K T-clones ( M N = K ). Assume: σi ∈ {−1, 1}, Jµ = J and θµ = 0. Recursive equation: hP PK −1 iL−1 2 1 βJ 2 PK −1 σj +σ ) +φ j=1 σj 2 ρ ( j=1 e {σj } P[σ] = P −1 iL−1(11) 2 1 βJ 2 PK −1 P hP σ ˜j +˜ σ ) +φ K ˜j j=1 σ 2 ρ ( j=1 e σ ˜ {˜ σj } 1 log(P[+1]/P[−1]) 2 Above canbe used to compute m± = φ =
m = tanh
Lφ L−1
P(F ) =
, and P
{σj } e
P
P 1 β 2 F +φ K j=1 2 ρ
1 βJ 2 PK ( j=1 ρ
2 {˜ σj } e
Alexander Mozeika and Anthony CC Coolen
σj
1 2
(1 ± m), where
δ F −J σ ˜j )2 +φ
PK
j=1 σj
PK
j=1
σ ˜j
. (12)
Statistical mechanics of lymphocyte networks
Phase diagram
Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks
B-clone size distribution for L = K = 4 Statistical mechanics of lymphocyte networks modelled with slow and fast variables 23
hci
P (c)
m
c
P (c)
P (c)
c
c
Figure 9. Behaviour of B clones in the immune system with fast B-clone equilibration. The system, defined on a random regular factor-graph with
The distribution ofconnectivity B clone sizes studied three L = K = 4, was studied for for the B-clone noisedifferent parameters ˜ 2B-clone {0.5, 1.0, 2.0}, represented by the dotted, dashed and solid lines respectively, in the high < and low > right) ( ⇡ 0.0929) noise regimes. Top: noise parameters in the “low” (top andT-clone “high” (bottom left Left: The average B-clone size, hci, as a function of the fraction of T regulator cells , m . Right: The distribution P (c) of the B-clone size c for = 0.0639 m− = 0.1 and bottom right m = 0.9) “amount” of Ag regimes. − size distributions for = 0.1219 with m = 0.1 (m = ). Bottom: B-clone c
1 2
(left) and m
= 0.9 (right).
Alexander Mozeika and Anthony CC Coolen
c
c
Statistical mechanics of lymphocyte networks
Summary We used statistical mechanics to study dynamics of B clones and T clones interacting on networks. We considered a simple scenario when T clones are modelled by binary variables. Many results of our analysis are independent of the network topologies and qualitatively consistent with experimental observations. Assumption of random network topology allows us to compute distributions of B-clone sizes. Preprint is available at arXiv:1603.01328 Acknowledgements Biology: Deborah Dunn-Walters, Victoria Martin, Joselli Silva O’Hare. Comp. Biology and Physics: Franca Fraternali, Alessia Annibale, Adriano Barra, Elena Agliari and Silvia Bartolucci. Alexander Mozeika and Anthony CC Coolen
Statistical mechanics of lymphocyte networks